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
Compliance & Data (UK/EU)
For UK teams, we default to GDPR-first thinking: data minimisation, purpose-limited storage, and clear access boundaries.
We can work under a DPA (template available on request) and implement practical retention/deletion flows when needed.
- GDPR-first patterns (minimise, restrict, document)
- DPA template available on request
- Retention/deletion and export flows where required
- Least-privilege access and secure session handling
- PII-safe logging + secure-by-default configuration
- NDA available for early-stage discussions
Timezone & Collaboration (UK/EU)
We align to UK time and EU overlap (GMT/BST with CET-friendly windows) for fast feedback cycles.
We keep the process lightweight: async updates, clear priorities, and written decisions to avoid ambiguity.
- UK/EU overlap with GMT/BST windows
- Async-first delivery with documented scope
- Weekly milestones and structured demos
- Clear escalation path for blockers
- Tight change control with clear sign-offs
Engagement & Procurement (UK)
We support typical UK procurement flows with clear scopes, change control, and invoice cadence.
If you prefer a discovery-first engagement, we can run a short paid discovery to lock requirements before build.
- GBP-based engagements and invoicing options
- Discovery-first option to reduce delivery risk
- Milestone-based billing when appropriate
- Transparent change control and sign-offs
- Vendor onboarding pack on request
Security & Quality (UK/EU)
We build for reliability and maintainability: clean PRs, tight review loops, and test coverage that matches risk.
Performance budgets and release checklists keep launches predictable—especially when multiple stakeholders review changes.
- CI-friendly testing: unit + integration + smoke tests
- Performance budgets + bundle checks (Core Web Vitals-minded)
- Structured release notes and rollback-safe deployments
- Security checklist for auth, roles, and data flows
- Observability hooks (logs + error tracking) ready for production
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.