Most AI models are deployed with insufficient testing. They pass unit tests but fail under real-world distribution shift, produce biased outputs across demographic groups, or degrade silently as data changes. Perimattic delivers comprehensive testing and validation — from accuracy benchmarking to adversarial red-teaming — so your models are production-ready before they reach users.
Testing Frameworks & Evaluation Tools We Build On — Great Expectations, Deepchecks, Evidently AI, DeepEval, Ragas, TruLens
AI model testing is the systematic process of evaluating a model's accuracy, robustness, fairness, and reliability before and after deployment. Unlike traditional software testing, which verifies deterministic logic, AI model testing must contend with probabilistic outputs, distribution shift, emergent behaviour, and subgroup disparities that standard unit tests cannot capture.
A model that performs well on a held-out test set can still fail in production. Real-world data distributions drift from training data over time. Edge cases that were rare in training become common in production. Minority subgroups may be systematically underserved. Adversarial inputs — whether accidental or deliberate — expose vulnerabilities that standard evaluation never probes. Without structured validation, these failures remain invisible until they impact customers or generate compliance risk.
Validation goes further than testing: it establishes that a model is not just accurate on aggregate but reliable, fair, and robust under adversarial conditions. Perimattic delivers both testing and validation as a continuous discipline — integrated into your CI/CD pipeline, not limited to a one-time pre-launch checklist.
Coverage
Selected test cases with no systematic edge-case coverage
Coverage
Structured test suites across accuracy, robustness, bias, and regression
Subgroup analysis
Aggregate metrics only — disparities in minority groups go undetected
Subgroup analysis
Disaggregated metrics across demographic and feature subgroups
Adversarial testing
Rare or entirely absent from standard test plans
Adversarial testing
Deliberate adversarial inputs and LLM red-teaming included
CI/CD integration
Manual, run once pre-launch and rarely repeated
CI/CD integration
Automated in deployment pipelines for every model change
LLM evaluation
Ad-hoc prompt testing with no structured evaluation framework
LLM evaluation
Structured RAG evaluation, hallucination scoring, and toxicity checks
The distinction matters most for models deployed in high-stakes contexts: credit scoring, clinical decision support, fraud detection, and hiring. These are exactly where structured AI testing and validation deliver the most critical protection.
Seven specialist service lines, each built for a specific dimension of AI model risk.
We establish accuracy, precision, recall, F1, RMSE, and task-specific metrics on held-out datasets and real production distributions. Benchmarks are disaggregated by subgroup, cohort, and data slice to expose disparities that aggregate metrics conceal.
We probe models with deliberately adversarial inputs — edge cases, out-of-distribution data, and for LLMs, red-teaming and prompt injection attacks — to identify failure modes before production exposes them in high-stakes situations.
We audit AI models for statistical bias and disparate impact across protected characteristics including gender, ethnicity, age, and geography. We provide disaggregated performance metrics, fairness metric analysis, and targeted remediation recommendations.
We embed automated regression test suites in your deployment pipeline so every model update is validated against baseline metrics before reaching production. Failed validations block deployment and trigger instant alerts.
We evaluate large language model performance using structured frameworks: RAG evaluation with Ragas and TruLens, hallucination detection, toxicity assessment, instruction-following accuracy, and systematic red-teaming for safety vulnerabilities.
We validate training and inference data pipelines using Great Expectations, Deepchecks, and custom profiling. Data quality issues upstream compound into model quality issues downstream — we catch them at source, before they corrupt your model.
We design and run A/B tests, shadow deployments, and canary rollouts to validate model changes under real production traffic before full rollout — eliminating the risk of a silent accuracy regression reaching all users at once.
A structured six-stage process from free scoping call to CI/CD-integrated automated regression testing.
We review your model architecture, training data, deployment environment, and intended use cases to scope the testing and validation programme. This session is free and results in a clear testing plan with defined success criteria.
We design a structured test plan covering accuracy benchmarks, subgroup analyses, adversarial scenarios, and regression coverage. We define the metrics, thresholds, and acceptance criteria before any testing begins.
We build automated test suites using appropriate frameworks for your model type: pytest for ML pipelines, Great Expectations for data quality, DeepEval or Ragas for LLMs. Test suites are version-controlled and designed for CI/CD integration.
