Quality is defined as a feeling
Stakeholders say responses look good, but there is no shared rubric for correctness, groundedness, task completion, or acceptable failure.
AI Evaluation & Observability
Know why an AI system is ready to release—and when its quality starts to change
Measure groundedness, task completion, tool use, safety, latency, and cost before release, then trace and monitor quality in production.
- Best suited for
- Teams preparing an AI workflow for release or diagnosing production quality
- Engagement shape
- Evaluation baseline, release gates, tracing, and operating cadence
The service in practice
Start with the work that needs to improve
A handful of successful prompts cannot establish whether an AI workflow is useful, safe, or economical. Teams need representative tests, component-level diagnosis, end-to-end task evaluation, and production evidence.
Capilano AI builds evaluation and observability systems for agents, retrieval, document workflows, and model-enabled applications. The goal is a repeatable release decision and a practical operating cadence—not an impressive dashboard disconnected from user outcomes.
Why this work matters
Replace operational friction with a service your team can trust
What is getting in the way
Failures cannot be located
Without traces and component metrics, teams cannot tell whether the prompt, retrieval, model, tool, integration, or source data caused the outcome.
Production behavior drifts silently
Content, models, prompts, tools, users, and traffic change while the original test set remains static.
What the engagement should change
A representative evaluation system
Approved scenarios, expected evidence, rubrics, automated checks, expert review, and known limitations.
Release gates teams can explain
Quality, safety, latency, and cost thresholds linked to the risk and value of the workflow.
Production learning without guesswork
Traces, dashboards, review samples, alerts, failure taxonomy, and regression tests connected to operating ownership.
Evaluation sample
Compare a candidate against a reviewed baseline
This interactive demonstration uses invented sample data. It does not upload a file, call a production model, or represent a client result.
Task completion68%86%
Grounded answers74%91%
Correct escalation81%94%
Illustrative gate passed
Candidate improved all three sample metrics. Real releases also review failures, safety, latency, cost, and protected holdout cases.
Where to apply it
Start with a bounded operating outcome
Each use case is scoped with data access, integration, evaluation, human approval, monitoring, and ownership from the beginning.
RAG and knowledge evaluation
Separate retrieval relevance, citation quality, answer groundedness, permission behavior, and answerability.
Agent and tool-use evaluation
Measure task completion, tool selection, arguments, sequencing, approvals, and escalation.
Document workflow evaluation
Score classification, field extraction, validation, review rates, and downstream posting accuracy.
Model or prompt migration
Compare quality, latency, cost, safety, and failure categories before changing production behavior.
How we deliver
Evidence before scale. Ownership before launch.
The method is intentionally practical: reduce uncertainty early, build the full operating path, and leave the service with people who can run it.
See our delivery approach- 01
Define the operating outcome
Map the work, baseline, users, decisions, exceptions, risks, and evidence required to call the engagement successful.
- 02
Prove the difficult assumptions
Test the data, integrations, model or platform behavior, quality target, and human workflow before scaling the build.
- 03
Build the complete service
Implement identity, data, workflow, evaluation, telemetry, deployment, documentation, and recovery—not only the visible AI feature.
- 04
Release with an owner
Roll out in controlled stages, train the operating team, review production evidence, and convert confirmed failures into improvements.
Typical engagement
What your team receives
The exact scope follows the operating outcome and current environment. These are the core work products typically required to make the result useful and supportable.
Use-case-specific quality rubric
Representative golden test dataset
Built-in and custom evaluators
Prompt, retrieval, model, and agent comparisons
OpenTelemetry traces and production dashboards
Release gates, alerts, and review cadence
Questions to resolve early
Frequently asked questions
What is a golden dataset?
It is a versioned set of representative inputs with expected evidence, actions, or judgments. It should cover common work, important edge cases, known failures, and policy-sensitive scenarios.
Can LLMs evaluate other LLMs?
They can support scalable rubric-based review, but their judgments must be calibrated against expert decisions. Deterministic checks and human review remain important where facts or consequences matter.
What should be traced?
Enough to reconstruct the decision path: retrieval, prompts, model responses, tool calls, validation, approvals, latency, cost, and outcome—while minimizing sensitive data and applying appropriate access and retention.
How often should evaluations run?
Run regression tests before relevant releases, targeted checks during incidents, and sampled production review on a cadence proportional to usage, change rate, and risk.
Apply it in context
Connect the service to an operating job
Use case
AI call answering, qualification, and booking
Answer routine inbound calls, capture a complete brief, apply booking rules, update the CRM, and hand consequential or uncertain conversations to a person.
Use case
Enterprise search and grounded RAG
Give teams permission-aware answers grounded in approved company content, with citations, freshness controls, and measurable retrieval quality.
Industry
Healthcare operations
Improve administrative intake, document routing, staff knowledge access, and operational coordination while keeping clinical judgment, privacy review, and accountable human oversight outside the automated workflow.
Anonymized delivery pattern
A controlled document-intake pattern for imaging operations
A representative administrative workflow for classifying incoming referral and operational documents, extracting routing metadata, and directing uncertain items to a trained team member.
Field notes
Read the implementation detail
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LLM Evaluation in Microsoft Foundry: Lessons from an Imaging Workflow
A practical evaluation pattern for an imaging-grounded AI workflow: separate extraction, retrieval, reasoning, tool use, and operational quality before treating a strong demo as a production system.
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Enterprise automation is moving from isolated scripts to AI-enabled operating systems. The differentiator is not autonomy alone; it is governed integration, evidence, observability, and ownership.
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