Hallucination blind spots
Confident, wrong answers that damage trust. The system sounds certain while it fabricates, and standard tests never flag it.
We test the systems your business runs on, from web and mobile applications to LLMs, RAG, agents, and predictive ML, with measurable quality gates so every release reaches production with confidence.
Classic testing still matters: functionality, performance, security, integrations. But AI systems break in ways a unit test never sees. We cover both, so a model change and a code change are held to the same standard before they reach your users.
The failures that hurt most are the ones that pass every traditional test and surface only after launch.
Confident, wrong answers that damage trust. The system sounds certain while it fabricates, and standard tests never flag it.
Systematic unfairness that goes undetected until it becomes a legal and reputational crisis hiding in everyday output.
Performance degrades quietly over time. What worked at launch slowly breaks without warning, and nobody owns the moment it does.
From established software testing to AI evaluation. Switch between the two to see how we cover each discipline.
Task accuracy, response consistency, hallucination detection, adversarial robustness, and agent behavior correctness.
Toxicity checks, bias testing, data privacy, security vulnerabilities, and governance readiness.
Latency, throughput, cost trade-offs, failure handling, and regression across model versions.
Monitoring, release gates, incident response, and continuous evaluation pipelines for production AI systems.
End-to-end quality from requirements analysis through deployment and beyond, with every phase covered.
Manual testing for user experience paired with automation for speed and repeatable accuracy.
Verify cloud-based applications hold up on scalability, reliability, and performance.
QA built into your DevOps pipeline for continuous quality inside agile delivery.
Load, performance, and stress tests that confirm your applications survive real-world traffic.
Proactive vulnerability assessments that protect sensitive data and applications.
Confirm the components of your software ecosystem interact the way they should.
Comprehensive coverage of API functionality, reliability, and data security across interconnected systems.
Optimize your QA process and equip your team with the testing knowledge to keep quality high.
Quality work is not a cost center. It defends the things that are expensive to win back once they are lost.
Meeting the highest standards reinforces the reliability and trust your brand is built on.
Reliable, user-friendly experiences turn into satisfied customers, loyalty, and advocacy.
Catching defects early in development cuts costly rework and frees up resources later.
Robust QA clears the path for smoother adoption and faster, more confident release cycles.
A clear path that starts with your goals and ends with release gates and monitoring you can rely on.
Define use cases, risks, and acceptance criteria, and map the architecture we are testing against.
Build the test suite, golden dataset, scoring rubric, and baseline metrics tailored to your system.
Run tests, tune prompts, retrieval, and policies, and implement safeguards and regression coverage.
Set release gates, automate evaluation, and monitor for drift once the system is live.
One stack across both disciplines. Switch between the frameworks we use to evaluate AI systems and the platforms our teams use to test the software you ship.
Structured evaluations for LLMs, RAG pipelines, and agents using benchmark suites, LLM-as-a-judge, and automated scoring.
Golden datasets, adversarial inputs, and business-focused scenarios to measure quality, regression, and robustness.
Validate structure, correctness, faithfulness, and consistency with schema checks, rule-based controls, and semantic evaluation.
Context relevance, retrieval precision, chunking quality, citation support, and grounded response behavior.
Bias, policy compliance, privacy leakage, unsafe outputs, and prompt injection resilience across critical workflows.
Traces, prompt versions, incidents, and performance metrics in dashboards that support ongoing quality improvement.
Plan, organize, and trace test cases and results across releases, with full coverage visibility for the team.
Load, stress, and endurance tests that confirm applications hold up under real-world traffic.
Active scanning and manual probing of web apps and APIs to find vulnerabilities before attackers do.
Automated UI and end-to-end checks across web and mobile so regressions surface early, not in production.
Run tests automatically on every commit and gate releases inside your delivery pipeline.
Generate realistic test data and validate data integrity at the database layer for reliable, repeatable runs.
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A practical guide to connecting AI quality work to business outcomes, written for the people who fund it and the people who build it.
Let’s assess your current AI solution and define measurable quality gates.
AI QA is the practice of evaluating and testing AI systems, including LLMs, RAG pipelines, agents, chatbots, and predictive ML, against measurable quality gates before and after they reach production. It targets the failures traditional testing misses: hallucinations, bias, prompt injection, and quality that drifts over time.
Traditional QA checks that code does what it was told to do. AI systems can fail without breaking: they hallucinate, leak bias, and degrade silently while passing every unit test. AI QA adds grounding checks, prompt-injection red-teaming, PII scanning, and regression baselines on every model change.
An assessment reviews one of your applications or AI systems to find where quality is at risk. We map the architecture, define acceptance criteria you can measure, and lay out a path to production readiness with the right release gates.
Discovery and evaluation design each run about one to two weeks. After that we move into execution and hardening, then set release gates and continuous QA that keep running after launch. Exact timing depends on the number of systems and the complexity of the use case.
For AI QA we use evaluation frameworks such as Braintrust, Promptfoo, DeepEval, and RAGAS, alongside dataset, output-validation, RAG-testing, safety, and observability tooling. For software testing we work across test management, performance, security, automation, CI/CD, and test-data platforms.
By signing up for the waiting list now, you'll secure your spot for early access and claim these valuable benefits.