The Sogeti GenAI Amplifier:

Introduction: Amplifying, Not Replacing

The discipline of Quality Engineering is transforming. Traditional practices must evolve as software systems become increasingly complex and development cycles accelerate. The Sogeti GenAI Amplifier for Software & Quality Engineering (GenAI Amplifier) embodies this shift by integrating Generative AI into the heart of the software development lifecycle (SDLC), not to replace the human expert, but to amplify their capabilities. This approach aligns with a core principle of modern quality engineering: automation should support expert judgment, not override it.

Conceptual Foundation

The GenAI Amplifier is a modular, scalable platform engineered to embed AI assistance into existing software and quality engineering workflows. It is designed to enhance human roles such as business analysts, testers, automation engineers, and product owners. In practice, this means automating repetitive, time-intensive tasks, providing intelligent recommendations, and offering real-time support while preserving the expert-in-the-loop principle that is essential for trust and quality assurance.

 

The platform leverages Retrieval-Augmented Generation (RAG), ensuring its AI-generated outputs are contextualized with enterprise-specific knowledge. The Amplifier operates as an orchestrated intermediary between human users and Large Language Models (LLMs), using pre-designed Gen AI workflows while preserving the expert-in-the-loop principle that is essential for trust and quality assurance. This foundation makes the Amplifier particularly effective in domains where accuracy, traceability, and domain alignment are critical.

TMAP Integration: Grounding in Proven
Practices

The GenAI Amplifier is infused with the TMAP body of knowledge for structured and adaptive quality engineering. TMAP emphasizes continuous quality, risk-based thinking, and teamwide collaboration, which are foundational for the GenAI Amplifier’s design and functionality.

Requirements Engineering Support

One of the Amplifier’s impactful applications is requirements engineering. The platform transforms diverse inputs, undocumented code, existing requirements, screenshots, and transcripts into structured requirements documentation and user stories.

 

Core capabilities include:

• Generating 9 types of requirements from diverse input sources,
• Assessing ambiguities with defect-level analysis,
• Producing high-quality user stories with optional Gherkin format,
• Creating consistent epics and features from fragmented stakeholder input,
• Delivering comprehensive requirements documents that facilitate shared team understanding.

 

The platform operates with dual-level RAG: embedded business analyst knowledge and industry standards within the platform’s knowledge system, plus user-specific RAG for enterprise contexts. Through systematic prompt engineering, outputs align with both established patterns and organizational requirements. By structuring requirements documentation from initial capture, the Amplifier helps reduce downstream anomalies, a fundamental “shift-left” principle, as also described in TMAP, that addresses quality concerns early in the development lifecycle.

Test Design and Test Generation

Test design demonstrates the Amplifier’s structured approach to TMAP practices. Testers begin with a defined test basis, business rules, requirements, user stories, or risk models and select between two primary approaches based on context and constraints.
Generic testing produces hand-picked test cases achieving moderate coverage through positive, negative, and boundary scenarios. This approach suits quick smoke testing, early iterations with volatile functionality, or teams with junior testers requiring low learning curves.

 

Coverage-based testing utilizes the platform’s Test advisory feature, which systematically analyzes the test basis using TMAP methodology and provides structured recommendations through embedded domain expertise within its RAG knowledge system. Test Advisory evaluates the test basis across four TMAP coverage groups: Process, Conditions, Data, and Appearance recommending appropriate test design techniques including decision tables, equivalence partitioning with boundary value analysis, process cycle testing, state transition testing, and modified condition decision coverage. Based on these recommendations, testers then generate comprehensive test cases using the platform’s pre-designed techniques for each coverage group, addressing regulatory requirements, complex rule engines, and scenarios demanding demonstrable traceability.
The platform operates through low-prompt and no-prompt workflows, interpreting system behavior and requirements to generate structured test cases. Testers retain control over approach selection, validation, and refinement. Coverage metrics refer specifically to the scoped test basis rather than entire system coverage, ensuring realistic and achievable quality targets.

