We build AI that does jobs: retrieval-grounded assistants that cite sources, document pipelines that read what people used to re-key, and agents that run multi-step processes under policy, human approval, and immutable audit. Agentic, not chatbot. Every deployment ships with governance, because enterprises don't get to say 'the model did it.'
The gap between a flashy AI demo and a production system is governance: who is this agent, what can it touch, who approved the action, and what happened at 3:41 AM when the numbers moved? We build from those questions backwards - which is why the work survives security review.
Most enterprise AI value is unglamorous: reading documents, reconciling records, answering the same questions with the right citation, routing exceptions to the one person who should see them. We automate the repetitive majority so people get the judgment work.
We're model-pragmatists. Frontier APIs where quality needs it; smaller or self-hosted models where residency or unit economics demand that. Retrieval, evaluation, guardrails, and audit make it dependable. The model is a component, not the strategy.
Sprint → Phase → Retainer
Assessment first, always. Scope and investment are bespoke to your estate - book a demo and we'll walk the plan together.
AI READINESS ASSESSMENT
Use-case portfolio, data-readiness verdicts, governance baseline, first builds specified.
- → Scored use-case portfolio
- → Data readiness report
- → Governance baseline
- → 90-day plan
FIRST AGENT PHASE
One governed agent or RAG system in production, with evaluation and audit from day one.
- → Production deployment
- → Evaluation harness
- → Runbook & guardrail config
- → Handover training
AUTOMATION RETAINER
A standing automation squad expanding coverage, tuning models, and holding the eval line.
- → Dedicated squad
- → Monthly eval & drift reports
- → Quarterly portfolio review
AI & Automation, answered
Agents or chatbots - what's the difference in practice?
A chatbot answers; an agent acts. Our agents run multi-step processes - look up, decide, write back, escalate - under explicit permissions and policy, with human approval where consequences warrant it, and every action logged. The chat UI, when there is one, is just the front door.
How do you prevent hallucinations in production?
Grounding, evaluation, and honesty. Answers must cite retrieved sources; faithfulness is measured against a regression suite; the system is designed to say 'not found' rather than improvise. For actions, policy checks run before execution - an agent can't invent its way past an RBAC rule.
Which models do you use?
Whatever the requirement earns. Frontier hosted models where quality and speed matter most; self-hosted or smaller models where residency, latency, or unit economics dominate. Architecture keeps you portable - we've swapped models under running systems without users noticing.
What does the first production agent cost?
Scoped after we understand your estate - anything else is a guess dressed as a quote. Book a demo; investment is bespoke. The diagnostic gives you a starting path against your actual stack.
Start with the sprint.
Fixed fee, few weeks, and you end up with a plan you could execute without us. Most clients don't - but the leverage is yours either way.