Sourcing Toolkit

Profitable deal flow,built as a system.

A fund wins by carrying companies from the first signal they exist through to a profitable exit, faster and with sharper judgement than the next fund. How much ground a team can cover was capped by the team’s work rate. Software now builds, iterates and optimises far quicker than any person can.

This page proposes a product roadmap: a data-driven deal flow pipeline to empower investment teams to go first, further, and faster.

Why this compounds

Every signal captured once. Each screen, score, call and outcome lands on one record. The pipeline has a memory, so the team learns from every pass as much as from every deal, and the ranking gets sharper the longer it runs.

Reach founders first. Public administrative data leads the paid databases by months, sometimes years. An earlier look means a better entry price, a less crowded round, and a relationship that starts before the banker calls.

A proprietary dataset no one can buy. As the team acts, its market understanding and its reasons for passing become structured data. That compounds into the fund’s own judgement, encoded and reusable, and it is an asset no subscription replicates.

Hours back to the human side. Screening, modelling and pack assembly run overnight. The recovered hours go to the founders, the network and the judgement, which is where venture returns are actually made.

The pipeline, end to end

The map below is interactive. Open it and click any stage, any component or any item on the human rail to read what it does, what it gives back to the team, and whether it is built and running today, scoped, or still on the roadmap.

The deal flow pipeline, in full

What stays human

Conviction in a founder
No score settles whether this is the person to back. That is a judgement made in a room.
The relationships that earn the look
Getting into a round early is a function of who trusts you, built over years.
The introductions that change a company
Connecting a founder to the one operator who has solved their exact problem is network work.
The call in the IC
The tooling supplies the evidence and the argument. A person still has to take the position.

Everything below raises how much ground a small team can cover. None of it replaces this, and the hours it returns go straight back into it.

Source agnostic intake

Own standing scouts, PitchBook export, Harmonic AI list, A partner’s spreadsheet. Anything that names a company can enter the funnel and be scored on the same terms.

Stage 01, Source and qualify

Define the investable universe, then find the companies inside it before anyone else is looking.

What it gives back: The weekly screen runs overnight, so the team opens a ranked dozen instead of working through four thousand records.

Sourcing Radar. Built, running.
Five standing scouts across research grants, federal awards, company registers, equity filings and open code. Surfaces named, contactable companies from the earliest public evidence they exist. Every new dataset becomes another scout.
Encoded thesis filter. Built, running.
The investable universe as a config file: keyword sets, classifications and six weighted factors. Swap in a fund’s own thesis definitions and the filtering sharpens at no extra running cost. Every score traces to a stated rule, so it can be argued with.
Investability Desk. Built, running.
First-pass diligence on the best of them: route to a buyer, economics against the incumbent today, what holds the moat, whether it could return a fund. Ends on a verdict and the opening question, and it will say avoid.
Deal flow CRM. Roadmap.
One record for every company and founder seen: both scores, engagement status, who holds the relationship. Turns a stream of weekly shortlists into something that compounds.

Stage 02, Diligence and execute

Convert a shortlist into signed terms on quantitative evidence, at a pace a small team can hold.

What it gives back: Assembling the diligence pack stops being the job, so the time goes into the calls and the questions a pack cannot answer.

Structured diligence playbook. Scoped.
The same question set every time, tied to the fund’s thesis, returning a score per dimension alongside the narrative. Two companies become comparable.
Founder call capture. Scoped.
Calls recorded and parsed against the playbook, so answers land on the dimension they settle and the gaps are visible before the next call is booked. The investor listens rather than types.
Returns and scenario model. Scoped.
Monte Carlo over the variables that actually move the outcome: entry price, dilution, exit multiple, timing. Returns a distribution of fund-level outcomes, with the probability attached to each.
Live market maps. Scoped.
Niche maps and competitor sets kept current by the same scouts, so the market view taken into IC is this week’s.
Execution tracking. Roadmap.
Term sheets, conditions and close steps written back to the record, so the pipeline shows where every live deal actually is.

Stage 03, Scale and exit

Make portfolio companies commercially investable to the next buyer, and get there sooner.

What it gives back: The repeatable parts of portfolio support ship as a package, so partner time goes to the founder instead of into rebuilding the same playbook company by company.

Ecosystem Map. Built, running.
A searchable map of climate domain and function specialists, so a founder reaches the person who has solved their specific problem. The map finds the name. A person still makes the introduction.
Commercial Engine. Built, running.
A redeployable commercial layer for a portfolio company: positioning, segmentation, outbound and a measurement loop that tunes itself on results.
Operating pack. Built at Reef.
Hiring frameworks, a data-driven recruitment pipeline and an outsourced development network, ready to drop into a company that needs to ship or staff faster.
Portfolio and exit tracking. Roadmap.
Company financials, valuation marks and fund-level reporting on the same record the deal was sourced into, from first signal to exit.

The record

One record every stage writes to. Roadmap. Scores from sourcing, findings from diligence, performance after close, and who owns each relationship. The record ranks what to look at next, so the pipeline has a memory and the ranking gets better the longer it runs. I built the equivalent at Reef and know what it has to hold.

Built, not proposed

Four components of it already run

The roadmap above is thirteen components. Four of them are built, and all four can be inspected rather than taken on trust. Two run on this page, one has its own page, and one was built and proven inside an operating company. That is what makes the rest of the pipeline a plan with a demonstrated build rate behind it.

Sourcing Radar

Built, running
Running on this page

Five standing scouts hold research grants, federal awards, company registers, equity filings and open code every week. The twelve companies below came out of a live run, each with the record it was found in attached.

Read the run →

Investability Desk

Built, running
Running on this page

First-pass diligence on a shortlist from any source, returning a verdict, the bands behind it, and the question to open the conversation with. It is just as willing to say avoid.

Read the verdicts →

Ecosystem Map

Built, running
A separate page of this site

A searchable map of climate domain and function specialists, so a portfolio founder reaches the person who has already solved their specific problem.

Open the Ecosystem Map →

Commercial Engine

Built, running
Built at Reef, running locally today

A redeployable commercial layer for a portfolio company: positioning, segmentation, outbound and a measurement loop that tunes itself on results. At Reef it ran outbound at an A$410 customer acquisition cost and roughly 8.5x return on ad spend inside thirty days, and carried the company from zero to sold in twelve months. It is not published here, and it is ready to point at a portfolio company.

The first tool

Sourcing Radar

The radar holds five standing scouts every week across research grants, federal awards, company registers, equity filings and open code, and surfaces named, contactable companies inside three climate and industrial theses from the earliest public evidence that they exist and are building.

It is source agnostic. A Pitchbook export, a Harmonic AI list or a spreadsheet off a partner’s desk enters the same funnel and is scored on the same terms, so a fund’s existing subscriptions become inputs to it.

Five standing scouts, and room for many more

Each scout is one job, pointed at one dataset, on its own cadence. Set the brief once and they run without being asked, surfacing only what changed. Every new dataset becomes another scout, widening the net without rebuilding anything.

weekly32 held

Spin-out sweep

UK academic spin-outs declared against UKRI research grants, across the three theses. Catches a company at the point its founding research becomes commercial.

weekly61 held

US non-dilutive sweep

US small companies awarded NSF SBIR/STTR grants. Non-dilutive federal money usually lands before a priced round and always before the press.

weekly595 held

New incorporation watch

UK companies incorporated in the last year under thesis-relevant SIC codes. The earliest formal signal a company exists at all.

weekly20 held

Equity round watch

Statutory share-allotment filings at Companies House, which record a UK equity round within a month of it closing. Around two thirds of UK rounds are never announced, so for most companies this is the only evidence a raise happened.

weekly19 held

Builder signal

Young GitHub organisations publishing in thesis territory, filtered to exclude universities, national labs and the open source arms of incumbents.

Next scouts in the roadmap

Patent filings. An IP filing typically precedes a launch by around eighteen months, the earliest hard signal available.

EU research consortia. CORDIS covers Horizon-funded projects and their commercial participants, with rich descriptions. Opens continental Europe.

Private placement filings. US notices filed within fifteen days of a raise, months ahead of any announcement.

