From metrics to maintainability
LLMOps & Observability
Monitoring, evals, cost control, and reliability tooling for AI systems in production.
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
- Define KPIs (cost/latency/quality/risk)
- Instrument the pipeline (events, traces, budgets)
- Add eval loops and regression gates
- Operationalize: dashboards, alerts, playbooks
- Iterate and harden with real traffic signals
Related Use Cases