AI Observability Consulting
See every production LLM decision — traces, cost, eval drift, and reconstructable logs — in your cloud, not a screenshot from a Copilot admin center.
- Service
- LLM Platform
- Industry
- Enterprise
- Updated
- 2026-08-25
- Engagement
- 4 wks
AI observability consulting installs traces, cost accounting, eval-linked drift alerts, and reconstructable decision logs for production LLMs in your cloud — the measurement half of LLMOps. ChatGPT Enterprise analytics and Microsoft Copilot usage reports are not observability; Custom GPTs are prototypes and cannot emit this telemetry. A four-week standard covers one production path. You own traces and evals; you pay the model provider with no markup; SSO/IdP stays in your stack.
Why teams pick this engagement
LLM Platform × EnterprisePII-safe traces
Logs are redacted and access-controlled via your IdP. Observability that stores raw customer text in a vendor SaaS is a second incident waiting.
Quality and cost on one timeline
Eval slices, token spend, latency, and tool-error rate share a dashboard. AI observability that is only latency is APM with extra branding.
Vendor-neutral telemetry
OpenTelemetry-style traces in your cloud. Model-agnostic: OpenAI, Anthropic, Azure, Google, or open weights all emit the same fields.
On-call that engineers accept
Pages on groundedness drops, tool-failure spikes, and cost anomalies — not on every token. Your platform team owns the rotation after week 4.
Four-week standard
Week 1 instrumentation gaps, week 2 trace schema and redaction, week 3 eval-linked dashboards and alerts, week 4 runbooks and handover.
Decision logs you can export
For a given user and time: prompt version, context IDs, tools, model, output, eval scores. Custom GPTs and Copilot cannot give you that log. Production agents must.
Key takeaways
- 01
AI observability means reconstructable traces (prompt, context, tools, model, user, cost, evals) — not a Copilot usage chart.
- 02
If you cannot replay why an agent wrote a record, you are not ready for write-actions. Copilot is often the wrong tool for that job.
- 03
Custom GPTs are prototypes; they do not give you CI evals or exportable decision logs.
- 04
Traces must be redacted and gated by your IdP or you have built a PII warehouse labeled “LLMOps.”
- 05
Four weeks stands up the control plane on one path you own; fleet expansion reuses the schema.
What the engagement covers
How we work
- 01
Discover
Week 1: current logs (usually prompt dumps or nothing), Copilot/ChatGPT gaps, PII risk, which production path to instrument first.
- 02
Design
Trace schema, redaction, eval slices, alert policy, decision-log fields, IdP groups for access.
- 03
Build
Week 2–3: instrumentation in your cloud, dashboards, golden-set job, redaction tests.
- 04
Validate
Replay a known-bad answer, prove PII is stripped, fire a drift alert in staging, confirm rollback from a trace.
- 05
Enable
Week 4: runbooks, on-call, 30 days support. You own telemetry; we do not host your traces as a product.
Take the playbook with you
The working documents from real engagements — free, in exchange for an email. They’re useful whether or not we ever talk.
LLM Trace Schema and Redaction Spec
Required fields (prompt version, retrieval IDs, tool calls, cost, user from IdP) and what must never be stored raw.
Get the spec ·Drift Alert and On-Call Runbook
What to page on, how to reconstruct a bad answer, and when to roll back a prompt or model.
Get the runbook ·