AI & Agents
AI engineering,
measured not vibed.
Knowledge systems, evaluation harnesses, and cost engineering on Anthropic's Claude. Not slideware — the agent answering questions on this site is one of ours.
The engineering under the demo.
Knowledge systems (RAG)
Assistants grounded in your documents, policies, and product data — answering with citations rather than confident invention.
- Chunking and retrieval tuned to your corpus
- Answers carry sources
- Refuses when the corpus does not know
Evaluation & regression testing
A test suite for behaviour. Every prompt or model change is measured against your real scenarios before it ships.
- Scenario sets from your own data
- Scored automatically in CI
- Catches quality regressions before users do
Model selection & routing
Not every request needs the largest model. We route by task so quality holds where it matters and cost falls where it does not.
- Task-appropriate model tiers
- Escalation on low confidence
- Portable across providers by design
Cost & latency engineering
Prompt caching, context trimming, and streaming so the experience feels immediate and the invoice stays predictable.
- Prompt caching for stable context
- Context budgets per interaction
- Cost per interaction measured, not estimated
Systems integration
Claude wired into the tools work actually happens in — CRM, ticketing, data warehouse, internal APIs.
- Typed tool interfaces
- Least-privilege service credentials
- Failure paths designed, not discovered
Safety & governance
Guardrails, approval gates, and audit trails so AI acts inside limits your risk function has actually signed off.
- Human approval for consequential actions
- Every decision logged
- Data residency and retention respected
Small bets first. Then scale what measures well.
Discover
- Find the use case with real volume behind it
- Map the data, systems, and constraints
Prototype
- A working system on your data, quickly
- Evaluated on your scenarios, not a demo script
Productionize
- Guardrails, monitoring, and cost controls
- Evals wired into the deployment path
Operate
- Watch quality and spend in production
- Tune, then expand to the next use case
A demo is easy. Keeping it good is the job.
Almost anyone can produce an impressive first demo now. The difficulty arrives later: the prompt that quietly regresses, the retrieval that degrades as the corpus grows, the bill that triples when usage does.
That is why we treat evals, cost budgets, and monitoring as part of the build rather than a later phase that never gets funded.
See agent engineering- The eval suiteYour scenarios, scored — so quality is a number, not an impression.
- Cost instrumentationPer-interaction spend visible from the first day in production.
- Prompt and tool sourceIn your repository, versioned, with the reasoning documented.
- A runbookWhat to do when quality drops, written for whoever is on call.
It is the model family we have gone deepest on for agentic work — strong reasoning, reliable tool use, and a safety posture that survives enterprise review. We design systems to be model-portable, so the depth is a choice rather than a lock-in.
Have a use case in mind?
Tell us what you want AI to do. We'll scope a prototype in 48 hours — and show you the unit economics before you commit to anything.