Founder crossover. Cross-references named researchers against company officers to catch academics who have quietly incorporated.

Each one is a few hours of work against a documented interface. The architecture was built for this: a scout drops into the same schema and flows through the same scoring, so widening coverage does not mean rebuilding anything.

Describe the brief once. The scouts run on their cadence and tell you when something is worth your attention.

The findings

Twelve companies, grouped by thesis and ranked within it. Every synopsis, date and name comes from the linked record. Nothing here is illustrative.

A note on what this list is. The radar has no knowledge of funding rounds. It ranks on evidence of activity, so a company surfaces because it won a grant, incorporated, or started building, whatever its funding status. Some of these have raised already. The claim is that each was found from its own public trace rather than from an announcement, which is what makes the method repeatable and the coverage systematic.

Electrification / Compute

AEPNUS TECHNOLOGY INC

80.2/100
NSF SBIR/STTR awardsUS

Enable domestic manufacturing of critical minerals and industrial chemicals through circular electrochemical processing. This project advances a scalable electrolyzer system that converts sodium sulfate by-products - an increasingly abundant byproduct of battery manufacturing, mining, textiles, and pulp and paper - into valuable sulfuric acid and sodium hydroxide for reuse on-site. By transforming by-products into essential reagents, the technology reduces disposal burdens, lowers operating costs, and strengthens supply chain resilience for U.S. […]

Why now. Awarded an NSF SBIR Phase II grant in August 2026 (about $1,240,154) - non-dilutive federal money, typically ahead of a priced round.

Thesis fit. matches 2 electrification keywords: energy storage, electrification.

Named on the public record. Bilen Akuzum.

Funding. outside the UK register, so equity filings are not visible to this source

not mentioned in the current funding-news feeds
Score breakdown
thesis fit1 x 0.25matches 2 electrification keywords: energy storage, electrification
funding signal0.5 x 0.2outside the UK register, so equity filings are not visible to this source
evidence depth0.75 x 0.15public record carries description, company_url, named people
recency1 x 0.12signal is 2 months old
signal type0.7 x 0.2grant signal
geography1 x 0.08based in OAKLAND, CA, inside the fund's scope

HEAT2POWER INC.

80.2/100
NSF SBIR/STTR awardsUS

Unlike conventional dynamic heat engines, which include moving parts and heat exchangers, thermophotovoltaic cells convert high-temperature thermal energy directly into electricity using a solid-state device, offering rapid load-response, greater modularity, and less down time. Recent innovations in thermophotovoltaic design enable these cells to generate up to 500 times more electricity per unit area compared to rooftop solar, unlocking cost-effective use of high-quality semiconductor materials and supporting distributed energy production in industrial settings. By integrating thermophotovoltaics with modular thermal batteries, facilities can power operations with low-cost energy and support the electric grid with flexible storage solutions, a critical capability as variable sources and loads increasingly enter the energy landscape. […]

Why now. Awarded an NSF SBIR Phase I grant in June 2026 (about $305,000) - non-dilutive federal money, typically ahead of a priced round.

Thesis fit. matches 2 electrification keywords: distributed energy, energy storage.

Named on the public record. Sean McSherry.

Funding. outside the UK register, so equity filings are not visible to this source

not mentioned in the current funding-news feeds
Score breakdown
thesis fit1 x 0.25matches 2 electrification keywords: distributed energy, energy storage
funding signal0.5 x 0.2outside the UK register, so equity filings are not visible to this source
evidence depth0.75 x 0.15public record carries description, company_url, named people
recency1 x 0.12signal is 4 months old
signal type0.7 x 0.2grant signal
geography1 x 0.08based in ANN ARBOR, MI, inside the fund's scope

BERRYCELLS LTD

79.5/100
UKRI Gateway to ResearchGB

BerryCell Ltd is a UK company founded by Professor Marina Freitag to commercialise research outcomes from EPSRC-funded programmes at Newcastle University, translating fundamental advances in ambient photovoltaics, photocapacitors, and coordination chemistry into market-ready products and services for the Internet of Things (IoT) and smart building sectors. The company's core technology platform integrates hybrid solar cells and photocapacitors that harvest energy from indoor and ambient light sources, enabling battery-free, maintenance-free operation of wireless sensors, edge AI devices, and distributed IoT networks. These devices are based on non-toxic, printable materials including copper coordination complexes and bio-derived components such as mushroom-derived chitosan membranes offering a sustainable alternative to conventional silicon photovoltaics and disposable batteries. […]

Why now. Declared as a spin-out against UKRI grant EP/V035819/1 at Newcastle University; company formed January 2026.

Thesis fit. matches 1 electrification keyword: energy storage.

Named on the public record. Marina Freitag.

Funding. no equity raised yet, and at 9 months old it is early enough that this reads as opportunity

not mentioned in the current funding-news feeds
Score breakdown
thesis fit0.5 x 0.25matches 1 electrification keyword: energy storage
funding signal0.6 x 0.2no equity raised yet, and at 9 months old it is early enough that this reads as opportunity
evidence depth1 x 0.15public record carries description, company_url, also_evidence_urls, named people
recency1 x 0.12signal is 9 months old
signal type1 x 0.2spinout signal corroborated by 2 separate records
geography1 x 0.08based in the UK, inside the fund's scope

illumion

76.2/100
UKRI Gateway to ResearchGB

illumion operates an energy storage management platform assessing battery performance.

Why now. Declared as a spin-out against UKRI grant EP/R00661X/1 at University Of Manchester; company formed January 2022.

Thesis fit. matches 1 electrification keyword: energy storage.

Named on the public record. Andrew Cooper, Christian Nielsen, Clare Grey, Edmund Harold Linfield.

Funding. filed a share allotment about 3 months ago with the irregular capital increase typical of priced investment

not mentioned in the current funding-news feeds
Score breakdown
thesis fit0.5 x 0.25matches 1 electrification keyword: energy storage
funding signal1 x 0.2filed a share allotment about 3 months ago with the irregular capital increase typical of priced investment
evidence depth1 x 0.15public record carries description, company_url, also_evidence_urls, named people
recency0.062 x 0.12signal is 57 months old
signal type1 x 0.2spinout signal corroborated by 4 separate records
geography1 x 0.08based in the UK, inside the fund's scope

Decoding Nature

MORF-BIO LTD

85.2/100
UKRI Gateway to ResearchGB

MORF-Bio Ltd is a spin out founded by Prof Gavin Thomas (CSO), Dr Joyce Bennett (CEO) and Dr Vicki Springthorpe (CTO), who were the PI, Project Manager and bioinformatician on our BBSRC funded IB Catalyst DETOX project. We secured a small additional grant from BBSRC to develop this further and went through the Innovate iCURE program and span out in October 2025 after raising seed funding led by the Deeptech Seed Fund. We now employ two additional software developers (2 York graduates in computer science) and will launch our first products in summer 2026. […]

Why now. Declared as a spin-out against UKRI grant BB/N01040X/1 at University Of York; company formed January 2025.

Thesis fit. matches 1 decoding_nature keyword: microbiome.

Named on the public record. Gavin Thomas.

Funding. filed a share allotment about 3 months ago with the irregular capital increase typical of priced investment

not mentioned in the current funding-news feeds
Score breakdown
thesis fit0.5 x 0.25matches 1 decoding_nature keyword: microbiome
funding signal1 x 0.2filed a share allotment about 3 months ago with the irregular capital increase typical of priced investment
evidence depth1 x 0.15public record carries description, company_url, also_evidence_urls, named people
recency0.812 x 0.12signal is 21 months old
signal type1 x 0.2spinout signal corroborated by 2 separate records
geography1 x 0.08based in the UK, inside the fund's scope

LIVING SENSORS INC

76.5/100
NSF SBIR/STTR awardsUS

Improve the ability to detect and manage contamination from per- and polyfluoroalkyl substances (PFAS), a class of persistent industrial chemicals often called “forever chemicals.” PFAS contamination has affected millions of acres of agricultural land in the United States, creating risks for food systems, ecosystems, and rural economies. Current monitoring approaches rely on laboratory testing of soil samples, which is costly and sparse, leaving large areas untested. This project will develop a plant-based environmental sensing system that enables large-scale screening of soils for PFAS contamination. […]

Why now. Awarded an NSF SBIR Phase I grant in June 2026 (about $305,000) - non-dilutive federal money, typically ahead of a priced round.

