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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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.