Test Automation Script Generation

The Amplifier addresses test automation engineering through specialized, pre-designed use cases that generate production-ready automation scripts. Rather than generic code generation, the platform provides expert-crafted workflows for specific automation frameworks including Playwright, Selenium, Katalon, Robot Framework, and legacy system conversion scenarios.
Multiple use cases per technology are available, covering diverse automation requirements from web applications to API testing and database validation.

 

Each automation use case incorporates detailed technical instructions embedded within its RAG system, covering project structure, coding conventions, locator strategies, and framework-specific best practices. These workflows ensure generated scripts follow established patterns and maintain consistency across different automation tools and testing approaches.
These pre-curated workflows ensure generated scripts align with enterprise standards, coding guidelines, and architectural patterns rather than producing generic automation code. The platform handles complex scenarios while maintaining separation between test logic, configuration management, and data handling across structured project architectures.
The platform is currently extending to support Model Context Protocol (MCP) integration, expanding its automation generation capabilities. Generated automation scripts require expert review and validation, maintaining the automation engineer’s role as the technical decision-maker. This approach supports systematic test automation development while ensuring traceability, maintainability, and adherence to established testing methodologies.

Test Data Management

Another key area of support is test data creation. The Amplifier generates realistic, privacy-compliant, and scenario-specific data sets. This includes synthetic data generation for edge cases, boundary conditions, and exploratory testing.
In TMAP, high-quality test data is a cornerstone of effective testing. By simplifying and accelerating this aspect, the Amplifier helps teams to maintain testing rigor while reducing preparation time.

Real-Time Assistance and Human Oversight

The GenAI Amplifier provides real-time support during requirements analysis and design workshops. This live interaction model allows for immediate resolution of ambiguities or inconsistencies, reinforcing TMAP’s recommendation for early and continuous quality involvement.
However, the platform is intentionally designed with guardrails. These include audit trails, access controls, and explainability features. Such safeguards ensure that while AI can act autonomously for low-risk tasks, all critical outputs remain subject to human validation practice consistent with TMAP’s emphasis on controlled, adaptive quality measures.

Real-World Impact: A Use Case Perspective

In practical use, the GenAI Amplifier has demonstrated its value across multiple roles and scenarios. For example:

 

From raw input to structure: Stakeholder feedback was transformed into structured epics and features, enabling teams to accelerate backlog creation.

 

Quality gatekeeping: Missing or unclear requirements were flagged by the Amplifier before implementation began.

 

Accelerated test generation: Functional and non-functional test cases were generated automatically, reducing lead time while preserving coverage.

 

 Script automation: Automated test cases were produced with minimal prompting and are ready for validation and integration.

 

In all these cases, the AI acted as an intelligent co-pilot, and the human expert retained control. This symbiosis reflects the very idea of amplification: enhancing capability, not replacing it.

Productivity Measurement and Validation

The Amplifier employs a systematic measurement framework to quantify productivity gains across quality engineering activities. Rather than subjective assessments, the platform applies structured metrics that combine effectiveness and efficiency measurements to generate objective productivity scores that enable consistent evaluation across diverse use cases and organizational contexts.

 

Measurement Formula
Productivity assessment follows the formula:
Productivity = Effectiveness × Efficiency

Where:
Effectiveness = (Accuracy × 30%) + (Completeness × 25%) + (Structural Adherence × 15%) + (Clarity × 15%) + (Relevance × 15%)
Efficiency = Baseline Time ÷ Amplifier Time

 

