Operate
Keep AI working.
Own and continuously improve AI systems after they reach production. Launch is the start of an AI system’s life, not the end of the project. Processes change, data drifts, models get replaced, and costs move. Operate keeps the system accurate, observable, and worth what it costs, with named people accountable for it.
Capabilities
Five capabilities. One team across all of them.
Managed AI operations
We take operational ownership of the system: monitoring, incident response, releases, and a regular review of what it delivered. You get named owners and agreed service levels rather than a support inbox.
Agent operations & maintenance
Agents depend on processes, tools, and data that keep changing. We update prompts, tools, and knowledge as they do, and retire behaviors that no longer match how the business works.
Deep diveAgent OperationsAI observability & evaluation
Every request traced from input to action, with quality scored continuously against your evaluation sets. When something degrades, you see where and why before your users report it.
Model & infrastructure operations
We test new models against your evaluations, roll them out behind flags, and roll back if the numbers slip. Infrastructure is patched, scaled, and kept inside your security boundary.
Performance & cost optimization
We route each task to the smallest model that does it well, cache what can be cached, and cut wasted tokens. Cost is reported next to the outcome it bought.
What you get
Things you can use. Not a slide deck.
Named ownership
Engineers accountable for the system, with agreed response times.
Live quality dashboard
Accuracy, latency, cost, and business outcome, in one place.
Model upgrade path
New models evaluated and adopted on evidence, not announcements.
Monthly review
What the system did, what changed, and what to improve next.
Proof
Operate in practice. Shipped and running.
- HealthcareA multi-year engagement keeping a scheduling platform at 99.9% uptime across 100+ clinics.99.9% uptime
- Private EquityAn ongoing engagement where each new acquisition joins the same knowledge layer, with no rebuild per deal.0 rebuild per deal
- HealthcareAn ongoing engagement keeping the reimbursement engine current as payer policy changes.Real-time scoring
The rest of the lifecycle
Related pillars
- 01AdviseKnow where to apply AI.Find where emerging AI capabilities can create measurable business value, and build the roadmap to get there.Explore Advise
- 02EngineerTurn possibility into production.Build the applications, agents, integrations, and data infrastructure that put AI inside real business workflows.Explore Engineer
- Across all threeGovernMake AI safe to scale.The controls required to deploy AI across the organization with confidence.Explore Govern
Go deeper
Put someone accountable on it.
Whether we built it or someone else did, we can take ownership of an AI system in production and keep it improving.