We run the full test suite against your model and generate a structured evaluation report: aggregate metrics, subgroup disaggregation, adversarial results, and identified failure modes with severity ratings.
We deliver a structured validation report and work through remediation options for any identified failures. We do not just flag issues — we help you understand root causes and prioritise fixes by business impact.
We integrate the test suite into your deployment pipeline so model validation runs automatically on every update. Monitoring hooks ensure ongoing drift or accuracy degradation triggers alerts before users are affected.
We review your model architecture, training data, deployment environment, and intended use cases to scope the testing and validation programme. This session is free and results in a clear testing plan with defined success criteria.
We design a structured test plan covering accuracy benchmarks, subgroup analyses, adversarial scenarios, and regression coverage. We define the metrics, thresholds, and acceptance criteria before any testing begins.
We build automated test suites using appropriate frameworks for your model type: pytest for ML pipelines, Great Expectations for data quality, DeepEval or Ragas for LLMs. Test suites are version-controlled and designed for CI/CD integration.
We run the full test suite against your model and generate a structured evaluation report: aggregate metrics, subgroup disaggregation, adversarial results, and identified failure modes with severity ratings.
We deliver a structured validation report and work through remediation options for any identified failures. We do not just flag issues — we help you understand root causes and prioritise fixes by business impact.
We integrate the test suite into your deployment pipeline so model validation runs automatically on every update. Monitoring hooks ensure ongoing drift or accuracy degradation triggers alerts before users are affected.
Select an industry to see how structured testing and validation protects AI models in that domain.
AI testing in financial services focuses on regulatory compliance, fairness across demographic groups, and accuracy under adversarial market conditions.
AI testing in financial services focuses on regulatory compliance, fairness across demographic groups, and accuracy under adversarial market conditions.
Clinical AI models require the highest standards of accuracy and fairness validation — errors have direct patient safety implications.
Actuarial and claims AI models must be validated for accuracy, regulatory compliance, and fairness across policyholder segments.
Recommendation and demand forecasting models require continuous validation to prevent revenue impact from silent accuracy degradation.
Hiring and shortlisting AI models face strict regulatory scrutiny — bias audits and fairness assessments are critical before deployment.
Contract analysis and compliance AI systems require accuracy validation and adversarial testing before handling high-stakes legal documents.
Perimattic's work resulted in stable production systems. The team was helpful, easily accessible, and communicative through email. Their professionalism was impressive.
Quality
4.5
Schedule
5.0
Cost
5.0
Willing to Refer
4.5
DevOps Managed Services · Oct 2023 – Aug 2024
24/7 monitoring and support for production environments plus Linux server administration for a leasing automation company.
The new architecture is scalable and highly efficient, saving a lot of money in fees. Perimattic provides high-quality IT consulting and cloud development work promptly and at great value. The team remains involved from the planning stage to providing support, showing diligence and proactiveness.
Quality
5.0
Schedule
5.0
Cost
4.5
Willing to Refer
5.0
AWS Migration (Legacy → Microservices) · Nov 2018 – Ongoing
Transitioned a travel systems company's legacy server system to an AWS-based microservices architecture with ongoing maintenance.
Four structural advantages that separate rigorous model validation from a pre-launch checkbox.
We test models we did not build, eliminating the confirmation bias that affects internal teams validating their own work. Our findings are objective, reproducible, and documented in a format suitable for regulatory review.
We test at every stage: data quality pre-training, model accuracy post-training, adversarial robustness pre-deployment, and regression testing post-deployment. One engagement covers the full model lifecycle, not just a pre-launch checklist.
Our team evaluates both classical machine learning models and modern LLM-based systems using dedicated frameworks. Whether you are validating a credit risk model or a RAG-based assistant, we apply the right evaluation methodology.
Test suites we build are designed to run in your deployment pipeline, not as a one-time engagement deliverable. You leave with automated validation that prevents regressions on every future model update.
“AI systems are probabilistic — they can be correct 95% of the time but fail catastrophically on edge cases that matter most. Structured testing is the only reliable way to find those cases before your users do.”
AI model testing is the systematic evaluation of a model's performance, robustness, fairness, and reliability using structured test suites and defined acceptance criteria. Validation goes further: it establishes that the model is fit for its intended purpose in its target deployment environment. Together, testing and validation answer two questions — does the model work correctly, and is it safe and appropriate to deploy?
