AI-native businesses
Built on deep domain knowledge and hard-won edge cases that can't be replicated overnight. We use AI to accelerate how the product is built, and to run a lean, automated business around it.
Our thesis
Deep software. Lean businesses.
We build with founders who know their industry inside out and need a technical partner to bring their vision to market. The products we build together are packed with hard-won domain knowledge and solved edge cases that aren't easily replicated. We use AI to accelerate how they're built and to run lean, automated businesses around them.
01
What we look for
Domain experts
Founders, often non-technical, with deep knowledge of their industry and an existing network of potential customers.
High-value B2B
Enterprise and mid-market software where each contract is worth a lot, so fewer customers are needed to reach profitability.
Hands-on partnership
Founders who want a technical co-founder, not a passive investor. We build the product alongside you, and where it genuinely suits both sides, we invest too.
02
Where the depth comes from
Most industries run on a patchwork: ERPs, portals, spreadsheets emailed every Monday, PDFs, and APIs that half-work. Every organisation in the chain has a slightly different version of the truth, and people spend their days copying between them.
The edge cases hiding in that mess are exactly what makes a product hard to copy. Founders who've worked inside these industries know where they are. What they usually can't do is build the software that handles them. That's where we come in.
03
Why it's the moat now
Anyone can call an AI model now. Models are becoming a commodity.
Hard-won domain knowledge isn't. A company that can take every messy feed in a market, handle every edge case and turn it into decisions owns something competitors can't download. Add AI on top, and that depth is what makes it useful rather than plausible.
04
How we build it
We take data as it comes.
Customers won't change their systems for you. We build for the files, exports and APIs they already have, in whatever state they're in.
We check everything.
We validate, clean and enrich every input, and we flag what doesn't add up, rather than letting bad data quietly become bad decisions.
We keep the trail.
Every number can be traced back to where it came from. When a customer asks "why does it say that?", there's an answer.
We keep humans where they matter.
We automate the drudgery, and route the genuinely ambiguous cases to people who can judge them.
We make it legible.
Two of our engineers hold PhDs in information visualisation, so presenting data so people can actually act on it is a specialism here, not an afterthought.
And we build the business around it.
This is the part most data projects miss. The data isn't the product. The decisions it enables are, and the company that sells them.
05
Companies we've built this way
Sygnal One: logistics
Shippers' transport data is scattered across ERPs, transport management systems and logistics providers, all in different formats. Sygnal One pulls it into one source of truth, then uses AI to spot savings in mode choice, truck fill and carrier compliance.
Case study coming soon
AdPeak: e-commerce advertising
A company we co-founded. Large online retailers have thousands of products, but advertising tools quietly focus spend on the best-sellers. AdPeak unifies Google Merchant Center, Google Ads and analytics data to optimise whole catalogues.
Read the AdPeak case studyInnov-8: field services
A panel management platform for field service operations. Innov-8 consolidates agency relationships, automates case allocation and tracks performance in real time across energy, utilities and financial services.
06
Is this you?
- You've spent years inside an industry and know exactly what's broken
- You know the people who'd pay to have it fixed
- It's B2B, and each customer is worth a lot to you
- Software is the business, not a side project
- You want founding engineers who'll build it with you, not a supplier who'll build what you specify