Thesis fit. matches 2 decoding_nature keywords: synthetic biology, protein engineering.

Named on the public record. Michael Prigge.

Funding. outside the UK register, so equity filings are not visible to this source

not mentioned in the current funding-news feeds
Score breakdown
thesis fit1 x 0.25matches 2 decoding_nature keywords: synthetic biology, protein engineering
funding signal0.5 x 0.2outside the UK register, so equity filings are not visible to this source
evidence depth0.5 x 0.15public record carries description, named people
recency1 x 0.12signal is 4 months old
signal type0.7 x 0.2grant signal
geography1 x 0.08based in RIVERSIDE, CA, inside the fund's scope

VITIDORE, INC.

75.8/100
NSF SBIR/STTR awardsUS

In mitigating the earth system’s temperature rise. By removing the barriers farmers face to planting permanent cover crops and generating income from the resulting soil carbon gains, this project would make our food supply chain more resilient. Today farmers leave 95% of ~400M US cropland acres bare outside of the cash crop season because the short-term costs, work and risk of annual cover crops outweigh the benefits. […]

Why now. Awarded an NSF SBIR Phase II grant in July 2025 (about $1,249,631) - non-dilutive federal money, typically ahead of a priced round.

Thesis fit. matches 2 decoding_nature keywords: soil health, soil carbon.

Named on the public record. Jeff Thiel.

Funding. outside the UK register, so equity filings are not visible to this source

not mentioned in the current funding-news feeds
Score breakdown
thesis fit1 x 0.25matches 2 decoding_nature keywords: soil health, soil carbon
funding signal0.5 x 0.2outside the UK register, so equity filings are not visible to this source
evidence depth0.5 x 0.15public record carries description, named people
recency0.938 x 0.12signal is 15 months old
signal type0.7 x 0.2grant signal
geography1 x 0.08based in SPOKANE, WA, inside the fund's scope

GitLife Biotech

75.5/100
UKRI Gateway to ResearchGB

GitLife Biotech develops DNA-based biosecurity tools and a version control system for synthetic biology using DNA barcoding.

Why now. Declared as a spin-out against UKRI grant EP/N031962/1 at Newcastle University; company formed January 2023.

Thesis fit. matches 1 decoding_nature keyword: synthetic biology.

Named on the public record. Natalio Krasnogor, Jaume Bacardit, Nikolay Zenkin, Richard Daniel.

Funding. filed a share allotment about 12 months ago with the irregular capital increase typical of priced investment

not mentioned in the current funding-news feeds
Score breakdown
thesis fit0.5 x 0.25matches 1 decoding_nature keyword: synthetic biology
funding signal1 x 0.2filed a share allotment about 12 months ago with the irregular capital increase typical of priced investment
evidence depth0.75 x 0.15public record carries description, company_url, named people
recency0.312 x 0.12signal is 45 months old
signal type1 x 0.2spinout signal
geography1 x 0.08based in the UK, inside the fund's scope

Applied AI

MOTMOT, INC.

79.8/100
NSF SBIR/STTR awardsUS

Support more effective and accessible inspection of public drinking water infrastructure. Many water utilities across the United States struggle to monitor the condition of buried pipelines due to high inspection costs, aging systems, and the operational difficulty of accessing live, pressurized water mains. This project will develop and test a robotic inspection method that can move through water main without requiring shutdowns or excavation. […]

Why now. Awarded an NSF SBIR Fast-Track grant in August 2025 (about $1,555,000) - non-dilutive federal money, typically ahead of a priced round.

Thesis fit. matches 2 applied_ai keywords: autonomous inspection, sensor fusion.

Named on the public record. Elliot Smith.

Funding. outside the UK register, so equity filings are not visible to this source

not mentioned in the current funding-news feeds
Score breakdown
thesis fit1 x 0.25matches 2 applied_ai keywords: autonomous inspection, sensor fusion
funding signal0.5 x 0.2outside the UK register, so equity filings are not visible to this source
evidence depth0.75 x 0.15public record carries description, company_url, named people
recency0.958 x 0.12signal is 14 months old
signal type0.7 x 0.2grant signal
geography1 x 0.08based in DETROIT, MI, inside the fund's scope

LAB2701 LLC

67.8/100
NSF SBIR/STTR awardsUS

The creation of quantum Oscillator Processing Units (OPUs), which would be a disruptive technology for Edge Computing. The OPU would increase data security by offering a local solution. The quantum OPU works as a remote solution for many industrial AI applications that require powerful and fast computing on the edge. […]

Why now. Awarded an NSF SBIR Phase I grant in August 2026 (about $304,526) - non-dilutive federal money, typically ahead of a priced round.

Thesis fit. matches 1 applied_ai keyword: industrial ai.

Named on the public record. Edmon Perkins.

Funding. outside the UK register, so equity filings are not visible to this source

not mentioned in the current funding-news feeds
Score breakdown
thesis fit0.5 x 0.25matches 1 applied_ai keyword: industrial ai
funding signal0.5 x 0.2outside the UK register, so equity filings are not visible to this source
evidence depth0.75 x 0.15public record carries description, company_url, named people
recency1 x 0.12signal is 2 months old
signal type0.7 x 0.2grant signal
geography1 x 0.08based in ATWOOD, OK, inside the fund's scope

CRABLINE ROBOTICS LLC

67.8/100
NSF SBIR/STTR awardsUS

Improved inspection, monitoring, and maintenance of critical infrastructure, with far-reaching impacts across industry, society, and education. The technology developed in the proposed activities will offer a safer, more precise, and cost-effective alternative to traditional operations. Its ability to perform stable, close-contact work on submerged structures reduces operational risks, lowers carbon-intensive vessel use, and enables intervention in previously inaccessible or hazardous locations. […]

Why now. Awarded an NSF SBIR Phase II grant in August 2026 (about $1,249,999) - non-dilutive federal money, typically ahead of a priced round.

Thesis fit. matches 1 applied_ai keyword: digital twin.

Named on the public record. John Grezmak.

Funding. outside the UK register, so equity filings are not visible to this source

not mentioned in the current funding-news feeds
Score breakdown
thesis fit0.5 x 0.25matches 1 applied_ai keyword: digital twin
funding signal0.5 x 0.2outside the UK register, so equity filings are not visible to this source
evidence depth0.75 x 0.15public record carries description, company_url, named people
recency1 x 0.12signal is 2 months old
signal type0.7 x 0.2grant signal
geography1 x 0.08based in SHAKER HEIGHTS, OH, inside the fund's scope

PERSEUS MATERIALS, INC.

67.8/100
NSF SBIR/STTR awardsUS

Relates to advancing oversized fiber-reinforced polymer components manufacturing by significantly reducing lead times for critical large-scale composites. Conventional mold-based production of large composite structures requires 4-14 months just to fabricate molds. This technology eliminates mold creation times and reduces part production to less than two weeks at a fraction of the cost, dramatically accelerating production of large-scale fiber-reinforced polymers. […]

Why now. Awarded an NSF SBIR Phase II grant in July 2026 (about $1,239,445) - non-dilutive federal money, typically ahead of a priced round.

Thesis fit. matches 1 applied_ai keyword: process optimization.

Named on the public record. Daniel Lee.

Funding. outside the UK register, so equity filings are not visible to this source

not mentioned in the current funding-news feeds
Score breakdown
thesis fit0.5 x 0.25matches 1 applied_ai keyword: process optimization
funding signal0.5 x 0.2outside the UK register, so equity filings are not visible to this source
evidence depth0.75 x 0.15public record carries description, company_url, named people
recency1 x 0.12signal is 3 months old
signal type0.7 x 0.2grant signal
geography1 x 0.08based in KNOXVILLE, TN, inside the fund's scope

Why this finds what databases miss

A paid database is a lagging index. A company appears in PitchBook because it has already raised and already been written about, so by the time you can query it, every fund running the same search is looking at the same name.

