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AI Operations (LLMOps)

Launching an AI system is the easy half. The other half is everything after: models change, documents change, costs drift, and accuracy quietly decays. AI Operations is the discipline that catches it — evaluation, monitoring, and cost governance, run as an ongoing practice. It’s how one of our production systems cut token consumption by 90% without losing accuracy.

What You Get

AI systems that stay accurate — and stay affordable.

Accuracy, continuously scored

Evaluation suites run on every model, prompt, or data change. Regressions are caught in a report, not by your users.

Costs governed, not discovered

Token and model spend tracked per use case, with the caching and routing levers pulled before the bill surprises you.

Drift detected early

Input and answer-quality monitoring flags when the world moves — new document types, new question patterns, a provider’s silent model update.

Changes shipped safely

A release discipline for AI: staged rollouts, regression gates, and rollback — the practices software learned the hard way, applied to models.

Scope

What’s included.

  • Evaluation harness — golden datasets, automated scoring, regression reports
  • Cost observability — spend per query, per use case, per model, with budgets and alerts
  • Optimization passes — caching, context compaction, and model routing reviews
  • Quality monitoring — answer sampling, user-feedback loops, drift alerts
  • Change management — model upgrades and prompt changes, gated by evaluation
  • Monthly operations report — accuracy, cost, and incidents in one page

At a Glance

  • Format: Ongoing practice, monthly rhythm
  • Headline result: 90% token reduction on a production agent
  • Applies to: Systems we built, or yours
  • Starts with: An operations review of your current system
  • Reporting: One-page monthly scorecard
FAQ

Questions about this service.

Our AI system was built by someone else. Can you operate it?
Yes — the operations review maps its architecture, instrumentation gaps, and cost profile first. Most systems need modest instrumentation before they can be operated properly; that’s part of the review’s output.
What does the 90% token reduction actually involve?
Mostly unglamorous engineering: caching repeated context, compacting conversation history instead of resending it, routing simple questions to cheaper models, and cutting retrieved content to what the answer uses. The evaluation suite proves accuracy held while spend fell.
How do evaluations work without labeled data?
We build the golden set from your real traffic and your team’s judgments — a few hundred questions with expected answers is enough to start, and the set grows with every incident.
Is this a tool or a service?
A service, run on tooling inside your environment. You own the dashboards, the evaluation sets, and the reports — if we part ways, the discipline stays with you.
Related

Often paired with.

AI Agents & Assistants

The systems this practice keeps healthy.

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Cloud Cost Optimization

The same discipline applied to your whole cloud bill.

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Data Governance & Privacy

Audit trails and access control for AI in regulated work.

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Start with a fit call.

Thirty minutes. Bring the problem as it actually is — the stalled pilot, the bill, the spreadsheet everyone quietly maintains. We’ll tell you honestly whether we’re the right builder for it.

You leave with a straight answer on fit — and if it fits, a free Blueprint to follow.

Request a fit call How Proof-First Build works