Quality Engineering Consulting QE Assessment and AI Transformation
Quality Engineering Consulting
Solution Overview
EPAM Quality Engineering Consulting is an advisory service that helps organizations assess, measure, pilot, and transform how quality is managed across their software delivery. Through data analysis and interviews, our Quality Architects diagnose quality gaps, design target-state solutions, and pilot them before scaling — giving leaders a clear, measurable path to higher quality releases and lower costs. Services range from rapid maturity evaluations to full-scale transformation programs, scaling from a single team to hundreds. Backed by 350+ assessments across industries, and recognized by Forrester, NelsonHall, and Gartner.
Customer Problem
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Quality lag increases rework, cost and brand risk
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AI adoption stalls without strategy, data quality and governance
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Fragmented testing practices slow releases and create blind spots
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No clear QE maturity baseline to guide investment
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Rising cost of quality from manual bottlenecks and flaky automation
EPAM Solution
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Conducts a data-led QE assessment to baseline maturity, analyze defects, coverage and automation health, then traces findings to actionable recommendations with ROI
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Provides rapid maturity assessments at scale using PRISM and AIRA to set organizational baselines and prioritize interventions
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Evaluates AI readiness, identifies high-impact use cases and designs governance to move beyond ad hoc experiments
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Runs AI enablement pilots that introduce agentic workflows with measurable productivity gains and a rollout plan
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Designs AI-native testing strategies for AI-infused applications, including ground truth datasets, evaluation metrics and automation approaches
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Delivers a governed AI transformation program using an AI Maturity Model with dedicated AI Coaches in Quality
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Key Differentiators
AI‑Powered Consultants
EPAM's Quality Architects bring deep hands-on QE expertise and architectural authority to design future-ready solutions
Data Driven Decisions
Up to 60% of our assessment effort is data analysis, enabling precise, data-backed decisions and action plans
Rapid Prototyping
Running POCs alongside assessment identify the right-fit tools and accelerators and drive measurable gains from Day 1
Benefits
Reduced Cost of Quality
Shorter cycles, fewer defects and less rework lower total cost
Faster Time to Market
Agentic and automated workflows compress testing cycles
Measurable Productivity Gains
Up to 30% gains across agentic QE workflows, as piloted
Clear QE Baseline and Roadmap
A single view of maturity, gaps and a sequenced plan to act
AI Governance and Adoption
Frameworks, controls and coaching to scale AI safely
Team Enablement and Handover
Coaching and knowledge transfer for sustainable change
Features
- Current State Analysis: End-to-end review from requirements to release through structured interviews and hard data analysis of defects, requirements, test cases, automation and CI/CD pipelines. Findings quantify leakage, coverage and waste to target quick wins and systemic fixes.
- Target State Design: Evidence-backed recommendations for processes, tooling, data and org design. Includes ideation workshops and, where relevant, proof-of-concept outputs to validate feasibility and value early.
- Actionable Backlog and Roadmap: A prioritized improvement backlog that blends quick wins and strategic initiatives, supported by an executive summary, a sequenced roadmap and a business case with ROI projections.
- Optional Proof of Concept: Focused POCs in AI enablement, test automation and test data management to de-risk direction and establish a foundation for scale in the client environment.
- Flexible Engagement Model: A consulting methodology and toolset that scale from a single team to large enterprises. Multidisciplinary squads include Quality Architects, Test Automation Architects, GenAI Engineers and domain specialists aligned to specific challenges.
Use Cases
Food Manufacturing
Problem Statement
Digital platform rollout for food processing lacked test automation, defect management, and system integration testing, risking delayed go-live across manufacturing plants.
Solution Proposed
5-week Quality Assessment has redefined test strategy with test automation, performance testing, and TDM. EPAM QE consultant implemented improvements with vendor teams through 5-month enablement.
Achieved results
200 defects found pre-release. Zero defect leakage. On-time go-live without testing delays.
Retail
Problem Statement
120 mixed squads (4-7 team members) across multiple vendors lacked structured AI workflows, limiting productivity and delivery velocity across BA and QA roles.
Solution Proposed
Single EPAM team of 17 experts created and piloted BA and QA agentic workflows, deployed AI agents at scale across all squads, trained teams on the EliteA platform, and built a measurement framework for adoption and quality tracking.
Achieved results
Up to 30% productivity increase per role. 62% efficiency gain in user story development. 53% productivity improvement in test case creation.
Financial Services
Problem Statement
Top-10 global bank with de-prioritized QA across a multi-vendor landscape faced fragmented practices, siloed operations, and limited visibility into product quality.
Solution Proposed
QE assessment across four platform verticals identifying quick wins in testing, automation, and data quality. Established a Quality Centre of Excellence with a new operating model, centralized reporting, and standardized practices across 50+ FTE.
Achieved results
£1M+ projected cost-of-quality savings. 3-year Quality Transformation program awarded. EPAM became sole EMEA testing services provider.
Healthcare
Problem Statement
Medical device software reengineering required a full regression test suite, but test cases could only be extracted from hundreds of training videos — each taking up to 3 days to convert manually.
Solution Proposed
Custom AI-driven solution using a cloud-native pipeline with RAG-enhanced contextualization, duplicate detection, and LLM-based quality validation to transform videos into atomic regression test cases automatically.
Achieved results
Video-to-test conversion reduced manual efforts from 3 days to 2 hours with ~80% savings on manual labor. Single AI-enabled engineer now covers the workload of 3 regular engineers.
Insurance
Problem Statement
Claims payment platform serving 200+ carriers relying on manual testing was unable to keep pace with release volume, resulting in incomplete coverage, defects leaking to production, and excessive rework.
Solution Proposed
Test automation assessment followed by a SWAT team implementing automation first transformation with 95% automation coverage, metrics-driven QA processes. AI transformation has enabled AI agents for 11 high-impact use cases and trained 29 team members.
Achieved results
Test cycle reduced from 9 to 2 days. 70% fewer production defects. 100% in-sprint automation. Up to 75% individual productivity gain with AI and 30% Team productivity gain.
Additional Information
Awards
Forrester Wave Leader, Q2 2024
Named a Leading Provider of Continuous Automation and Testing Services in The Forrester Wave, recognized for QE advisory, continuous testing, AI Testing CoE, and globally scaled delivery capabilities.
NelsonHall NEAT Leader, Q4 2025
Recognized as a Leader in Quality Engineering Services by NelsonHall NEAT 2025, praised for in-house resources, open-source accelerators, AI-driven testing platforms, and strong test design and automation engineering.
Gartner Emerging Leader in Innovation Guide, Q3 2025
Named an Emerging Leader in Generative AI Consulting and Implementation Services, recognized for disruptive genAI capabilities, cost-effective service delivery without compromising quality, and a differentiated talent strategy.
Forrester AI Consulting Landscape, Q3 2025
Included in Forrester's overview of 37 AI Consulting Services providers, recognized for helping AI leaders define business value, design the AI experience, and prove AI ROI across NA and EMEA markets.Questions & Answers
What is included in the QE Assessment?
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