Parikshan
parikshan - testing, examination by trial · formerly AIDLC
AI across the software testing lifecycle, from authoring to release.
At a glance
- Category
- Software Testing
- Theme
- Software Delivery & Quality
- Industries
- Technology, Financial Services, Healthcare, Manufacturing
- Capabilities
- 5 composed
- Pairs with
- Nirmiti, Vedha
Who it is for
- Software engineering teams shipping on a fixed cadence
- QA and SDET groups bottlenecked on manual authoring
- Platform teams standardizing test quality across squads
Systems it sits on
GitHub / GitLab · Jenkins / Azure DevOps · Selenium / Playwright · JUnit / pytest
Pairs with
Nirmiti · Vedha
Explainer
Parikshan in four minutes
A narrated walkthrough of the problem, how Parikshan works, and what changes.
Parikshan is Wayam's AI software-testing accelerator: intelligence across the development lifecycle so teams generate tests, execute them in CI, triage failures, and close coverage gaps without drowning in manual QA. It fits the tools engineers already use and keeps quality moving at release speed.
The problem
Software teams drown in manual test authoring, brittle suites, and late defect discovery while release pressure keeps rising.
The outcome
Faster, higher-coverage testing across the lifecycle: generate, run, triage, and ship with AI in the loop.
How it works
How Parikshan works
- 01
Connect the stack
Hook into repos, CI/CD, and existing test frameworks.
- 02
Generate and extend
AI authors and expands tests from requirements, diffs, and prior failures.
- 03
Execute and triage
Suites run in pipeline; failures are clustered and ranked for humans.
- 04
Gate the release
Coverage and risk views show what is safe to ship.
Module 01
Test generation
Create and expand unit, API, and UI tests from requirements and code.
- 02
Execution & triage
Run suites in CI, cluster failures, and surface the defects that matter.
- 03
Coverage & quality
Track risk, coverage gaps, and release readiness across the lifecycle.
- AI in software testing
- LifecycleAI in software testing
- execution & triage
- CI-nativeexecution & triage
Integrations
Ideal for
- Software engineering teams shipping on a fixed cadence
- QA and SDET groups bottlenecked on manual authoring
- Platform teams standardizing test quality across squads
- Regulated software that needs auditable release gates
Capabilities
Parikshan composes these capabilities from the shared Agent Platform layer. The same atoms power the rest of the portfolio, so adopting a second accelerator reuses the integration work rather than repeating it.
Composition
Parikshan is assembled from shared platform atoms. Hover a partner to see what you reuse on the next deployment.
- Generative Design
- Designs, tests, and code generated from stated intent and checked against constraints before a human reviews.
- Workflow Orchestration
- Specialist agents coordinated across a multi-step process, each handing structured context to the next.
- Anomaly Detection
- Deviation found in high-volume signal without a human defining the threshold first.
- Risk Scoring
- Severity, likelihood, and exposure quantified consistently so prioritization survives an audit.
- System Connectors
- MCP-connected reads and writes against the ERP, MES, PLM, CRM, and warehouse systems already in place.
Reason
Act
Reason
Reason
Ingest
FAQ
Is Parikshan only for UI testing?
No. It spans unit, API, and UI testing across the software lifecycle.
Does it replace our test frameworks?
No. It works with the frameworks and CI you already run, and adds AI where authoring and triage slow you down.
Where does it sit in the SDLC?
Across it: authoring, execution, failure triage, coverage, and release readiness.
How do we start?
A fixed-scope pilot targets one critical pipeline or suite and proves cycle-time and coverage gains.