LLMOps & Observability

What we deliver

Shipping AI is easy. Keeping it reliable, measurable, and cost-controlled is the hard part. We build the operational layer that makes AI systems production-ready.

• Token/cost tracking per request, per user, per workflow
• Quality evaluation (golden sets, regression tests, judge scoring)
• Latency and error monitoring with actionable dashboards
• Drift and abuse detection (input patterns, tool-call risk, failure spikes)
• Incident playbooks, alerts, and audit-friendly logging (with redaction)

Typical engagements

• LLM cost instrumentation and budget policies (routing, caching, guardrails)
• Evaluation harnesses and release gates for prompt/model changes
• Production monitoring with SLOs (latency, success rate, quality)
• Failure analysis: timeouts, provider errors, schema breaks, hallucination hotspots

How we work

1. Define KPIs (cost/latency/quality/risk)
2. Instrument the pipeline (events, traces, budgets)
3. Add eval loops and regression gates
4. Operationalize: dashboards, alerts, playbooks
5. Iterate and harden with real traffic signals

Triggers and delivery model

LLMOps becomes urgent when teams cannot explain a cost spike, a quality regression, or a provider failure. It is also the right layer when a system works in a test environment but has no owner, SLO, or incident path in production. We start by defining the questions operators must answer, then instrument the smallest complete path: request, retrieval, model call, validation, human outcome, and downstream action.

The delivery model is incremental. A baseline dashboard and representative eval set come first. Next, traces and cost dimensions are made consistent, followed by release gates and alert thresholds. Runbooks describe what to inspect, when to retry, when to roll back, and when to escalate to a human or provider. Typical deliverables are an event schema, dashboard, eval suite, budget policy, alert catalogue, incident playbook, and handover documentation. See the evaluation harness use case and engineering service.

Dependencies and acceptance

Useful instrumentation needs stable request identifiers, access to relevant provider metadata, and a decision about what may be retained or redacted. Observability does not make an undefined quality target measurable; a team must agree which failures matter. Acceptance covers trace completeness, cost attribution, alert usefulness, regression behaviour, and recovery steps exercised against representative failures. Scope a production reliability review.

Integration notes

Instrumentation should be introduced at the same boundaries as the system’s contracts. A single request identifier can join application logs, retrieval events, provider calls, validation results, and human outcomes without duplicating sensitive payloads. The result is a usable operational record rather than a second application to maintain. New signals are added when they answer a concrete question or support a decision.