Data reality
We work through your actual records, including the malformed, duplicated, and mislabelled ones. You get a written account of what your data can and cannot support before anyone designs anything.
Approach
Anything works once. We build for the Tuesday after launch.
The demo is not the hard part. The hard part is the system still being correct, affordable, and trusted three months later, when the data has drifted and the person who championed it has moved on. Everything below exists to make that outcome likely rather than lucky.
The chain
Six links. A break in any one of them is a failed project.
Most AI initiatives do not fail at the model. They fail at the data loop or the evaluation, and nobody notices for a quarter.
- 01
A problem worth solving
We start at the operating cost, not the technology. If a model cannot move a number you already track, we say so before you spend anything.
- 02
A trustworthy data loop
We work with your real records, including the malformed ones. Most failed projects fail here, quietly, months before anyone admits it.
- 03
The right model
Hosted, open weight, fine tuned, or no model at all. The choice follows the measurements, and we show you the ones that decided it.
- 04
Enough compute
GPU sizing, batching, caching, and serving designed against your latency target and your budget, priced before we build.
- 05
Rigorous evaluation
A test suite for model behaviour. Quality, safety, and latency thresholds that block a deploy the same way a failing unit test does.
- 06
A product people use
Shipped into the workflow where the decision is actually made, with the interface, permissions, and audit trail that requires.
Inside a Foundry Sprint
What the first three weeks actually look like.
No discovery workshop that produces a slide deck. Three weeks of measurement that produce a decision.
Baselines and architecture
We run the real task against candidate approaches and measure them. The architecture memo that follows cites those numbers rather than industry anecdotes.
Plan, cost, recommendation
A costed build plan with milestones, an inference cost model at your expected volume, and a written go or no-go. You could hand the plan to a competitor and they could execute it.
Evaluation
What has to pass before anything ships.
These run in CI, on every change, including ours. A failing gate blocks the deploy the same way a failing unit test blocks a merge.
Quality
Task accuracy measured against a held-out set your team helped label. The threshold is agreed before the build starts, not negotiated after.
Safety
Refusal behaviour, prompt injection resistance, and data leakage checks appropriate to what the system can reach.
Latency
p50 and p95 measured at realistic concurrency, because a system that is fast when idle is not fast.
Cost
Inference cost per request, tracked against the model we quoted, so a regression shows up as a number and not a bill.
In writing
The commitments that go in the contract.
Not values on a wall. Terms you can hold us to, and the reason each one exists.
Fixed price, fixed scope
Sprints and build milestones are quoted as fixed amounts before work starts. If we sized it wrong, that is our problem, not a change order.
The sprint fee comes off the build
Pay for discovery once. If you engage us to build within 60 days, the full sprint fee is credited against the first milestone.
Your cloud, your data, your code
Systems deploy into accounts you own and control. You keep the source, the weights we train, and the evaluation suite, with no license back to us.
Evaluation before deployment, every time
Nothing reaches production without passing the quality, safety, and latency gates we agreed on. That rule applies to our own changes too.
Thirty days of stabilization, included
The month after launch is where real usage finds what testing missed. That month is part of the engagement, not a new invoice.
We will tell you not to build it
A sprint that ends in a documented no is a successful sprint. It is the cheapest possible version of that discovery.
Measurement over demonstration
Anything can be made to work once, on a good example, in front of an audience. We report the failure rate, and we report it before you ask.
Boring where it counts
The interesting part is the model. The part that determines whether this is still running in a year is the data loop, the evaluation, and the monitoring.
Your leverage, not our lock in
We would rather you outgrow us than depend on us. Everything transfers, and enablement is a service we actively sell.
Next step
Put a real problem in front of us.
Bring the workflow that is breaking. We will tell you what the data can support and what it cannot, in writing, before you commit to a build.