Your AI model can look flawless in a demo and still fail the first week it meets real edge cases. Our AI testing services help to keep your systems accurate, reliable, and production-ready, ensuring your models behave as expected at scale.
years of AI experience
years in tech
clients worldwide
We combine AI expertise and QA under one roof, which means our services aren’t a generic QA checklist adapted for AI — they’re built by engineers who also build the systems being tested.
Our AI testing services cover the full stack: from a single model to the workflows and infrastructure it depends on.
Verify that AI features behave as specified across expected and edge-case inputs, not just the “happy path” a demo is built around.
Validate multi-step agent behavior: tool calls, decision chains, memory handling, and failure recovery when a step in the chain breaks.
Benchmark accuracy, precision, recall, and drift against labeled datasets and real-world data distributions.
Test the full application layer around the model: UI, APIs, business logic, and how AI outputs are actually consumed downstream.
Validate behavior every time a model, prompt, or finetune changes, so an “improvement” upstream doesn’t quietly break something downstream.
Confirm that AI components exchange data correctly with the systems already running your business.
Measure latency, throughput, and cost under real and peak traffic.
Check that model decisions can be traced, documented, and defended to auditors, regulators, or your own risk team.
We replaced the client’s periodic audit cycle with a continuous, three-layer testing pipeline. Key achievements:
We restructured the client’s ML pipeline around a unified model architecture, introduced RoboFlow for dataset annotation and training, and rebuilt data versioning so every model update could be validated and reproduced consistently.
Before model testing could be meaningful, we validated the data foundation: consolidating 100M+ operational records into a layered architecture, testing entity mapping across flights and turnarounds, and building a pilot ML pipeline.
That’s exactly what a first conversation is for.
accuracy testing for contract AI, hallucination detection, and explainability checks required for legal use cases.
bias auditing for hiring and scoring models, plus testing of LLM-powered learning and assessment features.
predictive maintenance model validation and anomaly detection testing under real sensor data conditions.
route and delay-prediction model validation, data pipeline integrity checks, and real-time forecasting accuracy testing under live operational conditions.
fraud model validation, regulatory compliance checks, and audit trails for automated AI decisions.
testing for FDA- and HIPAA-sensitive models, diagnostic AI validation, and safety-critical output verification.
recommendation engine testing, demand forecasting validation, and A/B benchmarking between model versions.
1. Discovery and AI risk assessment | We review your AI system’s architecture, data sources, and use cases to identify where the highest-risk failure points are, before writing a single test case. |
2. Test environment and automation setup | We build a testing environment that mirrors production, and set up automated pipelines so tests run continuously, not just before release. |
3. AI validation and testing execution | We run functional, model, security, and load tests against real and synthetic data, documenting every deviation. |
4. Results analysis and optimization recommendations | Findings are translated into a prioritized list: what’s blocking, what’s a risk, and what can wait. |
5. Production readiness and continuous monitoring | Once the system ships, we help set up ongoing monitoring so model drift, new edge cases, and regressions are caught before users notice. |
— systematic AI model validation catches inconsistencies before they reach users.
— load and scalability testing prevents the kind of failures that only show up under real traffic.
— regression and integration testing mean updates ship without breaking what already works.
— AI compliance testing builds the documentation and audit trail regulators increasingly expect.
— AI security testing surfaces prompt injection, data leakage, and access risks before attackers do.
— catching defects during testing is consistently cheaper than fixing them after deployment.
Traditional testing checks against a fixed, predictable output. AI testing has to account for probabilistic behavior, model drift over time, and data that changes after deployment — so test design, metrics, and pass/fail criteria all look different.
Both. We test in-house models and fine-tunes as well as third-party and API-based models (OpenAI, Anthropic, and others) as they’re integrated into your application and workflows.
We benchmark against labeled datasets and task-specific metrics — precision, recall, F1 score, hallucination rate, or task-completion rate, depending on what the model actually does — rather than relying on a single generic score.
We evaluate model outputs across demographic and use-case segments to identify systematic disparities, then trace them back to training data, prompts, or model behavior so they can be addressed at the source.
Yes. We set up automated AI testing as part of your existing pipeline, so every model or prompt change is validated before it reaches production, not after. For teams without in-house QA capacity, this also works as AI testing outsourcing — you keep ownership of the model, we own the validation.
Yes. Production AI systems change as data and usage change. We offer continuous monitoring and periodic re-testing to catch drift, new edge cases, and regressions after launch.
