AI Development
AI Evaluation & Testing
We build evaluation and testing systems for LLM products: golden datasets, automated scoring, human review loops, and regression gates. This turns “it feels worse” into measurable signals your team can ship against. Delivery aligned to Canada teams (PRO).
Overview
What this service is
AI evaluation is how you measure quality and prevent regressions across prompts, models, retrieval settings, and tool behaviors.
We define realistic test sets and scoring criteria, then automate evaluation so changes can be reviewed and shipped safely.
Delivery includes dashboards and workflows so teams can iterate quickly without losing trust in production behavior.
Start with evidence
Begin with a focused opportunity sprint
In 1–2 weeks, we map one consequential operation, test its automation case, and define a governed implementation plan before a larger build.
Standard
AI delivery standard
Quality and safety practices we ship with AI builds so the system stays measurable, maintainable, and production-ready.
Logging + tracing
Conversation and tool traces with request IDs, error visibility, and debug-friendly runbooks.
Guardrails + safety
Tool allowlists, PII-safe patterns, refusal behavior, and escalation routes for edge cases.
Evals + regression tests
Golden queries, scorecards, and regression checks so quality improves over time instead of drifting.
Cost + latency controls
Caching, prompt discipline, retrieval tuning, and routing so your app stays fast and predictable at scale.
Documentation + handoff
Architecture notes, environment setup, and next-step roadmap so your team can iterate safely after launch.
Security-first integration
Secrets isolation, role-based access, audit-friendly actions, and minimal data retention by design.
Benefits
What you get
Catch regressions before they hit users
Measure quality improvements with real metrics
Reduce subjective debates with shared eval criteria
Improve safety with red-team test coverage
Ship model/prompt changes confidently
Prioritize fixes using labeled failure modes
Features
What we deliver
Golden datasets
Representative test queries and expected behaviors based on real user intents and business rules.
Automated scoring
Scoring for relevance, correctness, format, citation quality, and policy compliance with repeatable runs.
Human review loops
Sampling-based review workflows to label failures and improve datasets over time.
Regression gates
CI-style checks that fail builds when quality drops below thresholds for key intents.
Safety and red-team tests
Prompt injection, policy-violating requests, and adversarial scenarios to validate guardrails.
Dashboards + reporting
Visibility into quality trends, failure clusters, and “what changed” between releases.
Process
How we work
Define criteria
Success metrics, intents, and failure taxonomy.
Build datasets
Golden queries + expected behaviors.
Automate scoring
Repeatable evaluation runs and reporting.
Add gates
CI checks and thresholds for release control.
Review loop
Human labeling workflow and iteration plan.
Tech Stack
Technologies we use
Core
Tools
Services
Use Cases
Who this is for
RAG accuracy validation
Evaluate retrieval precision and citation quality on real queries and content changes.
Chatbot regression protection
Prevent prompt or model changes from degrading user-facing answers and escalation behavior.
Tool-action correctness testing
Validate that agents call the right tools with the right parameters under edge cases.
Safety validation
Test prompt-injection defenses and refusal behavior against adversarial user inputs.
Enterprise rollout reporting
Produce quality reports and release notes that stakeholders can trust.
Implementation Patterns
How we frame common AI workflows
Illustrative patterns only—not client case studies, endorsements, or production-result claims.
Regulated mobile data workflow pattern
Challenge: Sensitive data workflows need explicit access boundaries, traceability, and documented operating responsibilities.
Approach: Threat-model the workflow, map authorization rules, select encryption controls, and define auditable state transitions.
Validation: Test access boundaries and recovery paths, record residual risk, and obtain any required independent compliance assessment.
Large knowledge-base retrieval pattern
Challenge: Long, mixed-format source collections need traceable retrieval and safe behavior when evidence is weak.
Approach: Evaluate hybrid retrieval, reranking, citations, structured outputs, and defined fallback or human-review paths.
Validation: Use a representative offline evaluation set and report citation quality, latency, and cost under documented test conditions.
Operations automation pattern
Challenge: Approval and system-sync workflows need deterministic controls around exceptions, retries, and ownership.
Approach: Model the workflow, add validation and approval gates, and use AI only for bounded classification or extraction tasks.
Validation: Baseline manual steps, test exception paths and audit logs, then compare pilot measurements before considering wider rollout.
Explore
Related solutions & technologies
Useful next pages if you’re planning an AI pilot or scaling this into a larger product.
Related solutions
FAQ
Frequently asked questions
Done right, they speed it up. Teams ship changes faster when they can see impact and avoid regressions early.
Usually both. We score relevance, correctness, formatting, citation quality, and safety depending on the product.
Yes. We include adversarial test sets and safety checks aligned to your policy and guardrails.
For most real products, yes. Sampling-based human review helps validate edge cases and keeps datasets honest.
Yes. We design evaluation runs and budgets so you can run meaningful checks during CI without excessive cost.
Datasets, scoring scripts, dashboards, and runbooks for expanding coverage over time.
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Regional
Delivery considerations for your region
Data and risk discovery (Canada)
Privacy, security, residency, and regulatory requirements differ by workflow. We document the applicable data flows, roles, retention needs, and control owners before recommending an architecture.
The resulting proposal lists the controls and evidence that are actually in scope. It is not a generic compliance, certification, or legal-assurance promise.
- Map data sources, destinations, roles, and sensitive fields
- Record access, retention, logging, and deletion requirements
- Identify required security or procurement evidence before contracting
- Use an NDA or DPA only when the parties mutually execute it
Working model (Canada)
Exact live-overlap hours, response expectations, meeting windows, and escalation contacts are confirmed in the proposal for each engagement.
Written decisions, scoped milestones, and asynchronous updates reduce unnecessary meetings without implying an unagreed service level.
- Proposal-specific overlap and meeting windows
- Named owners for decisions and blockers
- Written scope, assumptions, and change decisions
- Milestone cadence agreed before kickoff
Commercial setup (Canada)
The contracting entity, proposal currency, invoicing cadence, payment terms, intellectual-property terms, and required vendor documents are agreed before work begins.
The Opportunity Sprint can establish the evidence needed to scope a production pilot; it does not pre-commit either party to a rollout.
- Contracting entity and currency confirmed in writing
- Milestones and acceptance criteria defined in the proposal
- Vendor-document requirements identified before signature
- Scope changes require an explicit written decision
Delivery controls (Canada)
Testing, observability, release, security, and handover controls are selected for the actual system risk rather than promised as a generic bundle.
Acceptance measures and production responsibilities are recorded before implementation so both teams know what evidence will support release.
- Risk-based testing and acceptance measures
- Release, rollback, and observability responsibilities
- Security controls tied to the agreed threat model
- Handover artifacts defined in the signed scope
Want help with AI evaluation and testing?
Share your requirements for Canada delivery. proposal currency confirmed before contracting.
We’ll review the context and reply with a practical next step.