Standard software testing verifies deterministic logic — a function either returns the correct value or it does not. AI model testing must contend with probabilistic outputs, which means the same input can produce different responses, and aggregate accuracy can mask failure in specific subgroups or edge cases. Additional dimensions specific to AI include data drift (when input distributions shift from training), concept drift (when the relationship between inputs and targets changes), bias and fairness disparities across demographic groups, adversarial vulnerability, and for LLMs, hallucination, toxicity, and instruction-following failure.
Our testing programmes cover accuracy and performance benchmarking against held-out datasets and production distributions; subgroup and cohort disaggregation to identify disparate performance across demographic or feature segments; adversarial robustness testing using out-of-distribution inputs and, for LLMs, red-teaming and prompt injection probing; data quality and pipeline validation using Great Expectations and Deepchecks; regression testing integrated in CI/CD pipelines; LLM-specific evaluation using Ragas, TruLens, DeepEval, and custom hallucination scoring; and A/B and shadow testing in production environments.
LLM evaluation requires frameworks that go beyond traditional accuracy metrics. We evaluate LLMs across multiple dimensions: faithfulness and groundedness (for RAG systems, does the answer accurately reflect the retrieved context?); answer relevance (does the response actually address the question?); instruction-following accuracy (does the model adhere to system prompts and constraints?); hallucination rate (how often does the model generate plausible but incorrect facts?); toxicity and safety (does the model produce harmful content under adversarial prompting?); and output consistency. We use Ragas and TruLens for RAG evaluation, DeepEval for structured LLM testing, and custom red-teaming for safety assessment.
Adversarial testing deliberately probes a model with inputs designed to expose failure modes: out-of-distribution data, edge cases, deliberately ambiguous inputs, and for LLMs, carefully crafted prompts designed to bypass safety guardrails or inject malicious instructions. Models that pass standard accuracy benchmarks frequently fail adversarial testing because training data does not cover adversarial scenarios. Adversarial testing is critical for any model deployed in a sensitive context: healthcare, finance, legal, or any application where a failure has significant consequences.
Bias detection begins with identifying the protected characteristics relevant to your model's use case — typically gender, ethnicity, age, and geography. We then compute disaggregated performance metrics across subgroups defined by those characteristics and apply standard fairness metrics including demographic parity, equalised odds, and calibration. We identify statistically significant performance disparities and provide a root-cause analysis. Where bias is attributable to training data imbalance, feature proxies for protected characteristics, or label noise, we provide targeted remediation recommendations. All audit findings are documented in a structured report suitable for regulatory review.
A focused model accuracy and robustness assessment for a single model typically takes two to four weeks. A comprehensive validation programme including bias audits, adversarial testing, and CI/CD integration typically takes six to eight weeks. LLM evaluation engagements vary based on the number of use cases and the depth of red-teaming required. We provide a more precise timeline after the free scoping call.
Engagement cost depends on the model type, number of models, testing scope, and whether CI/CD integration is included. A focused validation assessment for a single model starts from around USD 15,000. A comprehensive testing programme covering multiple models, bias audits, adversarial testing, and CI/CD integration typically ranges from USD 30,000 to USD 60,000. We scope every engagement before quoting so there are no surprises.
Yes. We build test suites using pytest, Great Expectations, and framework-specific evaluation libraries designed to run in GitHub Actions, Jenkins, or any standard CI/CD platform. Test runs produce structured reports and threshold-based pass/fail signals that can gate deployments. When a model update fails validation, the deployment is blocked and the team receives an alert with the specific metrics that failed. We have integrated testing pipelines with AWS SageMaker, Azure ML, GCP Vertex AI, MLflow, and Kubeflow.
We have delivered AI testing and validation engagements for clients in financial services (credit scoring, fraud detection, risk models), healthcare (diagnostic assistance, clinical documentation, patient triage), insurance (actuarial models, claims prediction), legal and compliance (contract analysis, regulatory risk models), e-commerce (recommendation systems, demand forecasting), and HR and recruiting (screening and shortlisting models). Our testing frameworks are designed to meet the data sensitivity and regulatory requirements common in these sectors.
Tell us about your workflow and we will show you exactly where an AI agent can reduce manual workload, improve throughput, and deliver a measurable return in the first quarter.