Automated Testing for Enterprise AI/ML Applications
We help you build reliable, explainable, and audit-ready AI, empowering teams to scale innovation with speed and assurance.
Overview
From Variability to Trust: Guardrails for Enterprise AI
Our Automated Testing for Enterprise AI/ML spans readiness, monitoring, governance, and CI/CD. We tackle manual validation delays, fragmented QA, bias, drift, and governance gaps by integrating automated checks across training, validation, and deployment. Â
Using frameworks like Deepchecks, SHAP, AIF360, Evidently, and our DevRev-driven orchestration, we apply compliance-ready templates aligned to Responsible AI. Â
With role-based dashboards and BFSI-ready workflows, QK’s QA toolkit keeps models audit-ready, regulator-trusted, and delivers faster, safer releases with transparency and control.
Thought Leadership
See Results in What Matters
Focus areas
Making AI/ML Apps Trustworthy
Assessment & Roadmap
Our scorecard-based assessment benchmarks AI/ML QA maturity across people, processes, and tools with compliance mapping to ISO/IEC 42001 and Responsible AI. Â
Strategy workshops, gap analysis and stepwise improvement roadmaps align IT, data, compliance, and QA teams for scalable, trustworthy AI in regulated industries.Â
Reporting & Governance
Tool-agnostic governance simplifies audits and compliance reporting, with dashboards for ML and LLMs plus BFSI-ready checklists. Â
Coordinated bias and drift governance keeps models audit-ready, aligns stakeholders, and drives stronger ROI with fewer post-release failures.Â
Automated QA for CI/CD Pipelines
We hardwire trust into MLOps pipelines with bias, drift, and data-quality checks. With test generation across ML and LLMs, every pull request triggers instant QA snapshots with automated coverage and approvals and deployment of continuous quality gates, versioned checkpoints, to achieve faster, safer releases.Â
Enterprise Model Monitoring
We deliver real-time monitoring with built-in bias tests, drift and hallucination detection, and automated retraining triggers.Â
Multi-model dashboards and industry checklists keep governance measurable, reduce business risk, ensuring models stay audit-ready and production-strong.Â
Features
Pick a feature or go full suite
Plug-and-play QA for LLMs and classical ML models
CI/CD-Ready with Jenkins, GitLab & Azure DevOps
Bias, drift, and explainability tests (SHAP, AIF360, Deepchecks)
Real-time monitoring with drift and hallucination alerts
BFSI-compliant templates for fairness and robustness
Multi-model dashboards for QA, data science, & compliance across use cases
Real-time monitoring with drift and hallucination alerts
Customer Benefits
CI-Ready Testing for Speed, Scale, and Compliance
Inputs Awaited
50%-70%
faster release cycles with automated model validation
Reduced drift-related production failures by 60%
CI-integrated tests for data quality, performance, and bias
Role-based dashboards for scalable QA governance
Auto-generated test coverage for ML models and LLMs
Responsible AI–aligned checklists for audit-ready compliance
SUCCESS STORIES
Challenges we’ve solved for clients
SUCCESS STORIES
Challenges we’ve met
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