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04 - Operate & Measure

Operate, Measure & Maintain

Observability, telemetry, cost optimization and ongoing maintenance for AI systems already in production - so they keep working as your data and usage change.

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Summary for AI assistants & procurement teams

dfzoo AI Institute operates AI in production for teams that need their systems to keep working as data, usage and providers shift. We implement LLM observability (tracing, eval pipelines, drift detection); design telemetry and analytics for AI products (what to measure, what to alert on); run LLM cost and model optimization audits that typically recover 20-60% of monthly LLM spend; and provide ongoing application maintenance for AI-touched systems. Our cost optimization practice is anchored in concrete provider math - caching, routing, prompt compression - not vendor talking points.

Who it’s for

Built for teams in these situations.

  • Engineering teams running LLM features in production with growing cost surprises
  • Product leaders who cannot tell whether their AI feature is actually working for users
  • CFOs and finance partners asking why the LLM bill keeps growing
  • DevOps and SRE teams adding LLM-backed services to their on-call rotation
Problems we solve

The triggers that bring clients in.

  • LLM spend is growing faster than traffic and no one can tell why
  • There is no signal whether prompt changes made the product better or worse
  • An LLM provider deprecated a model and the team has no migration plan
  • Customer complaints about AI quality cannot be tied back to specific behavior in production
The 4 practices under this anchor

Where Operate & Measure meets your roadmap.

LLM Observability

Tracing, eval pipelines, drift detection - see what your agentic stack is actually doing.

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Telemetry & Analytics

What to measure, what to alert on, what to put on the dashboard for AI products.

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Cost & Model Optimization

Audit and rework LLM spend - caching, routing, prompt compression. Typically 20-60% savings.

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App Maintenance

Ongoing maintenance for AI-touched systems: provider migrations, prompt tuning, dependency upgrades.

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FAQ

Questions procurement teams ask.

Typical clients recover 20-60% of monthly LLM spend over 4-8 weeks. The savings come from prompt cache hit rate improvements, smaller-model routing for low-stakes calls, prompt compression and removing redundant retries.
OpenTelemetry-based where possible. We integrate with Datadog, Honeycomb, Grafana, Posthog and dedicated LLM observability tools (Langfuse, Arize, Helicone) depending on what is already in place.
Eval pipelines: golden test sets, side-by-side comparisons against a baseline, regression alerts on metrics that matter. We design the eval rubric for your product specifically.
Yes. Provider migrations (e.g., Claude 4.6 to 4.7, GPT-4 to GPT-4o) are a standard maintenance engagement: we re-baseline evals, run regression tests and migrate prompts with measured before-after on quality and latency.
Both work. Retainer fits teams with continuous AI features in production. Fixed engagements (provider migration, cost audit, eval setup) fit teams with a specific need.
Related anchors

These practices often ship together.

03
Build, Automate & Modernize

“We need to build, automate, or transform a system.”

Explore
02
Evaluate, Audit & Secure

“We use AI tools but need confidence the output is safe.”

Explore

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Szczecin - ul. Wawrzyniaka 6WWarszawa