AI · Deep dive 04
Evaluations, Guardrails & Observability
Every prompt tested, every output traced, every cost tracked. We set up eval suites, guardrails and dashboards so you can ship AI features you can defend — to users, to finance, and to legal.
The scope
The production-rigour layer for AI: evaluation harnesses, prompt versioning, guardrails for content + PII, cost + latency dashboards. Often retrofitted onto existing AI features that shipped without them.
Does this sound familiar?
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The AI feature has been degrading for weeks and nobody noticed until users complained.
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OpenAI changed their model last month and your prompts broke. You found out from a support ticket.
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Monthly AI costs are up 40% and nobody knows which feature is responsible.
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Legal asked for an audit of your AI outputs and you don't have one to give.
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PII is being passed to external models and nobody's sure how much.
The customer payoff
The payoff
What you feel once it’s running.
Eval suites that catch prompt degradation before users do.
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Cost breakdown per feature — you know where the money goes.
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PII + content guardrails tested + documented.
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Audit trail — every prompt, output, and cost logged.
Phases
⏱ 3–6 weeks typicalHow Evaluations, Guardrails & Observability actually runs.
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01
Inventory
List every AI feature, every prompt, every model call in the product. Often the map itself is half the value."
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02
Instrument
Add tracing (Langfuse / Helicone / custom), cost logging, and basic eval suite for each feature."
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03
Guardrails
PII scrubbing, content filters, confidence thresholds, token budgets. Per feature, not blanket."
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04
Dashboards
Cost, latency, quality, guardrail-trigger counts. Visible to the team weekly."
The hand-off
In the handover
What lands in your hands — every artefact, nothing hidden.
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AI observability stack (tracing + logs + dashboards)
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Prompt versioning + regression test suite in CI
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Guardrails documented + tested
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Cost breakdown dashboard
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Incident runbook (what to do when eval fails)
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Audit log + retention policy
Straight questions
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Q·01 We're early — is this overkill?
Depends on scale. If your AI feature handles 100 calls/day from friendly beta users, maybe skip. If it's handling customer-facing traffic, the guardrails are already overdue."
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Q·02 What tools do you use?
Langfuse or Helicone for tracing (open source + hosted both fine). Bring-your-own Langsmith if you prefer. Custom dashboards in Metabase or Grafana."
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Q·03 What's your take on prompt regression testing?
Essential. We save golden examples + expected output shape, run them on every prompt change, fail the build if quality drops. Same as unit tests for deterministic code."
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Q·04 PII redaction — pre or post call?
Pre-call, always. Either via Presidio-style scrubbing or by routing to a model with data residency guarantees. Post- call redaction is too late."
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Q·05 How much does instrumentation slow things down?
Negligible at the scale most products operate. Tracing adds single-digit ms; guardrails under 100ms typical."
Ready to start
Ship AI you can defend.
Three-week engagement to retrofit production rigour onto AI features. Start with the one that scares legal the most.
Start a rigour engagementThe wider map
Every service page at a glance.
Each link below opens a dedicated page on that specific piece of one of our four service pillars. Jump sideways — different service, same way of working.
Digital Product Strategy
Service overview →Web & Mobile Development
Service overview →Business Automation
Service overview →AI Integration
Service overview →- 01 AI Opportunity Mapping
- 02 AI-Driven Product Features
- 03 AI-Powered Automation
- 04 Evaluations, Guardrails & Observability — you’re here
- 05 Vendor-Neutral Integration