Start with one workflow. Expand when it works.
We keep the first engagement bounded. The goal is to prove value in a real workflow, not to launch an endless transformation program.
Two bounded entry points, depending on what is blocking you.
Both are paid, both are short, and both end with a decision you can defend.
Agent Reliability Audit
Two weeksA live or limited-release AI system that cannot be confidently measured, approved or scaled.
- Task success measured end to end on your real traffic
- Failure modes identified and ranked by operational cost
- Human review load, permissions, latency and cost reviewed together
- A regression baseline and a suite that runs in CI
- Remediation priorities, ordered by what would change the rollout decision
Operational AI Diagnostic
One to two weeksA business-critical workflow running at volume with measurable labor, delay, rework or capacity cost.
- One workflow mapped end to end with the people who run it
- The current economics quantified, in your numbers
- The boundary between AI, deterministic rules and human judgment defined
- Integration requirements and exception paths specified
- Target metrics and a fixed implementation scope you can approve or decline
Engineering work is scoped from evidence, not assumed in advance.
If the entry engagement shows there is nothing worth building, the document says so and there is no proposal attached to it.
Reliability Engineering
Typically four to ten weeksA measured system that now needs the failure modes engineered out before it can widen.
- Remediation of ranked failure modes
- Eval harness and release gates in CI
- Guardrails, permissions and approval boundaries
- Review-load reduction with the evidence to justify it
Operational AI Deployment
Typically four to twelve weeksA defined workflow that needs to be built, integrated, evaluated and launched.
- Workflow build and system integration
- Exception handling and human approval layer
- Operational dashboards and alerting
- Bounded rollout, then expansion
AI Product Engineering
Project or monthlyAn AI feature in your product that has to survive real customers.
- Production backend, state and data model
- Evaluation before and after launch
- Latency, cost and failure handling
- Instrumentation for adoption, not just uptime
Forward-Deployed Pod
Monthly, with a three month horizonAn ongoing AI roadmap that needs embedded senior execution without building the whole capability in-house.
- Named engineers embedded with your team
- Ownership of a workflow portfolio
- Continuous evaluation and improvement
- Knowledge transfer as an explicit deliverable
How we price, stated plainly.
Entry engagements are paid and fixed
Diagnostics and audits are quoted as a single number for a defined scope, agreed before any work starts.
Engineering is scoped, not hourly
Deployment work is priced against an agreed scope. We do not sell hours, and we do not sell seats.
No free pilots
The first engagement is bounded and paid, because both sides need to prioritize it. If budget is the constraint, we make the scope smaller rather than the work cheaper.
Scoped after a short discovery
Every engagement depends on the integrations, the workflow risk, how ready the data is, and who owns what. We scope against your actual situation rather than against a generic template.
Payment terms, milestones and everything else commercial are set out in the engagement letter for your scope.
The things people ask before the first call.
One owner of the workflow who can make decisions, access to the systems involved at the level the scope requires, and real cases or a redacted sample that behaves like real ones. The access model is part of the scope, agreed before kickoff.
Tell us the AI system or operational workflow that is blocking scale.
We will tell you what we would measure first, and whether it is worth deploying.
Discuss a Production Problem