AI in production, operated like everything else that matters
SLOs, observability, change control, cost management, and incident response for Claude-powered systems after launch. Delivered by operators, for operators.
Scope of engagement
- Ongoing operation of Claude-powered services after initial build: SLOs, ownership, incident response.
- Observability: prompts, cache hit rate, tool-call outcomes, cost, latency, evaluation drift.
- Model and prompt change management with regression testing before every rollout.
- Cost control: caching, model routing, batching, and provider-level usage guardrails.
- Evaluation-set stewardship: keeping the benchmark honest as usage patterns evolve.
- On-call coverage aligned with your existing rota, not bolted on top.
Running AI is an operations problem first
Keeping an AI service healthy looks a lot like keeping a Cassandra or Kafka estate healthy: disciplined observability, practised incident response, and explicit ownership of drift. That is our day job. The same operational rigour we apply to data-platform estates is how we run AI services after the first engagement ends.
A predictable path from scope to running system
Onboard
Inventory the system, SLOs, dependencies, evaluation sets, and existing incident history. Establish a shared operational picture.
Instrument
Close observability gaps. Land the dashboards, alerts, and evaluation jobs that will drive ongoing operation.
Operate
Run the system: incident response, change management, cost tuning, drift detection, and routine review cadence.
Improve
Quarterly reviews that feed back into prompt, model, retrieval, and tool-layer improvements with explicit evidence.
What clients walk away with
Clear ownership
No ambiguity about who is on the hook when a Claude-powered service misbehaves. Response is practised, not improvised.
Predictable cost
AI cost that matches the business case instead of drifting with every new prompt change. Guardrails that actually hold.
Systems that improve
Evaluation-driven iteration that makes the service measurably better over time instead of silently regressing.
Common questions
How does this interact with our existing on-call?
We slot into your incident response process rather than creating a parallel one. Runbooks, rotas, and escalation paths are agreed during onboarding.
Do you take over the system or operate alongside the internal team?
Whichever the client needs. For most enterprises we operate alongside an internal team, bringing depth without removing ownership.
What tooling do you use for observability?
We work with whatever you already have. Where gaps exist, we close them with tooling that matches your stack, not a bespoke silo.
How do you keep evaluations from going stale?
Evaluation sets are reviewed on a scheduled cadence and expanded when real-world failures reveal gaps. Staleness is a known risk and we operate against it explicitly.
Start a conversation
Tell us about the system you're building or the decision you're trying to make. We'll match you with a specialist.