We build systems that finish work: answers with citations, document pipelines that kill re-keying, agents that act only inside permissions and human approval. The control plane is Agent Cloud, because enterprises do not get to shrug 'the model did it.'
A flashy demo and a production system diverge on boring questions: who is this agent, what can it touch, who approved the action, what happened at 3:41 AM when money moved? We start from those questions. That is why the work tends to survive security review.
Most of the money is in dull work - reading documents, reconciling records, answering the same questions with the right citation, routing exceptions to the one person who should see them. Automate that majority and people keep the judgment calls.
We pick models by the job. Frontier APIs when quality demands it; smaller or self-hosted when residency or unit cost wins. Retrieval, evals, guardrails, and audit make the whole thing dependable. The model is a component.
Sprint, then phase, then 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
Governed agents, in production
5 weeks
from build to security approval
For the first agent deployed under Agent Cloud governance.
The two agents built before it, without governance, never got through review at all. That is the difference the control plane makes.
94%
of agent actions auto-approved under policy
The remaining 6% routed to a named human for approval.
0
actions executed without a policy decision
Every action is evaluated before it happens, and every one leaves an immutable audit line.
61%
lower model spend after routing
Cheaper models handle the work that does not need a frontier model, with no measurable quality change across evaluation sets.
Spend caps have been hit four times. Every one was a runaway loop, killed automatically before it became an invoice.
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.