Baseline first
No model gets proposed before we have measured how the decision is made today. Without that number, "better" is a marketing claim.
About
We were founded to say no more often
DAIC Labs started from a pattern we kept seeing on the inside: organisations commissioning ambitious data and AI programmes without ever establishing how well the decision was already being made. Without a baseline, no result can be judged — so every project succeeds on paper and very few change anything.
We built the firm around fixing that order of operations. Measure first. Ship something small against real data early. Publish the evaluation, including the parts that disappoint. And build on foundations your own engineers can run once we are gone.
How we operate
Four commitments
Evaluation in the open
We publish precision, recall and the error cases — including the ones that make the work look worse than a demo would.
No proprietary layer
Open tooling, your accounts, your repositories. Leaving us should cost you nothing but the notice period.
Engineers on the call
The people who scope your problem are the people who build it. There is no separate delivery team you meet later.
Shape of the firm
Deliberately small, deliberately senior
Every engagement is staffed with engineers who have run these systems in production, not just built them for a client. We take on fewer projects than we are asked to, which is the only way that stays true.
2 wksTo a fixed-price recommendation
4–6 wksTo a first working increment
3Major clouds supported
<1 dayEnquiry response
Where we are strongest
- Operational data — logistics, supply chain, field operations, service delivery.
- Document-heavy domains where the knowledge exists but cannot be found.
- Teams with a cloud bill they cannot explain.
- Regulated environments where governance is the thing blocking the deal.
FAQ
Questions we get asked first
With a 30-minute call, then usually a two-week fixed-price feasibility phase. We measure how the decision is made today and what a realistic improvement would be. You get a written recommendation either way, including the recommendation not to build.
Not always, but you do need the specific data the model would depend on to be reliable and available. Part of the feasibility phase is establishing whether that is true. Where it is not, the honest answer is that data engineering comes first.
AWS, Azure and Google Cloud. We have no reseller relationship with any of them, so the recommendation follows your constraints rather than our margin.
You do. We work in your repositories and your cloud accounts with standard, open tooling. There is no DAIC Labs runtime that would have to be replaced if you stopped working with us.
Access governance, PII classification, masking and retention are part of the platform work, not an appendix to it. Where data cannot leave your environment, we work inside it. We will sign your DPA and security schedules.
Then we tell you, and we stop. It happens, and it is a considerably cheaper outcome than discovering it after a year of build. Half the value of the feasibility phase is the projects it prevents.
Test the claim on your own problem
Book a call. Bring the version of the problem you would not put in a brief — that is usually the one worth solving.