Enterprise RAG
Retrieval architecture and the evaluation harness that keeps it honest.
Enterprise RAGLLMOps & AI Operations
The operational discipline that keeps an AI system working after launch — when nobody is watching it closely any more.
The problem
Launch is not the finish line
A production AI system degrades in ways traditional software does not. Nothing throws an exception. The system keeps responding, and the answers slowly get worse.
Models get deprecated on the provider's schedule, not yours. Prompts drift as people edit them without regression testing. Your corpus changes underneath the retriever. Costs move as usage patterns shift.
Six months after launch, the common question is "is this still as good as it was?" — and without instrumentation, nobody can answer it. That uncertainty is what quietly kills internal trust in AI, and trust is far harder to rebuild than a system is to fix.
Scope
What we deliver
- Continuous evaluation — The evaluation harness runs against production traffic, not just in CI, so quality is measured on what users actually ask.
- Regression testing in CI — Prompt and model changes are gated by the same acceptance criteria used before launch. No silent quality changes.
- Drift detection — Alerting on shifts in input distribution, retrieval quality, and output characteristics before users report them.
- Model lifecycle management — A defined process for evaluating, staging, and promoting new model versions — and for handling deprecation on someone else's timeline.
- Observability — Request tracing through retrieval and generation, latency by stage, quality and groundedness metrics, and safety event monitoring.
- Cost attribution and control — Spend broken down by team and use case, with anomaly alerting. Most organizations cannot answer what AI costs them per workflow.
- Incident response — Runbooks for the failure modes specific to AI systems, which your existing on-call playbooks do not cover.
Detail
Failure modes we instrument for
| Failure mode | How it presents |
|---|---|
| Silent quality decay | No errors, no alerts. Answers get subtly worse as the corpus or usage pattern shifts. |
| Retrieval drift | The index falls behind the source of truth, and the system answers confidently from superseded content. |
| Prompt regression | A well-intentioned edit improves one case and breaks ten others that nobody re-tested. |
| Model deprecation | A provider retires a version and behaviour changes underneath a system nobody re-evaluated. |
| Cost drift | Usage grows or context windows expand; spend rises without anyone owning the number. |
| Guardrail erosion | Safety filters tuned for launch-era traffic stop matching how the system is actually used. |
Questions
Frequently asked
How do you evaluate a non-deterministic system?
Statistically, against a maintained test set, with thresholds rather than exact matches. Groundedness, citation accuracy, and task completion are all measurable. The point is not perfection but detecting change.
Can you operate this for us?
Yes, under a retainer with defined service levels — or we hand over to your team with runbooks and training. Both are legitimate outcomes and we will recommend the one that fits your staffing.
We already have observability. Is this different?
Yes. Your APM tells you the service responded in 800ms. It cannot tell you the answer was wrong, ungrounded, or retrieved from a document the user should not have seen.
Related
Enterprise AI Platforms
Shared infrastructure so every team is not rebuilding this.
Enterprise AI PlatformsStart with an assessment, not a proposal.
A structured evaluation of your data, infrastructure, security constraints, and candidate use cases — delivered as a prioritized roadmap you own.
Direct response from an engineer. Typically within one business day.