LLMOps Consulting
Turn prompts, evals, traces, and releases into an operating system — so ChatGPT Enterprise and Copilot prototypes can become production you can run.
- Service
- LLM Platform
- Industry
- Enterprise
- Updated
- 2026-08-25
- Engagement
- 4 wks
LLMOps consulting installs an operating system for production LLMs in your cloud: versioned prompts and context, golden-set evals in CI, tracing and AI observability, cost routing, and release runbooks. Custom GPTs and Microsoft Copilot remain prototypes; write-actions and evals run here. Typical install is a four-week standard. You own prompts, evals, and traces; you pay the model provider with no markup; SSO/IdP stays in your stack.
Why teams pick this engagement
LLM Platform × EnterpriseChanges cannot skip evals
Prompt, tool, or model swaps fail the build if golden-set scores drop. LLMOps without a gate is a wiki of prompts.
Cost and quality on one board
Token spend, cache hit rate, eval scores, and latency by route — so LLMOps is not “we added LangSmith” with no owner.
Model-agnostic by design
Routing and evals sit in your cloud. OpenAI, Anthropic, Google, Azure, or open weights are providers, not the platform.
Engineers own the loop
We pair with your platform team. After week 4 they merge prompt PRs, triage eval failures, and page on drift — we do not keep the pager as a product.
Four-week standard
Week 1 inventory of prompts and shadow GPTs, week 2 repo and tracing, week 3 evals in CI, week 4 runbooks and 30 days on-call.
Reconstructable releases
Every production answer can name prompt version, retrieval set, model, tools called, and eval batch. That is AI observability, not a dashboard screenshot.
Key takeaways
- 01
LLMOps is prompts + evals + traces + releases with owners — not a tracing SaaS login and a Slack channel named #gpt.
- 02
Custom GPTs are prototypes; Copilot is often the wrong tool when write-actions and evals are required. LLMOps is the promotion path.
- 03
If a prompt can change in production without a golden-set gate, you do not have LLMOps.
- 04
You own the eval suite and traces; inference is billed by the provider to you; we are model-agnostic.
- 05
Four weeks is enough to stand up the control plane on one production path; fleet expansion reuses it.
What the engagement covers
How we work
- 01
Discover
Week 1: inventory of prompts, GPTs, Copilot Studio topics, shadow scripts, current evals (usually none), identity and secret handling.
- 02
Design
Repo layout, eval slices, trace schema, router policy, and the promotion rule out of Custom GPTs and Copilot.
- 03
Build
Week 2–3: versioned prompts, tracing in your cloud, first golden set, CI gate on one production path.
- 04
Validate
Regression on the golden set, canary of a prompt change, incident tabletop, PII redaction check on traces.
- 05
Enable
Week 4: runbooks, owners, 30 days on-call. Your platform team merges prompt PRs; we do not host LLMOps as SaaS.
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.
LLMOps Minimum Viable Control Plane
The repo layout, eval gate, trace fields, and on-call checklist we install in four weeks — independent of vendor.
Get the control plane ·Prompt Change CI Gate Template
GitHub Action / Azure DevOps pipeline sketch that blocks merges when golden-set scores regress.
Get the template ·