Public administrative data is a leading indicator. A company shows up in a research grant, a federal award or a share-allotment filing because it exists and is building, often years before it raises and with no intent to be found. That gap is the sourcing window, and it is where the advantage sits: reach the right founders earlier, and get into the round earlier.

It costs nothing to monitor. No scraping behind logins, no paywalled provider, no personal contact data. Every contact path is a company’s own public channel.

The scoring is yours to set

Six rule-based factors decide what rises. Nothing is learned or fitted, so every score traces back to a stated rule and can be defended out loud in an investment committee.

The weights below are my reading of a generic thesis. Point them at how your fund actually defines its universe, calibrate them against the companies you passed on and why, and the same machinery ranks against your conviction instead of mine. Every number here is one line in a config file, so what counts as a high-scoring venture changes the moment you decide it should.

FactorWeightWhat it asks
thesis fit25%How strongly the company's own description or declared classification matches the thesis.
funding signal20%Whether anyone has backed the company with equity, read from statutory share-allotment filings.
signal type20%What kind of public signal it is, and whether more than one record corroborates it.
evidence depth15%How much the public record actually reveals - enough to form a view, or not.
recency12%How fresh the signal is, full marks inside a year and nothing beyond five.
geography8%Whether the company sits inside the fund's UK, Europe and US scope.

Where this goes with a team behind it

Everything above was built solo, in days, against generic thesis definitions. That is the floor of what this method produces. The ceiling depends on three things a fund already has and I did not.

  • Your thesis, encoded. The keyword and classification sets are my reading of three theses from the outside. Replaced with how Systemiq actually defines them, and calibrated against the companies you passed on and why, the filtering gets materially sharper at no extra running cost.
  • More scouts. Patents, EU research consortia, private placement filings and founder crossover are scoped and queued. Each widens the net into geographies and signal types the five running today cannot see.
  • Your judgement in the loop. Every ranking factor is a number in a config file. When an investor disagrees with a placement, that disagreement becomes a weight change, and the system carries it forward permanently.

The work this removes is the repetitive part: screening thousands of records every week to find the dozen worth a conversation. That happens overnight, on a cadence, whether or not anyone has time for it. What reaches an investor is a short, ranked list with the evidence attached, so the hours go into judging companies rather than finding them.

From sourcing to first conversation

Finding a founder is half the problem. The other half is reaching them early, warmly and at a volume a small team can sustain. I built exactly this engine once before at Reef: a data-driven acquisition system that found and qualified targets automatically, ran outbound at a A$410 customer acquisition cost and roughly 8.5x return on ad spend inside thirty days, and carried the company from zero to sold in twelve months.

Pointed at this pipeline, the same machinery closes the loop: a scout surfaces a founder from their first public trace, the system drafts an approach grounded in the specific signal that surfaced them, and it tracks what turns into a conversation. Over time, replies become another ranking input, so the sourcing gets better at finding the founders who actually want to talk. That is the sourcing spike, running end to end, and it is the part I would most want to build alongside an investment team.

What it does not do yet

A sourcing process that only presents its strengths is not worth trusting. Continental Europe is thin, because both register-grade sources are UK and the US coverage is grant-based. The press check reads current funding-news feeds, so it detects present coverage rather than historical. And the company register supplies the largest share of screened records and almost none of the shortlist, because an incorporation record carries no description of the business. Each of these has a known fix already scoped.

Generated 01 October 2026. No scraping behind logins, no personal contact details, and every contact path is a company’s own public channel.

The second tool

Investability Desk

Finding a company early is half the job. The other half is deciding which of them deserves a conversation, and what to open it with. The desk runs standardised first-pass diligence against two rubrics: the fundable conditions, moat mechanisms, and anti-portfolio from my own VC thesis, and a fund's own published investment parameters.

It will take a shortlist from any source. A Pitchbook export, a Harmonic AI list, a spreadsheet off a partner's desk, or the radar's own top slice all enter the same way, and every name on the list comes back with a verdict and the question to open with. The radar's score is left untouched and the two are never blended.

Each verdict closes on whether a market could plausibly carry the exit a fund needs. On the central case a company would need to exit at about $4.3bn to return a $150m fund on its own, within a band of $2.3bn to $10.7bn. Most companies fail that immediately, and saying so in week one is the useful part.

22
Ventures taken in
22
Fully reviewed and published
8
Scored dimensions
6
Fundable conditions derived

What it returns

Six verdicts, three of which are a no. A rejection that names its reason and the capital pool that fits instead is worth more than a ranking of enthusiasm, and every verdict here is produced by rule from bands visible on the page.

3Engage now

Clears every condition, and the weakest point is one a conversation can move.

4Diligence now

Clears every condition with no coachable gap. Straight to a first call.

3Track

Not yet, with the signal that would change the answer named.

4Insufficient evidence

Too little disclosed to assess. Unread rather than rejected.

7Avoid

A named reason not to proceed, and the capital pool that fits instead.

1Out of universe

Outside the fund's own published parameters.

The 8 dimensions underneath

Weights sum to one and the total never renders without these rows underneath it. Each dimension cites one anchor from the thesis and one from the fund's own published material, which is what makes the rubric theirs. Where the public record is silent, a dimension scores the undisclosed band and says so.

Unit economics today, subsidy-free

0.16

Does the buyer pay less with this than without it, today, with no subsidy or credit revenue?

Sources

Thesis. Condition 2; Chi's law that industries gravitate to the best unit economics; the Green Premium method

Fund's own material. "gross profitable unit economics"; the commitment to first-quartile returns

Route to market and channel conflict

0.16

Who has to sell this, does that party profit from the thing it reduces, and can the company reach the buyer without the incumbent's permission?

Sources

Thesis. Condition 6; the distribution thread; moat mechanism 5, incumbent agency

Fund's own material. Ohme, whose stated whole advantage is having figured out how to make partnerships work

Friction and soft-cost load

0.10

What share of the buyer's total cost is everything other than the product, and does the company compress it or inherit it?

Sources

Thesis. Station A: soft costs above 60% of project cost with under 10% of qualified US sites developed; heat pumps blocked by installers and codes

Fund's own material. Greeneye, which retrofits onto farmers' existing equipment rather than replacing it

Named moat mechanism

0.14

Which of the five mechanisms holds the advantage, or is this a temporary lead?

Sources

Thesis. Part V.6, the five mechanisms; Henderson's ownability ladder and the defect test; Condition 3

Fund's own material. "technological moats"; data advantages that compound as the solution grows

Capital efficiency and fund-return math

0.16

Can this return the fund at the ownership Systemiq can get, and how much capital does it consume getting there?

Sources

Thesis. Condition 4; the power law, where 5% of capital produced 60% of returns; cleantech 1.0 cause 1

Fund's own material. "capital efficiency"; the asset-light principle, that neither Greeneye nor Archetype AI owns the physical assets it transforms

Time to liquidity inside the clock

0.10

Is there a credible exit inside ten years, given that nine years is the average even for the companies that succeed spectacularly?

Sources

Thesis. Condition 5; the fund clock, ten years plus two one-year extensions; cleantech 1.0 cause 2

Fund's own material. "commercial scale at pace"; the page-7 stage definition, beyond R&D and product validation and ready to scale

Commercial traction and deployment proof

0.10

Has anyone bought it twice?

Sources

Thesis. Repeat purchase across seasons as the leading indicator; Greeneye's fourth commercial season

Fund's own material. Applied AI companies hitting $3-5m revenue within the first year; Hoxton's $8m of customer commitments

Impact coupling

0.08

Does the impact grow with every unit of revenue, or is it a separate track that marketing maintains?

Sources

Thesis. The coupling test; rebound accounting on efficiency claims

Fund's own material. "positive impact is a function of returns"; "we invest only if a company is system-changing"

The 6 fundable conditions are derived from these bands rather than assessed separately, so a condition and the evidence underneath it can never disagree. A condition that fails produces an Avoid naming the condition, its source, and the layer of the capital stack that should be funding that risk instead.

