How to use this checklist
This checklist is for companies building their own models, ML infrastructure or research-led products, including university spin-outs.
Work through each list before you contact investors. Anything you cannot tick is either a task to finish or a risk to explain openly in your pitch.
1. Company and legal basics
These apply to every company raising money, but gaps here slow rounds more than almost anything else.
- Up-to-date cap table showing every shareholder, option and convertible.
- Signed IP assignments from all founders, staff and contractors.
- Articles of association and any shareholder agreement to hand.
- SEIS or EIS advance assurance applied for, if you are raising from UK angels.
- Company filings at Companies House up to date.
2. Machine learning proof points
The evidence investors in this sector look for first.
- A defined technical milestone linked to customer value.
- Reproducible experiments and honest benchmarks.
- Design partners testing the technology.
- A route to a commercial product.
3. Numbers and financial model
Your model should show how the money you raise gets you to the next milestone.
- Compute budget including failed runs.
- Research hiring plan and costs.
- Option pool needed for research talent.
- University royalties or equity, if applicable.
4. Data room documents
Have these organised in one shared folder before the first meeting.
- IP assignments and any university licence.
- Dataset records and licences.
- Code repository and experiment logs ready for review.
- Technical roadmap.
5. Pitch and investor readiness
Readiness is also about how you run the process.
- A short deck that states the problem, customer, traction and ask in the first few slides.
- A clear amount to raise and a list of what it pays for.
- A target list of investors who back your sector and stage.
- A one-paragraph answer to 'why now?'.
Red flags to fix before you pitch
Investors often stop at these issues:
- University IP terms not agreed.
- Benchmarks on test sets you tuned against.
- Compute plans assuming first-time success.
- No commercial route in sight.
Machine learning readiness: ready versus common gap
| Area | Ready when | Common gap |
|---|---|---|
| IP | Owned or licensed in writing | Ownership unclear |
| Results | Reproducible and honest | Cherry-picked benchmarks |
| Compute | Budget with contingency | Optimistic estimates |
| Commercial | Design partners engaged | Research only |
Questions to prepare before you pitch
- Do we own every piece of our core IP?
- Could an outside expert reproduce our results?
- What will the next milestone cost in compute?
- Which design partner would pay first?
How KJ Enterprises evaluates machine learning businesses
KJ Enterprises backs ML teams with clean IP, reproducible results and a clear path from research to product.
If that describes your company, you can apply for investment. Related reading: the complete funding guide and the step-by-step how-to and costs and terms and the applied AI checklist.