Overview
We build LLM-powered products, retrieval systems, and intelligent automation with the engineering rigor AI demos usually skip: evaluation suites, guardrails, cost controls, and honest accuracy reporting.
Every executive has seen the impressive AI demo. Far fewer have seen one running in production a year later — because the distance between a prototype and a dependable system is evaluation infrastructure, failure handling, and cost discipline that most teams discover too late.
We build AI products the way we build any other production system: with measurable acceptance criteria. Before scaling an AI feature, we build the evaluation harness that proves it works on your data, define what happens when the model is wrong, and model the unit economics at your real volume. Then we ship.
82%
of support tickets auto-resolved for our largest deployed assistant
12x
document processing throughput gain in insurance claims intake
<2%
hallucination rate maintained across production RAG deployments
Capabilities
What our ai practice covers
Every engagement is scoped from these building blocks — mixed to fit your product, not a package.
LLM application development
Copilots, document intelligence, and conversational products built on Claude, GPT, and open-weight models — with structured outputs and graceful failure modes.
Retrieval-augmented generation (RAG)
Search and answer systems grounded in your documents, with citation, freshness pipelines, and hallucination rates we measure and publish to you.
AI agents & workflow automation
Multi-step agents that handle claims triage, order exceptions, and back-office workflows — with human review gates where the stakes demand them.
Evaluation & guardrails engineering
Golden datasets, regression suites, and safety filters so model or prompt changes ship with the same confidence as code changes.
Model integration & orchestration
Routing across providers for cost and capability, fallback chains, caching, and observability across every token spent.
AI readiness & data preparation
Honest assessments of where AI will and won't pay off in your operation, plus the data cleanup work that determines success.
Engagement
How this engagement runs
Four phases with named deliverables — you'll know exactly where the work stands every week.
01
Use-case qualification
We score candidate use cases on value, risk, and data readiness — and tell you plainly which ones aren't worth building yet.
02
Proof with your data
A working prototype evaluated against a golden dataset from your real documents and edge cases — not cherry-picked demos.
03
Production hardening
Guardrails, cost controls, human-in-the-loop workflows, and monitoring before real users touch the system.
04
Measure & expand
Accuracy and ROI dashboards reviewed monthly; successful patterns extended to adjacent workflows.
FAQ
AI questions, answered plainly
Ready to talk ai?
Bring the problem — a technical lead will sketch the approach, the team shape, and an honest budget range on the first call.