The run

22 ventures reviewed out of 22 taken in, across two feeds: 12 from the sourcing radar and 10 from the hand-assembled list. The hand-assembled half stands in for a Pitchbook or Harmonic export and arrived with no stage, round or description attached, which is how the desk gets tested on what a real shortlist looks like. Every band was drafted from the company's own public pages, each fetched and cited with the date it was read, then reviewed by hand before publication.

3

Engage now

Sensat

Engage now
74/100
Hand-assembled listApplied AiGB

The call. Scores 74.0 of 100 and clears every condition. The weakest point is named moat mechanism at 2/4, which is a gap a conversation can move rather than a structural flaw.

Open with. You describe the advantage. Is it structural, is it relational, or is it a lead? If it is relational, tell me about a promise you kept when it was expensive.

Bands, market read and evidence

Visualise infrastructure projects in real-world context, identify risks earlier and accelerate decision-making across teams.

Could the market carry the exit? Plausible but unproven at the scale required. Infrastructure project software for energy and water is a large physical-world market, and 30-plus people supporting $200bn of projects across eight countries is the right kind of leverage for it. Reaching roughly $4.3bn needs this to become the system of record for infrastructure delivery rather than one visualisation tool among several, and nothing published shows which of those it is becoming. Contract value and renewal rate would settle it and neither is public.

Unit economics today, subsidy-free3/4
Route to market and channel conflict3/4
Friction and soft-cost load4/4
Named moat mechanism2/4
Capital efficiency and fund-return math3/4
Time to liquidity inside the clock3/4
Commercial traction and deployment proof3/4
Impact coupling3/4

Every dimension had something to read. 12 pages read. Reviewed by WA on 2026-10-08.

Crover

Engage now
59/100
Hand-assembled listDecoding NatureGB

The call. Scores 59.0 of 100 and clears every condition. The weakest point is route to market and channel conflict at 2/4, which is a gap a conversation can move rather than a structural flaw.

Open with. You are hiring into channel before the channel is public. Which partner type have you decided to build around, and why that one?

Bands, market read and evidence

The future of grain storage management is now! Transform your operations with the world

Could the market carry the exit? Grain storage monitoring across farms, cooperatives and ports is a large physical market with a clear loss to recover. Reaching the roughly $4.3bn central exit the model requires would need the port and large-operator segment rather than the farm segment, because device counts at farm level are unlikely to carry it. Which segment the company is actually winning is the first thing to establish on a call.

Unit economics today, subsidy-free2/4
Route to market and channel conflict2/4
Friction and soft-cost load3/4
Named moat mechanism2/4
Capital efficiency and fund-return math3/4
Time to liquidity inside the clock3/4
Commercial traction and deployment proof2/4
Impact coupling2/4

Every dimension had something to read. 13 pages read. Reviewed by WA on 2026-10-08.

Pano AI

Engage now
58/100
Hand-assembled listDecoding NatureUS

The call. Scores 58.0 of 100 and clears every condition. The weakest point is friction and soft-cost load at 1/4, which is a gap a conversation can move rather than a structural flaw.

Open with. What has to happen on a customer's site between signing and the first unit of value, and who pays for that?

Bands, market read and evidence

Improve wildfire & bushfire response with Pano's early detection & intelligence system. Equip first responders with data for faster, safer coordinated action.

Could the market carry the exit? Wildfire detection for utilities, agencies and large landowners is a growing physical-world market whose buyer faces catastrophic liability as the alternative, which is the sort of forced-purchase dynamic that supports pricing power. Whether it reaches the roughly $4.3bn central exit the model requires depends on how much of the network Pano has to own in order to serve it. The capital question and the market question are the same question here.

Unit economics today, subsidy-free2/4
Route to market and channel conflict2/4
Friction and soft-cost load1/4
Named moat mechanism3/4
Capital efficiency and fund-return math2/4
Time to liquidity inside the clock3/4
Commercial traction and deployment proof3/4
Impact coupling3/4

1 of 8 dimensions are undisclosed in the public record. 7 pages read. Reviewed by WA on 2026-10-08.

4

Diligence now

Heat Geek

Diligence now
79/100
Hand-assembled listElectrificationGB

The call. Scores 79.0 of 100 and clears every condition, with no gap a conversation would fix. This goes to a first call rather than a coaching conversation.

Bands, market read and evidence

Heat Geek makes it easy for homeowners to save money on their bills with AI designed, efficient heat pump systems.

Could the market carry the exit? Plausible on size and awkward on structure. UK heat pump retrofit is a large physical-world market with a long runway, so the roughly $4.3bn central bar is not absurd for whoever ends up owning installer trust at national scale. The structural question for this fund is that the end customer is a homeowner, and Systemiq's published parameter is "founders building B2B companies". Heat Geek is B2B2C: it sells training, verification and demand to installers, and the installers sell to households. Whether that counts is a partner conversation rather than something the public record settles.

Unit economics today, subsidy-free3/4
Route to market and channel conflict4/4
Friction and soft-cost load3/4
Named moat mechanism3/4
Capital efficiency and fund-return math3/4
Time to liquidity inside the clock3/4
Commercial traction and deployment proof3/4
Impact coupling3/4

Every dimension had something to read. 10 pages read. Reviewed by WA on 2026-10-08.

Dexory

Diligence now
69/100
Hand-assembled listApplied AiGB

The call. Scores 69.0 of 100 and clears every condition, with no gap a conversation would fix. This goes to a first call rather than a coaching conversation.

Open with. Does the impact grow because you sell more, or alongside selling more? Those are different businesses.

Bands, market read and evidence

Gain real-time warehouse intelligence with DexoryView. AI-driven intelligence that eliminates blind spots, improves accuracy and delivers ROI in under 6 months.

Could the market carry the exit? Warehouse inventory intelligence across 3PL, retail fulfilment and manufacturing is a large physical-world market of the kind the report says the fund looks for. Whether it supports the roughly $4.3bn central exit the model requires turns on whether the subscription can carry hardware cost at scale, which the fetched evidence does not answer. That is the first diligence question rather than a reason to stop.

Unit economics today, subsidy-free4/4
Route to market and channel conflict4/4
Friction and soft-cost load2/4
Named moat mechanism2/4
Capital efficiency and fund-return math2/4
Time to liquidity inside the clock3/4
Commercial traction and deployment proof3/4
Impact coupling1/4

1 of 8 dimensions are undisclosed in the public record. 13 pages read. Reviewed by WA on 2026-10-08.

Sylvera

Diligence now
66.5/100
Hand-assembled listDecoding NatureGB

The call. Scores 66.5 of 100 and clears every condition, with no gap a conversation would fix. This goes to a first call rather than a coaching conversation.

Open with. Your saving is stated as a percentage. Percentage of what baseline, and who measured it?

Bands, market read and evidence

Independent ratings, market data, and geospatial analysis for carbon credits and low carbon commodities. Trusted by investors, corporates, and developers.

Could the market carry the exit? The central question, and it is about the market rather than the company. Sylvera is a well-built intelligence business whose addressable market is the size and durability of carbon and environmental-attribute markets. If those markets institutionalise under CORSIA, Article 6 and compliance regimes, the roughly $4.3bn bar is reachable for the infrastructure layer underneath them. If voluntary demand stays volatile, this is an excellent business in a market too small to return a fund on its own. That is a view on policy and demand rather than on execution, and it is the thing to form a house view on before a first call.

Unit economics today, subsidy-free2/4
Route to market and channel conflict3/4
Friction and soft-cost load3/4
Named moat mechanism3/4
Capital efficiency and fund-return math3/4
Time to liquidity inside the clock3/4
Commercial traction and deployment proof2/4
Impact coupling2/4

Every dimension had something to read. 12 pages read. Reviewed by WA on 2026-10-08.

MORF-BIO LTD

Diligence now
58.5/100
Sourcing RadarDecoding NatureGB

The call. Scores 58.5 of 100 and clears every condition, with no gap a conversation would fix. This goes to a first call rather than a coaching conversation.

Open with. Does the impact grow because you sell more, or alongside selling more? Those are different businesses.

