Describe your system. Turn its context into the guarantees, risks and tests that matter.
QA Hub helps teams define quality for their particular product and choose an actionable test strategy. Specialist knowledge and technical constraints are brought together behind a guided experience.
Functional product POC Architecture defined · implementation roadmap
A music recommendation service, a claims workflow and a generative assistant each need their own definition of quality. Teams must connect domain knowledge, product expectations, operating conditions and verification methods before choosing tests.
QA Hub brings that expertise into the workflow so users can focus on their system and the decisions they need to make.
Product definition, development of the product-experience POC, and formalisation of the architecture: knowledge-pack contracts, composition rules, decision records, expert arbitration and a validation protocol.
The working POC demonstrates the product experience. The defined architecture provides the implementation path for the composition engine and traceable decision records.
Capture the sector, system type, execution context, device and constraints. The user confirms the structured context and its assumptions.
The Quality Model Engine selects versioned Quality Model Packs and combines applicable risks, properties, metrics and methods through deterministic rules.
Turn the model into priorities, verification criteria, tools and artefacts. Surface incompatible requirements for explicit arbitration.
The Quality Evidence Layer records the context, selected packs, references, assumptions, conflicts and decisions in a versioned decision record.
These contracts form the specified decision architecture. Each pack carries specialised knowledge, curated references and an explicit description of its coverage.
A mobile music app recommends tracks and starts playback. The team needs useful recommendations, reliable listening and an acceptable experience under real network conditions and load.
The relevant model combines music and AI/recommender knowledge with mobile and production constraints.
Define relevance criteria with the product team and select datasets, properties or human judgements that can evaluate them.
Exercise playback through device and network changes, including interruption and recovery.
Measure response times and errors under the agreed workload, and connect them to the affected user journey.
A recommendation method may exceed the mobile response budget. The proposed strategy should expose that conflict, retain the contributing requirements and let the team decide how to resolve it.
Knowledge coverage identifies the dimensions supported by the available packs. A partially supported domain carries its missing dimensions into the decision record.
The record links a recommendation to the confirmed context, the contributing pack and its version, and the curated references supporting it.
An expert can adjust priorities or constraints through the defined arbitration rules. The reason, affected requirements and approval remain attached to the decision.
The specified engine composes a confirmed context and versioned knowledge through deterministic rules. AI assists context extraction and can personalise downstream artefacts. A guided form and structured templates provide fallback paths.
Provider substitution, persistent checkpoints and output validation are part of the target architecture for continuity.
The Evidence Layer makes references, assumptions, coverage and expert choices inspectable. Its first implementation is the next product step.
The longer-term vision connects applications, DOM, APIs, documentation, Jira, Confluence, tests and traces to map real journeys and their verification evidence.
A four-question intake covers sector, system type, execution context and device. Five sectors have contextual strategies and structured, exportable artefacts.
The POC uses parameterised templates and simulated agent activity to demonstrate the experience.
The pilot protocol compares composed quality models with an expert prompt and a generic risk-based checklist, using practitioner assessment of useful coverage, relevance, actionability and traceability.