AI that survives contact with production.
Most AI projects stall between the prototype that impressed the room and the system a business can depend on. We build the second one.
Retrieval-augmented systems
Search and answer layers over your own contracts, tickets, manuals and records: hybrid retrieval, versioned sources, and a citation on every response.
Agentic workflows
Multi-step automations that read, decide and act inside your existing systems, with defined stopping points where a person approves anything consequential.
Extraction and classification
Structured data pulled out of PDFs, scans and email, validated against your business rules and routed to the right queue instead of a person's inbox.
Evals and guardrails
Golden datasets, regression suites and eval gates wired into CI, so a prompt change or model upgrade is a measured release rather than a Monday surprise.
Pipelines, privacy and residency
Ingestion, chunking, redaction, retention and residency handled up front, so security review is a checkpoint you pass rather than a wall you hit.
Model strategy and cost
Routing between frontier and smaller models, caching, and fine-tuning where it pays, chosen against a measured cost per request, not a preference.
How an AI engagement runs
A fixed-scope discovery first, so the decision to build is made on evidence rather than enthusiasm.
Discovery
Two to three weeks. We frame the problem, inspect the data and build a working proof of the part most likely to fail.
Evaluation
A golden set and a scoring harness before the build, so quality has a number attached from the beginning.
Build
Weekly releases into an environment you use, with the eval suite running on every change.
Operate
Monitoring, cost tracking and a retraining cadence, handed to your team with the documentation to run it.