De-risk every AI release before it reaches a user.
AI features fail differently. They're confidently wrong, subtly biased, or fine in English and broken in Hindi. We combine automated evaluation with trained human reviewers to find those failures while you can still fix them.
An AI feature that's wrong 3% of the time is a product risk, not a rounding error.
Confidently wrong
LLMs produce fluent, well-formatted answers that are factually false. Nothing in the output signals the difference. Only a human who knows the domain can tell.
Fine in English, broken elsewhere
A model that performs well in English often degrades badly in other languages — while still sounding fluent enough to be trusted.
Non-deterministic by design
The same prompt returns different output. Traditional pass/fail assertions can't handle that. You need rubric-based evaluation with human calibration.
Quality decays after launch
Models drift, providers update, retrieval indexes go stale. A feature that passed in March can be failing by June with nobody watching.
Coverage across the AI stack.
is the answer true, and is it supported by your sources?
invented facts, fake citations, fabricated policies
same question, repeated: does the answer hold?
harmful, unsafe or inappropriate outputs
output quality across demographic and linguistic slices
adversarial input, instruction override, data exfiltration attempts
does the agent call the right tool with the right arguments?
does it hold state across a long conversation?
is quality equivalent across your supported languages?
response time and token spend under realistic load
does a model or prompt change break what used to work?
Rubrics, not vibes.
We work with your team to define what “good” means for your product: accuracy thresholds, tone requirements, safety boundaries, refusal behaviour. This becomes a scoring rubric, not a subjective opinion.
Golden datasets, adversarial prompts, edge cases and real user queries — assembled to cover the scenarios that actually matter to your business.
Automated scoring gives breadth across thousands of cases. Matched human reviewers — domain experts and native speakers — grade the cases where judgment is required.
You get scored results, failure clusters, root-cause analysis and a prioritised fix list. The suite becomes your regression pack for every future release.
GenAI & LLM Testing
Validate accuracy, consistency and safety across prompts, models and versions — before your users find the gaps.
- Prompt coverage
- Hallucination detection
- Output consistency
- Regression across model versions
AI Agent Testing
Agents plan, call tools and take real actions. We test the whole chain, including what happens when a step fails.
- Multi-step workflows
- Tool-call accuracy
- Failure recovery
- MCP server validation
Chatbot & Conversational AI
Intent coverage, tone, escalation and the messy way real people actually type.
- Intent coverage
- Context retention
- Escalation paths
- Multilingual
Voice AI Testing
Real accents, real background noise, real interruptions — on real devices in real rooms.
- Accent diversity
- Noise conditions
- Barge-in
- Wake-word accuracy
RAG Evaluation
Check that answers are grounded in your documents and that citations point where they claim to.
- Retrieval precision
- Grounding
- Citation accuracy
- Freshness
Red Teaming & AI Safety
Adversarial testing by humans who are genuinely trying to break your model.
- Jailbreak attempts
- Prompt injection
- Toxicity
- Misuse scenarios
Bias & Fairness Testing
Measure output quality across demographic, linguistic and regional slices, with native speakers in each.
- Demographic slices
- Language parity
- Regional fairness
- Documented evidence
Model Monitoring
Models drift quietly. Continuous evaluation catches it before your support queue does.
- Drift detection
- Production sampling
- Sentiment tracking
- Alerting
Questions we get asked.
Ready to ship with confidence?
Book a 30-minute call. We'll map your release process, show you where quality is leaking, and scope a pilot you can run on your next release.