Bands, market read and evidence

MORF-Bio Ltd is a spin out founded by Prof Gavin Thomas (CSO), Dr Joyce Bennett (CEO) and Dr Vicki Springthorpe (CTO), who were the PI, Project Manager and bioinformatician on our BBSRC funded IB Catalyst DETOX project. We secured a small additional grant from BBSRC to develop this further and went through the Innovate iCURE program and span out in October 2025 after raising seed funding led by the Deeptech Seed Fund. We now employ two additional software developers (2 York graduates in computer science) and will launch our first products in summer 2026. […]

Could the market carry the exit? Cannot be assessed from this evidence. Bioprocess optimisation software sells into industrial biotechnology and pharmaceutical manufacturing, both large, and a seventeen-month-old spinout with no named customer gives no basis to judge what share it reaches. Against a roughly $4.3bn bar the honest answer is that it is too early to form a view, which is itself the finding.

Unit economics today, subsidy-free2/4
Route to market and channel conflict3/4
Friction and soft-cost load3/4
Named moat mechanism2/4
Capital efficiency and fund-return math3/4
Time to liquidity inside the clock2/4
Commercial traction and deployment proof2/4
Impact coupling1/4

1 of 8 dimensions are undisclosed in the public record. 12 pages read. Reviewed by WA on 2026-10-08. Radar score 85.2.

3

Track

Antobot

Track
46.5/100
Hand-assembled listDecoding NatureGB

The call. Scores 46.5 of 100, below the 55 threshold, holding on named moat mechanism. What would change it: one of the five mechanisms becoming nameable against evidence.

Open with. In five years, when a larger competitor wants this market, what specifically stops them? Name the mechanism rather than the advantage.

Bands, market read and evidence

Autonomous robotics and intelligence for commercial growers.

Could the market carry the exit? Cannot be assessed from this evidence, and the narrowness is the issue rather than the technology. Everything published is about strawberries on tabletops. That is a sensible wedge and it is a long way from the roughly $4.3bn central bar unless the platform crosses into other crops and other growing systems. Whether the autonomy stack generalises, and what it costs to make it generalise, is the first question worth asking.

Unit economics today, subsidy-free2/4
Route to market and channel conflict2/4
Friction and soft-cost load2/4
Named moat mechanism1/4
Capital efficiency and fund-return math2/4
Time to liquidity inside the clock2/4
Commercial traction and deployment proof2/4
Impact coupling2/4

1 of 8 dimensions are undisclosed in the public record. 5 pages read. Reviewed by WA on 2026-10-08.

GitLife Biotech

Track
39/100
Sourcing RadarDecoding NatureGB

The call. Scores 39.0 of 100, below the 55 threshold, holding on unit economics today, subsidy-free. What would change it: a published price, or an independent measurement of the saving.

Open with. What does your buyer pay today for the thing you replace, and what do they pay with you in place? Subsidy and credit revenue excluded.

Bands, market read and evidence

We will set the global standard in the management of provenance and integrity for biological assets; enabling the potential of synthetic biology to be realised worldwide.

Could the market carry the exit? Cannot be assessed from this evidence. A system of record for biological assets could be a large market if it became the standard, and a feature of an existing laboratory information vendor if it did not. Those two outcomes sit orders of magnitude apart against a roughly $4.3bn bar, and nothing published distinguishes them.

Unit economics today, subsidy-free1/4
Route to market and channel conflict2/4
Friction and soft-cost load2/4
Named moat mechanism2/4
Capital efficiency and fund-return math2/4
Time to liquidity inside the clock1/4
Commercial traction and deployment proof1/4
Impact coupling1/4

4 of 8 dimensions are undisclosed in the public record. 5 pages read. Reviewed by WA on 2026-10-08. Radar score 75.5.

MOTMOT, INC.

Track
36/100
Sourcing RadarApplied AiUS

The call. Scores 36.0 of 100, below the 55 threshold, holding on unit economics today, subsidy-free. What would change it: a published price, or an independent measurement of the saving.

Open with. What does your buyer pay today for the thing you replace, and what do they pay with you in place? Subsidy and credit revenue excluded.

Bands, market read and evidence

Support more effective and accessible inspection of public drinking water infrastructure. Many water utilities across the United States struggle to monitor the condition of buried pipelines due to high inspection costs, aging systems, and the operational difficulty of accessing live, pressurized water mains. This project will develop and test a robotic inspection method that can move through water main without requiring shutdowns or excavation. […]

Could the market carry the exit? Cannot be assessed from this evidence, and the shape of the doubt is specific. US water infrastructure is unarguably large, with the site citing "$625B Needed in the next 20 years". The question is what share of that a condition-assessment vendor captures when its customers are municipalities buying on constrained public budgets through slow procurement. Large problem and large addressable revenue are not the same thing, and a roughly $4.3bn exit needs the second.

Unit economics today, subsidy-free1/4
Route to market and channel conflict2/4
Friction and soft-cost load1/4
Named moat mechanism1/4
Capital efficiency and fund-return math1/4
Time to liquidity inside the clock2/4
Commercial traction and deployment proof2/4
Impact coupling2/4

4 of 8 dimensions are undisclosed in the public record. 6 pages read. Reviewed by WA on 2026-10-08. Radar score 79.8.

4

Insufficient evidence

illumion

Insufficient evidence
25/100
Sourcing RadarElectrificationGB

The call. 8 of 8 dimensions are undisclosed in the public record, so there is not enough to assess. The company is not rejected; it is unread. One call would resolve most of it.

Open with. Who has to sell this to the end buyer, and what do they earn for doing it?

Bands, market read and evidence

illumion operates an energy storage management platform assessing battery performance.

Could the market carry the exit? Cannot be assessed. Nothing is known about what this company sells, so nothing can be said about whether its market would carry the exit the fund needs.

Unit economics today, subsidy-free1/4
Route to market and channel conflict1/4
Friction and soft-cost load1/4
Named moat mechanism1/4
Capital efficiency and fund-return math1/4
Time to liquidity inside the clock1/4
Commercial traction and deployment proof1/4
Impact coupling1/4

8 of 8 dimensions are undisclosed in the public record. 0 pages read. Reviewed by WA on 2026-10-08. Radar score 76.2.

LIVING SENSORS INC

Insufficient evidence
25/100
Sourcing RadarDecoding NatureUS

The call. 8 of 8 dimensions are undisclosed in the public record, so there is not enough to assess. The company is not rejected; it is unread. One call would resolve most of it.

Open with. Who has to sell this to the end buyer, and what do they earn for doing it?

Bands, market read and evidence

Improve the ability to detect and manage contamination from per- and polyfluoroalkyl substances (PFAS), a class of persistent industrial chemicals often called “forever chemicals.” PFAS contamination has affected millions of acres of agricultural land in the United States, creating risks for food systems, ecosystems, and rural economies. Current monitoring approaches rely on laboratory testing of soil samples, which is costly and sparse, leaving large areas untested. This project will develop a plant-based environmental sensing system that enables large-scale screening of soils for PFAS contamination. […]

Could the market carry the exit? Cannot be assessed. No public source consulted says what this company sells.

Unit economics today, subsidy-free1/4
Route to market and channel conflict1/4
Friction and soft-cost load1/4
Named moat mechanism1/4
Capital efficiency and fund-return math1/4
Time to liquidity inside the clock1/4
Commercial traction and deployment proof1/4
Impact coupling1/4

8 of 8 dimensions are undisclosed in the public record. 0 pages read. Reviewed by WA on 2026-10-08. Radar score 76.5.

VITIDORE, INC.

Insufficient evidence
25/100
Sourcing RadarDecoding NatureUS

The call. 8 of 8 dimensions are undisclosed in the public record, so there is not enough to assess. The company is not rejected; it is unread. One call would resolve most of it.

Open with. Who has to sell this to the end buyer, and what do they earn for doing it?

