Evaluate, Validate, and Trust Your RAG Systems at Scale
The RAG evaluation platform that catches hallucinations before your users do.
Gen AIR is Techment's RAG evaluation platform, purpose-built to test Retrieval-Augmented Generation systems for accuracy, relevance, and groundedness. It combines Azure AI Foundry's automated evaluation metrics with custom dashboards and human-in-the-loop validation — giving enterprise teams end-to-end confidence in what their AI actually says, before it reaches production.
Why RAG Evaluation Gaps Stay Invisible Until Production
If you can't measure retrieval and groundedness, you can't trust your RAG system.
No way to measure RAG performance
Teams lack structured tools to assess whether their AI is retrieving the right content and generating accurate answers.
Multi-metric evaluation engine
Scores every response across similarity, retrieval, completeness, relevance, and groundedness using GPT-4.1 as judge.
Hallucinations go undetected
Generative models produce plausible but unsupported answers, and without groundedness checks these slip through.
Groundedness scoring, built in
Every answer is checked against its source context, so unsupported claims get flagged — not shipped.
Retrieval quality is an unknown
Even when an answer looks correct, the underlying retrieval may be pulling the wrong or incomplete context.
Dedicated retrieval scoring
Gen AIR isolates retrieval accuracy from generation quality, so you know exactly where a failure originates.
No test data for custom systems
Custom RAG systems have no ready-made evaluation datasets, and generating realistic test cases manually is slow and costly.
Synthetic dataset generation
Auto-generates realistic Q&A datasets directly from your own PDFs using Azure AI Foundry — no manual authoring.
Human review is disconnected
QA teams review AI outputs in silos, with no structured interface to compare expected vs. actual responses.
Human-in-the-loop validation
A purpose-built dashboard lets QA analysts compare responses side by side, mark correctness, and log feedback in one place.
Continuous improvement has no baseline
Without reproducible evaluation metrics, it is impossible to know whether a model or prompt change actually helped.
Reproducible, versioned runs
Every evaluation run is tracked, so you can measure the real impact of each change over time.
From Raw PDFs to a Trusted Ground-Truth Dataset
Gen AIR layers a structured evaluation and human-review workflow on top of Azure AI Foundry. Step through it below.
Dataset Creation
Generate a synthetic Q&A dataset from your reference PDFs in AI Foundry. Configure task type, question style, sample size (50–1,000) and output format, then export as JSONL, enrich with real responses, citations and edge cases, and convert back for evaluation.
Five Metrics Behind Every Score
Every response is judged on five independent axes, so a failure points at a cause — retrieval, coverage or grounding — not just a low number. Select an axis to see what it catches.
Similarity
Semantic match between the generated answer and the expected response.
Answers that drift in meaning even when the wording looks plausible.
Proven Performance, Out of the Box
Gen AIR's evaluation framework has been validated on real enterprise RAG deployments.
Zero manual test authoring
Synthetic datasets generate themselves from your existing PDFs.
Catch hallucinations early
Groundedness scoring flags unsupported answers before release.
Identify retrieval gaps before users do
Retrieval failures are isolated from generation failures.
Build stakeholder trust
Every score is backed by a reproducible, versioned evaluation run.
Continuous, measurable improvement
Compare model and prompt changes against a real baseline.
Enterprise-ready, any RAG stack
Works with custom RAG implementations, not just off-the-shelf ones.
Built on Azure AI Foundry & Purpose-Built Review Tooling
AI & Evaluation Platform
Dashboard & Review Interface
Evaluation Metrics
Gen AIR FAQs
What is Gen AIR?
Gen AIR is Techment's evaluation framework for Retrieval-Augmented Generation (RAG) systems. It combines Azure AI Foundry's automated metrics with custom dashboards and human validation to measure a RAG system's accuracy, relevance, and groundedness end to end.
How does Gen AIR detect hallucinations?
Gen AIR scores every generated response for groundedness — checking whether the answer is actually supported by the retrieved source content. Responses that make claims unsupported by the retrieved context are flagged before they reach production.
Do I need to create test data manually?
No. Gen AIR auto-generates realistic synthetic Q&A datasets directly from your own reference PDFs using Azure AI Foundry, including adversarial and out-of-scope edge cases, removing the need for manual test authoring.
Which evaluation metrics does Gen AIR measure?
Gen AIR evaluates RAG responses across five metrics: similarity, retrieval score, completeness, relevance, and groundedness, using GPT-4.1 (or another selected model) as the judge model.
How does human validation work in Gen AIR?
QA reviewers use the Streamlit dashboard to compare query, agent response, and expected answer side by side. They mark responses as correct or incorrect, add feedback, and build a trusted ground truth dataset for ongoing improvement.
Can Gen AIR work with my existing RAG stack?
Yes. Gen AIR is enterprise-ready and designed to evaluate custom RAG implementations rather than requiring a specific retrieval or generation stack, so it layers onto systems you've already built.
Know Exactly How Your RAG System Performs, Before Your Users Do
Book a personalised demo and see Gen AIR evaluate a real RAG deployment end to end.
