AI Development
AI Evaluation & Testing Services
We implement evaluation and testing for AI systems so quality is measurable: golden datasets, automated scoring, regression gates, and dashboards for RAG, agents, and copilots.
Overview
What this service is
We create test cases that represent real user queries and edge cases, then measure outputs with automated scoring and human review where needed.
For RAG and agents, we validate both the answer and the underlying mechanics—retrieval relevance, citation coverage, and tool-call correctness.
Evals integrate into your delivery flow so prompt and model changes are gated the same way you gate code releases.
Benefits
What you get
Predictable quality improvements
Teams can iterate quickly while avoiding accidental regressions in production.
Fewer customer-facing failures
Evals catch risky changes before they impact users and support teams.
Clear quality targets
Dashboards show where performance is strong and where tuning is required.
Better retrieval and tool correctness
RAG and agent components are tested separately, not treated as a black box.
Safer model/provider changes
Switch providers or models with a regression harness that validates behaviour.
Features
What we deliver
Golden datasets
Representative queries and expected outcomes built from your real workflows and content.
Automated scoring
Heuristics, model-graded scoring, and structured checks for format and correctness.
Retrieval evaluation
Measure relevance, coverage, and citation quality for RAG systems with repeatable tests.
Tool-call validation
Validate schemas, parameters, retries, and idempotency for agent actions and workflows.
Regression gates in CI
Run evals on prompt/model changes and block releases when quality drops below thresholds.
Quality dashboards
Track metrics over time and identify which prompts, sources, or tools cause failures.
Process
How we work
Define success criteria
We set measurable targets and build an eval plan that matches your workflows.
Build datasets
We collect and curate test cases, edge cases, and expected outcomes.
Implement scoring
We implement scoring, dashboards, and manual review loops where necessary.
Add regression gates
We integrate evals into CI and define threshold-based release gates.
Tech Stack
Technologies we use
Core
Tools
Use Cases
Who this is for
RAG knowledge assistants
Test retrieval relevance, citation coverage, and answer helpfulness across a real query set.
Tool-enabled agents
Validate tool parameters and outcomes so automations remain correct after prompt changes.
Summarization and extraction
Score structured outputs against expected schemas and key field accuracy targets.
Safety and policy constraints
Add red-team and policy tests for prompt injection and disallowed output scenarios.
Provider migrations
Compare models/providers using the same dataset to choose the best quality/cost trade-off.
FAQ
Frequently asked questions
We start with a focused set (often 30–150 cases) that represent core journeys, then expand based on usage and failures.
Yes. We measure retrieval relevance/coverage and generation behaviour so improvements are targeted and measurable.
It typically speeds teams up after initial setup by reducing production regressions and debugging time.
Yes. We add adversarial cases for injection, jailbreak attempts, and policy violations relevant to your product.
Yes. We design evals to run efficiently with tiers (quick checks per PR, deeper suites nightly or pre-release).
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Regional
Delivery considerations for your region
Data and risk discovery (Australia)
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 (Australia)
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 (Australia)
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 (Australia)
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
Stop shipping AI changes without confidence
Share your workflows and examples—we’ll build an eval plan with datasets, scoring, and quality gates.
Regression checks included.