Effectiveness: Quality Assessment Framework
Effectiveness measures the quality of output based on five essential
characteristics that collectively determine deliverable quality.
Accuracy (30% weighting) evaluates correctness and factual
precision of generated content, including technical accuracy, data
validity, and alignment with source materials.
Completeness (25% weighting) assesses whether all required
elements are present and sufficiently detailed, covering scope
fulfilment, requirement satisfaction, and comprehensive coverage
of specified topics.
Structural Adherence (15% weighting) examines conformity to
established templates, formats, organisational standards, and
methodological frameworks such as TMAP guidelines.
Clarity (15% weighting) measures readability, coherence, comprehensibility,
and logical flow of the output.
Relevance (15% weighting) determines alignment with specific
requirements, contextual appropriateness, and stakeholder
needs.
Each characteristic receives independent scoring against predefined
criteria, ensuring comprehensive quality assessment
without subjective bias. This framework deliberately excludes
other potentially relevant factors such as ethics, bias detection,
security compliance, system performance, or domain-specific
regulatory requirements that may apply in different organizational
or technical contexts.

 

Efficiency: Time Performance Analysis
Efficiency measures time performance by comparing total Amplifier
processing time against documented human baselines. The
calculation includes both pre-processing time (input preparation,
RAG configuration, parameter setup) and post-processing time
(output refinement, validation, formatting adjustments). Baseline
times derive from historical project data, expert estimations, or standardised benchmarks for equivalent tasks performed by skilled practitioners using traditional methods.
The efficiency ratio greater than 1 indicates Amplifier outperforms human baseline performance, whilst ratios less than 1 suggest traditional methods remain more time-efficient. This measurement accounts for complete end-to-end delivery time rather than isolated processing duration, providing realistic productivity assessment.

 

Final Productivity Scoring Framework
The combined productivity score provides standardised interpretation with specific performance thresholds:

P > 20: Excellent Performance – High-quality output produced significantly faster than human baseline, indicating optimal Amplifier utilisation with substantial productivity gains.

10 < P ≤ 20: Good Performance – Solid quality with notable time savings, representing effective Amplifier application with meaningful productivity improvements.

5 < P ≤ 10: Fair Performance – Acceptable results but improvements needed in either quality or speed, suggesting optimisation opportunities in prompt engineering, RAG configuration, or baseline accuracy.

P ≤ 5: Poor Performance – Low quality or significantly slower than human baseline, indicating inadequate Amplifier application requiring fundamental process adjustments, better input preparation, or alternative approach consideration.

 

Critical Measurement Considerations
Productivity depends on multiple factors: input quality and completeness, appropriate RAG enablement where applicable, clear task understanding and clarifications, independent scoring of attributes by qualified assessors, and reliable baseline accuracy established through historical data or expert validation.
Successful implementation requires establishing consistent evaluation criteria, training assessors on scoring methodology, maintaining updated baseline data, and accounting for learning curves in both human and Amplifier performance. The systematic approach ensures consistent productivity assessment across diverse quality engineering applications whilst maintaining objective evaluation standards and enabling continuous improvement through measured feedback loops.

Strategic Vision: From Tool to Capability Enabler

The GenAI Amplifier is not merely a point solution. It represents a strategic vision for the future of quality engineering. By embedding AI throughout the SDLC and grounding it in frameworks like TMAP, it supports:

 

1. Shift-left quality practices by catching issues early,
2. Continuous learning and improvement through feedback loops,
3. Team collaboration by democratizing access to quality engineering insights.

 

This vision aligns with DevOps and Agile principles, where speed, collaboration, and adaptability dominate. AI becomes a partner in this journey, not as a decision-maker, but as a force multiplier for human expertise.

Why It Matters

In today’s fast-moving digital landscape, organizations must deliver high-quality software rapidly and responsibly. The Sogeti GenAI Amplifier helps teams achieve this by operationalizing AI in a controlled, explainable, and team-centric manner.

 

It reduces manual overhead, improves precision, and fosters cross-role collaboration. By grounding its functionality in the TMAP body of knowledge and supporting low- to no-prompt interactions, the Amplifier represents a meaningful evolution in how quality engineering is practiced, not by disrupting existing roles, but by amplifying them.

Published: 30 April 2026
Author: Antoine Aymer
               CTO QE&Testing
               at Sogeti Global

This blog is a partner contribution to the “Amplified Quality Engineering” publication.