Validate your AI before your users find the problems.
AI systems fail in ways traditional QA was never designed to catch. Hallucinations, prompt injection, tool-call failures, and behavioral drift require a specialized validation approach — and that's exactly what we provide.
AI testing coverage
AI testing is not the same as software testing.
Traditional software testing
Tests deterministic functions. Given input X, you always expect output Y. A broken assertion tells you clearly what failed and where.
AI system testing
Tests probabilistic systems where outputs vary by design. Evaluation requires ground-truth datasets, semantic scoring, adversarial inputs, and behavioral regression tracking across model versions.
We bridge this gap with structured evaluation frameworks, curated test datasets, and evidence-based reporting that gives your team a clear, defensible answer to "is this AI system ready to ship?"
7 failure modes teams discover too late.
These are the categories of AI failure that surface in production — after the system is already in users' hands.
Hallucination
AI models generate plausible-sounding but factually incorrect outputs. Without structured evaluation, teams ship confidently incorrect information to users.
Prompt Injection
Malicious inputs can override system instructions, bypass safety filters, or extract sensitive data. Standard security testing misses this category entirely.
Inconsistent Behavior
AI outputs vary across model updates, prompt changes, and context window differences — making regression testing essential and uniquely challenging.
Tool Call Failures
AI agents that use tools (search, code execution, APIs) can call wrong tools, pass bad parameters, or fail silently without proper validation coverage.
Bias & Fairness Issues
Models can produce outputs that are biased by demographic data in training — issues that surface inconsistently and require structured evaluation frameworks.
Context Window Drift
Long-context conversations degrade instruction-following and memory reliability. Behavior at the start of a conversation differs from behavior 20 turns in.
Jailbreak Vulnerabilities
Crafted inputs can bypass content policies and safety guardrails. Red-team testing is required to identify and patch these before public deployment.
A structured evaluation framework for every layer of your AI system.
Output Quality Validation
Safety & Security Testing
Reliability & Regression
Agentic Workflow Testing
Discover → Evaluate → Validate → Report → Improve
Map the AI system and risk surface
We audit your AI architecture, identify all input vectors, model calls, tool integrations, and data flows — then prioritize by risk.
Build a benchmark dataset
We create a structured test dataset including golden-path cases, edge cases, adversarial inputs, and regression anchors specific to your use case.
Run structured validation against your system
We execute automated and manual evaluation across all dimensions — accuracy, safety, consistency, tool use, and compliance — and capture evidence.
Deliver evidence-based findings
You get a structured report with pass/fail rates, risk severity ratings, specific failure examples, and clear remediation recommendations.
Support prompt and system fixes
We help fix identified issues — prompt refinement, guardrail implementation, retrieval tuning, or safety layer additions — then re-validate.
Get started
Ready to validate your AI system?
Book a free consultation and we'll audit your current AI system, identify the highest-risk areas, and show you exactly what validation coverage you need before launch.