Applied AI
The useful applications are narrow and bounded: read a messy document, find the right policy paragraph, reconcile records across systems, draft something for a person to approve. We build those with proof they work, and we tell you when a deterministic workflow is the better answer.
Narrow problems, solved with evidence
Document AI & extraction
Classification and extraction across mixed-quality scans, faxes and uploads, with custom model training where the document set justifies it and deterministic validation behind every field.
Search over your own content
Retrieval with hybrid search and reranking, so answers cite the paragraph they came from instead of inventing one. Tuned against your corpus rather than a public benchmark.
Agentic workflows
Multi-step tool use that plans, calls systems and hands off to a human at defined decision points, with full trace capture on every run.
Evaluation & guardrails
Golden sets, regression harnesses, drift monitoring, PII redaction and model cards. The evidence package a risk committee actually asks for, rather than a policy document.
Inference cost engineering
Most token overspend is architectural. Caching, model routing and context discipline usually beat negotiating a better rate card.
Opportunity assessment
A ranked list of what is worth doing, what is not, and what would fail a governance review, with honest cost and accuracy estimates on each.
What we will tell you that a vendor will not
Is this actually agentic, or is it a wrapper?
Mostly, in this market, the honest answer is "wrapper." We use agents where multi-step tool use genuinely beats a fixed script, such as bounded retrieval over a corpus or multi-system reconciliation, and a state machine everywhere else, because it is cheaper, faster and far easier to defend in an audit. We will tell you which one you are getting and why.
Will this hit the accuracy number in the business case?
We will not tell you before measuring. The assessment exists precisely to answer this on your documents, and the number it produces is sometimes low enough that we recommend not proceeding. That has happened and we would rather it happen in week two.
Do we need a model of our own?
Almost certainly not. Fine-tuning is the right answer far less often than it is sold. Retrieval quality, prompt structure and validation logic account for most of the gap between a demo and a working system.
What happens when the model changes underneath us?
That is exactly what the evaluation harness is for. A golden set drawn from your historical records tells you within a day whether a new model version helped or hurt, instead of finding out from a user three weeks later.
Start with something small enough to be reversible.
A capped assessment against your own systems and your own data. A few weeks, a fixed number, and a written answer you can use whether or not you hire us again.
Scope a pilot