Bands, market read and evidence

In mitigating the earth system’s temperature rise. By removing the barriers farmers face to planting permanent cover crops and generating income from the resulting soil carbon gains, this project would make our food supply chain more resilient. Today farmers leave 95% of ~400M US cropland acres bare outside of the cash crop season because the short-term costs, work and risk of annual cover crops outweigh the benefits. […]

Could the market carry the exit? Cannot be assessed. No public source consulted says what this company sells.

Unit economics today, subsidy-free1/4
Route to market and channel conflict1/4
Friction and soft-cost load1/4
Named moat mechanism1/4
Capital efficiency and fund-return math1/4
Time to liquidity inside the clock1/4
Commercial traction and deployment proof1/4
Impact coupling1/4

8 of 8 dimensions are undisclosed in the public record. 0 pages read. Reviewed by WA on 2026-10-08. Radar score 75.8.

Allye Energy

Insufficient evidence
25/100
Hand-assembled listElectrificationGB

The call. 8 of 8 dimensions are undisclosed in the public record, so there is not enough to assess. The company is not rejected; it is unread. One call would resolve most of it.

Open with. Who has to sell this to the end buyer, and what do they earn for doing it?

Bands, market read and evidence

Could the market carry the exit? Cannot be assessed from this evidence. The register says the company is active and engineering-led, and nothing else about what it sells could be read.

Unit economics today, subsidy-free1/4
Route to market and channel conflict1/4
Friction and soft-cost load1/4
Named moat mechanism1/4
Capital efficiency and fund-return math1/4
Time to liquidity inside the clock1/4
Commercial traction and deployment proof1/4
Impact coupling1/4

8 of 8 dimensions are undisclosed in the public record. 0 pages read. Reviewed by WA on 2026-10-08.

7

Avoid

Agreena

Avoid
54.5/100
Hand-assembled listDecoding NatureDK

The call. Credit-dependent nature projects. Margins rest on credit sales, so the business prices a market rather than a product. Better fit: a real-assets or catalytic-capital pool.

Bands, market read and evidence

With Europe’s largest soil carbon programme, Agreena helps farmers profitably transition to regenerative agriculture and companies achieve sustainability goals.

Could the market carry the exit? The addressable market is the voluntary soil-carbon market, whose size is a function of corporate buying appetite rather than of a cost somebody is forced to avoid. On the returns model's central case this would need roughly a $4.3bn exit. The fetched evidence neither supports nor refutes that, and the Playbook's anti-portfolio places credit-dependent nature projects outside venture equity regardless of market size.

Unit economics today, subsidy-free0/4
Route to market and channel conflict3/4
Friction and soft-cost load2/4
Named moat mechanism2/4
Capital efficiency and fund-return math3/4
Time to liquidity inside the clock2/4
Commercial traction and deployment proof3/4
Impact coupling3/4

1 of 8 dimensions are undisclosed in the public record. 9 pages read. Reviewed by WA on 2026-10-08.

AEPNUS TECHNOLOGY INC

Avoid
43/100
Sourcing RadarElectrificationUS

The call. Fails fundable condition: It sits in the deployment-constrained half, or it is a capital-light layer sitting on top of the invention-constrained half rather than inside it. Surfaced by a technology-risk signal (nsf sbir), with no deployment evidence in the public record. Non-dilutive research money is being spent to retire technology risk, which is the grant layer doing its job. Venture equity enters once that risk is retired. Fundamental science belongs in government labs, very capital-intensive assets with corporations, and companies above about $5bn in public markets. In between, any sector works so long as the startup targets a lucrative market and has a shot at 10x or more. Grants and public research money fund the science; venture equity enters once the technology risk is retired. (PLAYBOOK Part III.7 condition 1, via Part I.3 and Part III.5)

Bands, market read and evidence

Aepnus converts chemical waste streams into the essential reagents your business relies on. Our groundbreaking electrochemical technology closes the loop on your chemical supply chains - making your operations more cost-effective, resilient, and sustainable.

Could the market carry the exit? Industrial reagent regeneration sits in a large physical-world market, and the honest answer on the roughly $4.3bn central exit is that it cannot be assessed while it remains unclear whether this is an equipment business, a licensing business or a processing business. Those three have different margins, different capital needs and different exit multiples.

Unit economics today, subsidy-free2/4
Route to market and channel conflict2/4
Friction and soft-cost load1/4
Named moat mechanism2/4
Capital efficiency and fund-return math1/4
Time to liquidity inside the clock1/4
Commercial traction and deployment proof2/4
Impact coupling3/4

3 of 8 dimensions are undisclosed in the public record. 4 pages read. Reviewed by WA on 2026-10-08. Radar score 80.2.

HEAT2POWER INC.

Avoid
36.5/100
Sourcing RadarElectrificationUS

The call. Fails fundable condition: It sits in the deployment-constrained half, or it is a capital-light layer sitting on top of the invention-constrained half rather than inside it. Surfaced by a technology-risk signal (nsf sbir), with no deployment evidence in the public record. Non-dilutive research money is being spent to retire technology risk, which is the grant layer doing its job. Venture equity enters once that risk is retired. Fundamental science belongs in government labs, very capital-intensive assets with corporations, and companies above about $5bn in public markets. In between, any sector works so long as the startup targets a lucrative market and has a shot at 10x or more. Grants and public research money fund the science; venture equity enters once the technology risk is retired. (PLAYBOOK Part III.7 condition 1, via Part I.3 and Part III.5)

Bands, market read and evidence

Efficient storage and reconversion of renewable energy

Could the market carry the exit? Large market, wrong capital. Long-duration storage and industrial waste heat recovery are genuinely enormous, and the site's own figure of "Over 160,000 GWh" wasted annually in Germany alone is credible as an opportunity. The problem for a $150m venture fund is not whether the market supports a roughly $4.3bn company; it is that getting there means financing built thermal assets, and the equity that funds those gets diluted long before the exit. This is the FOAK capital-stack problem in its textbook form.

Unit economics today, subsidy-free2/4
Route to market and channel conflict1/4
Friction and soft-cost load1/4
Named moat mechanism2/4
Capital efficiency and fund-return math1/4
Time to liquidity inside the clock1/4
Commercial traction and deployment proof1/4
Impact coupling3/4

5 of 8 dimensions are undisclosed in the public record. 7 pages read. Reviewed by WA on 2026-10-08. Radar score 80.2.

PERSEUS MATERIALS, INC.

Avoid
32.5/100
Sourcing RadarApplied AiUS

The call. Fails fundable condition: It sits in the deployment-constrained half, or it is a capital-light layer sitting on top of the invention-constrained half rather than inside it. Surfaced by a technology-risk signal (nsf sbir), with no deployment evidence in the public record. Non-dilutive research money is being spent to retire technology risk, which is the grant layer doing its job. Venture equity enters once that risk is retired. Fundamental science belongs in government labs, very capital-intensive assets with corporations, and companies above about $5bn in public markets. In between, any sector works so long as the startup targets a lucrative market and has a shot at 10x or more. Grants and public research money fund the science; venture equity enters once the technology risk is retired. (PLAYBOOK Part III.7 condition 1, via Part I.3 and Part III.5)

Bands, market read and evidence

Perseus Materials builds the world’s fastest manufacturing system for large-scale composite parts without molds, tooling, or delays. By reinventing how composites are made, we unlock a new era of structural materials: faster to produce, easier to assemble, and strong enough for real-world scale.

Could the market carry the exit? Cannot be assessed from this evidence. Large-scale composites serve wind, aerospace and marine, which are substantial physical-world markets, and two pages carrying one capability claim and a block of placeholder text give no basis to judge whether this company reaches any of them. The roughly $4.3bn question cannot be opened yet.

Unit economics today, subsidy-free2/4
Route to market and channel conflict1/4
Friction and soft-cost load1/4
Named moat mechanism2/4
Capital efficiency and fund-return math1/4
Time to liquidity inside the clock1/4
Commercial traction and deployment proof1/4
Impact coupling1/4

6 of 8 dimensions are undisclosed in the public record. 2 pages read. Reviewed by WA on 2026-10-08. Radar score 67.8.

