AI Center of Excellence

Make your AI reliable, safe and ready for production

We evaluate and test AI systems (LLMs, RAG, agents, chatbots, and predictive ML) with measurable quality gates, so you can deploy with confidence.


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    AI fails differently.
    We test for that.

    Traditional QA misses hallucinations, prompt injections, and silent regressions. Our AI QA engine catches what unit tests can’t — before it reaches production.
    • Hallucination & grounding checks on every output
    • Prompt injection & jailbreak red-teaming suite
    • PII leakage scanner across all model outputs
    • Regression baselines — every model change tracked
    • Latency & cost profiling under real load

    The hidden risks in production AI

    Hallucination blind spots

    Confident-sounding wrong answers that damage trust. Your AI sounds certain, but is completely fabricating.

    Bias leakage

    Systematic unfairness that goes undetected until it becomes a crisis. Legal risk hiding in every output.

    Quality drift

    Performance degrades silently over time. What worked at launch slowly breaks without warning.

    Comprehensive AI quality assurance

    AI Quality Evaluation

    Measuring task accuracy, response consistency, hallucination detection, adversarial robustness, and agent behavior correctness.

    Safety, Risk & Compliance

    Covering toxicity checks, bias testing, data privacy, security vulnerabilities, and governance readiness.

    Performance & Reliability

    Testing latency, throughput, cost trade-offs, failure handling, and regression across versions.

    Operational Readiness

    Covering monitoring, release gates, incident response, and continuous evaluation pipelines for production AI systems.

    How we work?

    1

    Discovery (1-2 weeks)

    Define use cases, risks, acceptance criteria, and map architecture.

    2

    Evaluation Design (1-2 weeks)

    Build test suite, golden dataset, scoring rubric, and baseline metrics.

    3

    Execution & Hardening

    Run tests, tune prompts/retrieval/policies, implement safeguards and regression.

    4

    Readiness & Continuous QA

    Set release gates, automate evaluation, and monitor drift after launch.

    Technology stack for AI QA & evaluation

    Evaluation frameworks

    Run structured evaluations for LLMs, RAG pipelines, and agents using benchmark suites, LLM-as-a-judge, and automated scoring.

    Braintrust | Promptfoo | DeepEval | RAGAS

    Datasets & benchmarks

    Build golden datasets, adversarial inputs, and business-focused scenarios to measure quality, regression, and robustness.

    Synthetic data | Golden sets |  Label Studio

    Output validation

    Validate structure, correctness, faithfulness, and consistency with schema checks, rule-based controls, and semantic evaluation.

    Schema validation | Faithfulness | LLM-as-a-judge

    RAG & retrieval testing

    Assess context relevance, retrieval precision, chunking quality, citation support, and grounded response behavior.

    Context precision | Recall | Grounding

    Safety & governance

    Check bias, policy compliance, privacy leakage, unsafe outputs, and prompt injection resilience across critical workflows.

    Guardrails | Policy tools | Red teaming

    Observability & continuous QA

    Connect traces, prompt versions, incidents, and performance metrics into dashboards that support ongoing quality improvement.

    Langfuse | LangSmith | Arize Phoenix | Monitoring

    Our Success Stories

    The 2025 AI Quality and ROI Playbook:
    From Engeneering to the Boardroom

    QA impact on production AI

    0 %

    Hallucination detection rate

    0 %

    Reduction in production incidents
    0 X

    Faster deployment cycles with confidence

    Ready to make your AI production-ready?

    Let’s assess your current AI solution and define measurable quality gates.


      By ticking this box, you agree to ⋮IWConnect’s Terms & Privacy Policy. You also agree to receive future communications from ⋮IWConnect. You can unsubscribe anytime.