Project Test-case AI is an internal AI platform developed by MOHA to transform how product teams design, review, and maintain software test suites. By combining an ISTQB-compliant knowledge base with a fine-tuned large-language model (LLM), Test-case AI produces reusable, standards-aligned test cases in minutes, freeing engineers for higher-value quality work. A forthcoming version focuses on greater precision, multilingual support, and template-as-code scalability—demonstrating MOHA’s commitment to embedding AI directly in production workflows.
Business Problem
Pain Point | Impact Before X |
Skill gap in structured testing | Edge-case coverage was inconsistent, leading to re-work and support overhead. |
Manual, repetitive authoring | Writing cases from scratch each sprint drained team time and morale. |
Inconsistent documentation standards | Review cycles slowed and cross-team reuse was limited. |
Knowledge silos | Valuable lessons remained trapped in local files instead of being shared across projects. |
Solution Overview
Test case AI integrates three pillars:
- Curated Knowledge Base – More than a hundred gold-standard templates covering functional, non-functional, performance, security, accessibility, and usability scenarios, authored under ISTQB guidelines.
- LLM Engine with Prompt-Engineering Layer – A fine-tuned model that generates complete test cases (title, preconditions, steps, expected results, data, priority) from screen mock-ups, feature descriptions, and desired test depth. Retrieval-augmented generation ensures outputs are grounded in approved templates.
Collaborative Web Application – Users upload feature specs, receive draft suites within seconds, review or edit as needed, and commit final cases to a central repository automatically linked to CI pipelines.
Technology Stack
Layer | Technologies | Rationale |
Front-end | React, Tailwind | Fast diff view, inline editing, role-based access. |
API | FastAPI (Python) | Lightweight, async, integrates cleanly with LangChain. |
Orchestration | LangChain, Qdrant | Hybrid keyword–vector retrieval for domain-specific prompts. |
Models | Fine-tuned GPT-4o with on-prem fallback | Balances quality, cost, and data residency. |
Observability | Postgres, Prometheus, Grafana | Full traceability and quality dashboards. |
Change Management
- Ambassador network – Each squad designates a QA champion to coach peers and surface feedback.
- Hands-on workshops – Short sessions cover ISTQB essentials and prompt-engineering best practices.
- Visible dashboards – Real-time views of authoring-time saved and coverage improvements encourage adoption.
Governance council – Monthly reviews maintain vocabulary consistency and risk-based prioritization.
Roadmap
- Self-healing suites that update automatically when requirements change.
- Risk-based prioritization driven by defect history and telemetry.
- Synthetic test-data generation aligned with validation rules.
Template marketplace for vertical-specific packs (e.g., fintech, healthcare).
Business Impact
Early pilots show dramatic reductions in authoring effort and smoother standardization across squads. Teams onboard faster, escaped defects drop, and knowledge once locked in individual projects is now captured centrally for continuous reuse. By integrating AI into core QA workflows, MOHA accelerates delivery while strengthening product quality—a clear example of AI turning operational bottlenecks into strategic advantages.
Project Test case AI demonstrates MOHA’s practical approach to AI adoption: codify institutional knowledge, pair it with a purpose-built LLM workflow, and embed the result directly into production processes. The outcome is a scalable, intelligent test-case system that enhances efficiency, consistency, and team satisfaction—laying a strong foundation for MOHA’s future product-quality excellence.