LAB2701 LLC

Avoid
28.5/100
Sourcing RadarApplied AiUS

The call. Fails fundable condition: It sits in the deployment-constrained half, or it is a capital-light layer sitting on top of the invention-constrained half rather than inside it. Surfaced by a technology-risk signal (nsf sbir), with no deployment evidence in the public record. Non-dilutive research money is being spent to retire technology risk, which is the grant layer doing its job. Venture equity enters once that risk is retired. Fundamental science belongs in government labs, very capital-intensive assets with corporations, and companies above about $5bn in public markets. In between, any sector works so long as the startup targets a lucrative market and has a shot at 10x or more. Grants and public research money fund the science; venture equity enters once the technology risk is retired. (PLAYBOOK Part III.7 condition 1, via Part I.3 and Part III.5)

Bands, market read and evidence

The Oscillator Processing Unit (OPU) is an oscillator-based computer, whose development is sponsored by the National Science Foundation. Analog computing is the key to overcoming the von Neumann bottleneck, decreasing energy requirements, and increasing computational density past the limits of

Could the market carry the exit? Cannot be assessed from this evidence. A general-purpose analog processor aimed at "any other source of vibrations" is either a very large market or a collection of small ones, and a publicly sponsored development programme with no named customer gives no basis to choose between them against a roughly $4.3bn bar.

Unit economics today, subsidy-free1/4
Route to market and channel conflict1/4
Friction and soft-cost load1/4
Named moat mechanism2/4
Capital efficiency and fund-return math1/4
Time to liquidity inside the clock1/4
Commercial traction and deployment proof1/4
Impact coupling1/4

7 of 8 dimensions are undisclosed in the public record. 4 pages read. Reviewed by WA on 2026-10-08. Radar score 67.8.

BERRYCELLS LTD

Avoid
25/100
Sourcing RadarElectrificationGB

The call. Fails fundable condition: It sits in the deployment-constrained half, or it is a capital-light layer sitting on top of the invention-constrained half rather than inside it. Surfaced by a technology-risk signal (ukri), with no deployment evidence in the public record. Non-dilutive research money is being spent to retire technology risk, which is the grant layer doing its job. Venture equity enters once that risk is retired. Fundamental science belongs in government labs, very capital-intensive assets with corporations, and companies above about $5bn in public markets. In between, any sector works so long as the startup targets a lucrative market and has a shot at 10x or more. Grants and public research money fund the science; venture equity enters once the technology risk is retired. (PLAYBOOK Part III.7 condition 1, via Part I.3 and Part III.5)

Bands, market read and evidence

Explore Berry Cells, a cleaner power solution using berry juice for efficient dye-sensitized cells. Learn our approach to sustainable, smart energy for the Internet of Things.

Could the market carry the exit? Cannot be assessed from this evidence, and the honest reading is that the question is premature. A company seven months old publishing an instructional page on how to make its own device is at the research stage, where the number that matters is the cost of the next experiment rather than the size of a plausible exit.

Unit economics today, subsidy-free1/4
Route to market and channel conflict1/4
Friction and soft-cost load1/4
Named moat mechanism1/4
Capital efficiency and fund-return math1/4
Time to liquidity inside the clock1/4
Commercial traction and deployment proof1/4
Impact coupling1/4

8 of 8 dimensions are undisclosed in the public record. 3 pages read. Reviewed by WA on 2026-10-08. Radar score 79.5.

CRABLINE ROBOTICS LLC

Avoid
25/100
Sourcing RadarApplied AiUS

The call. Fails fundable condition: It sits in the deployment-constrained half, or it is a capital-light layer sitting on top of the invention-constrained half rather than inside it. Surfaced by a technology-risk signal (nsf sbir), with no deployment evidence in the public record. Non-dilutive research money is being spent to retire technology risk, which is the grant layer doing its job. Venture equity enters once that risk is retired. Fundamental science belongs in government labs, very capital-intensive assets with corporations, and companies above about $5bn in public markets. In between, any sector works so long as the startup targets a lucrative market and has a shot at 10x or more. Grants and public research money fund the science; venture equity enters once the technology risk is retired. (PLAYBOOK Part III.7 condition 1, via Part I.3 and Part III.5)

Bands, market read and evidence

Improved inspection, monitoring, and maintenance of critical infrastructure, with far-reaching impacts across industry, society, and education. The technology developed in the proposed activities will offer a safer, more precise, and cost-effective alternative to traditional operations. Its ability to perform stable, close-contact work on submerged structures reduces operational risks, lowers carbon-intensive vessel use, and enables intervention in previously inaccessible or hazardous locations. […]

Could the market carry the exit? Cannot be assessed. Nothing published says who buys this, what it costs or what it replaces, so the roughly $4.3bn question has no purchase. Ordnance retrieval suggests a government buyer, which would make this a procurement business rather than a venture-scalable one, and that is an inference the record does not support.

Unit economics today, subsidy-free1/4
Route to market and channel conflict1/4
Friction and soft-cost load1/4
Named moat mechanism1/4
Capital efficiency and fund-return math1/4
Time to liquidity inside the clock1/4
Commercial traction and deployment proof1/4
Impact coupling1/4

8 of 8 dimensions are undisclosed in the public record. 9 pages read. Reviewed by WA on 2026-10-08. Radar score 67.8.

1

Out of universe

Vertical Future

Out of universe
35.5/100
Hand-assembled listDecoding NatureGB

The call. Out of universe: The statutory register records this company as Liquidation (registered as 10379325, incorporated 16 September 2016). A company being wound up is outside the universe whatever its technology looks like.

Bands, market read and evidence

Could the market carry the exit? Settled rather than assessed. The company is in liquidation, so the question of whether its market could support a roughly $4.3bn exit is answered by events. The transferable lesson is the one the Playbook already records: venture-funded first-of-a-kind heavy infrastructure is the category that produced cleantech 1.0's losses, and controlled-environment agriculture has been one of its clearest modern examples.

Unit economics today, subsidy-free1/4
Route to market and channel conflict2/4
Friction and soft-cost load2/4
Named moat mechanism1/4
Capital efficiency and fund-return math2/4
Time to liquidity inside the clock0/4
Commercial traction and deployment proof2/4
Impact coupling1/4

4 of 8 dimensions are undisclosed in the public record. 6 pages read. Reviewed by WA on 2026-10-08.

What the record supports, and what it does not

Across 22 ventures and 176 scored dimensions, nearly half came back undisclosed. That is a finding about what companies publish at this stage, and it is the reason the output ends on a question.

17
Read from the live site
1
Read only from the archive
4
Nothing readable anywhere
7
Median pages read, per venture
139
Pages read in total
48%
Dimensions undisclosed

The company's own site is read first. Where a site refused every request or no longer resolves, the Wayback Machine is read instead and every archived claim is labelled and dated to its snapshot. Alongside both, the UK and US statutory registers supply incorporation date, company status and declared activity, which is how one venture here turned out to be in liquidation while its website was simply unreachable. A further 3 fetched pages were read and then refused for not naming the company, and they are counted nowhere above.

The narrative read per dimension is AI-drafted from the fetched evidence and nothing else, then reviewed by hand. Bands, weights, conditions and verdicts are rule-based, and the renderer refuses to publish a venture nobody has signed off.

Most early-stage companies publish no pricing, no channel and no repeat-purchase evidence. That scores as undisclosed, which is the honest reading and is also what a first call is for.

Marker scans read marketing copy, so a company that writes well about its channel can score better than one that sells well and writes badly. The review step corrected a lot: the heaviest change in this run moved a route to market from 1 of 4 to 4 of 4, because the rule wanted a page whose address said partner while the company ran a named installer network.

Four ventures could not be read from any public source. Two carry no website in the feed, one carries a domain belonging to a different company, and one has a site that no longer resolves and no archived copy. The verdict says so.

Stage and round size are usually unknown for anything an upstream feed surfaces, and neither is ever guessed. Where a US issuer has filed a Form D the date is read from the SEC, and that hit one venture in twelve.

No entity resolution across sources. A company appearing in two feeds would be assessed twice under two names.

Run generated 08 October 2026. Free and public sources only, no paid data provider and no API keys. Every claim on a venture page resolves to a URL with a fetch date or to a printed assumption.

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