Step 1: Define the technical milestone the round buys
Unlike applied AI, where the question is mainly commercial, ML-first companies must show the technical problem is solvable. Set a concrete milestone — accuracy on a real task, latency, cost per inference, model size — and show why reaching it unlocks customers.
Step 2: Build a realistic compute budget
Training and experimentation costs can dominate the plan. Estimate compute for each experiment, include failed runs, and note any cloud credits or research partnerships. Investors distrust budgets that assume everything works first time.
Step 3: Secure your IP and team position
If the core work began at a university or former employer, clarify ownership and any licence or spin-out terms before you raise. Make sure all founders and contractors have assigned IP to the company.
- IP assignment agreements for everyone who contributed.
- University spin-out or licence terms agreed in writing.
- Records of datasets used and their licences.
Step 4: Prepare for technical diligence
Expect investors to bring in technical advisers who will review your code, evaluation methods and results. Keep reproducible experiments, honest benchmarks with clear test sets, and documentation of what has not worked.
Step 5: Show a path to a commercial product
Even deep-tech investors need a route to revenue: an API, a licensing model, an embedded product or a vertical application. Early design partners who test your models help show that route is real.
Applied AI versus machine learning fundraising
| Question | Applied AI startup | ML-first company |
|---|---|---|
| Main risk investors fund | Market and distribution | Technical feasibility |
| Key evidence | Paying customers and retention | Benchmarks and research milestones |
| Biggest cost | Sales and product | Compute and research talent |
| Typical diligence | Commercial references | Technical review of code and results |
Questions to prepare before you pitch
- What technical milestone will this round achieve, and how will we know?
- How much compute does the plan need, including failed experiments?
- Does the company own all of its core IP outright?
- How are your benchmarks built, and could results be overstated?
- Who are your design partners, and what would they pay for?
How KJ Enterprises evaluates machine learning businesses
KJ Enterprises backs machine learning teams whose technical milestones connect clearly to a commercial product. We value reproducible results, careful compute planning and clean IP ownership.
If that describes your company, you can apply for investment. Related reading: the complete machine learning funding guide and how to raise for an applied AI startup.