AI Modernization
Plenty of teams have proved AI can do something useful. Fewer have it running where the work is, with tests that prove it still works tomorrow. That gap is the job.
What we build
Agents, automation, retrieval and evaluation are not separate projects. Built apart they produce a demo; built together they produce a system people can rely on.
Single agents where one is enough, multi agent systems where the work genuinely splits. Planning, tool use, retries and fallbacks — built so a failed step is visible and recoverable, not silent.
The unglamorous middle of the business: intake, routing, approvals, reconciliation, reporting. We automate the path end to end and leave a clear seam where a person still needs to decide.
Retrieval augmented generation that is grounded in your content and honest about what it does not know. Built around the corpus you actually have, including the messy parts.
The part most pilots skip. Before anything is depended on, there is a test set, a score and a threshold — so a change to a prompt, a model or a tool is a measurable event rather than a gamble.
How an engagement runs
Every phase ends in something you can judge. Nothing depends on a long build finishing before anyone sees whether it works.
We follow the actual process, not the documented one — where the time goes, where the exceptions are, and which steps genuinely need judgement. The output is a shortlist of what is worth automating and what is not.
A narrow slice, built against your real inputs and measured against a test set drawn from real cases. If the numbers are not there, you find out in weeks and at small cost.
The proven slice becomes a production path: integrated with your systems, with error handling, retries, audit trail and the human review steps the process needs.
Evaluations wired into CI, tracing and cost controls in place, security and data handling reviewed, failure modes given somewhere to go. This is what makes it dependable rather than impressive.
We operate it, watch the scores, and take on the next slice. Or we hand it over with the tests, the runbooks and the context your team needs to own it.
Where we usually start
Work moving between systems by hand: rekeying, chasing approvals, assembling the same report every week.
Something that demoed well and then stalled on reliability, security review, cost or the absence of any way to tell whether it works.
High volume, repetitive triage and extraction where the rules are real but have never been written down.
A system that still runs the business but can no longer be changed safely — modernized in slices rather than rewritten in one jump.
AI work is easy to make look good and hard to make dependable. These are the rules we hold to, on every engagement.
Send us the process you would most like to stop doing by hand. We will tell you honestly whether it is worth automating — and what it would take.