Requirement intelligence
Convert source material into reviewable, testable delivery tickets.
One quality engineering system
Connect intent, validation, execution, and evidence.
QACkt coordinates specialized agents across the complete quality lifecycle while preserving human approval, source lineage, and enterprise controls.
3specialized quality suites
7governed workflow stages
1shared evidence model
Capabilities
A complete working system, not an isolated generator.
Functional validation
Design, automate, execute, and diagnose API, web, and mobile behavior.
Performance engineering
Model realistic demand and explain system behavior under load.
Governed orchestration
Route work through policies, approvals, and typed agent contracts.
Quality knowledge
Ground every output in source, execution evidence, and graph context.
Deployment choice
Run privately or use a managed cloud with dedicated tenant options.
Operating flow
Every stage is visible and reviewable.
Inputs, decisions, evidence, findings, approvals, and artifacts remain available throughout the workflow.
Understand
Assemble source and operational context.
Generate
Create typed quality assets.
Critique
Find ambiguity, gaps, and unsafe assumptions.
Correct
Apply evidence-backed revisions.
Approve
Enforce accountable human control.
Execute
Run isolated validation workloads.
Analyze
Explain results and publish evidence.
Platform design
Three suites connected by one quality circuit.
Teams work in focused spaces while shared context, human control, and evidence keep the entire quality lifecycle connected.
01Suite experiences: Focused workspaces for requirements, functional quality, and non-functional quality.
02Quality circuit: A visible path from understanding and generation through approval, execution, and learning.
03Human control: Roles, policy, and accountable decisions remain present throughout the workflow.
Specialized workspaces share context, control, evidence, and learning.
Shape ideas into clear, reviewable delivery intent.
Design coverage, automate checks, and understand outcomes.
Model system qualities, validate behavior, and guide improvement.
Governed quality workflow
1Understand
2Generate & critique
3Approve & execute
4Analyze & learn
Roles, policy, and approvals guide consequential actions.
Requirements, tests, runs, findings, and decisions stay connected.
Every cycle produces traceable artifacts and reusable insight.
Engineering outcomes
What this changes for delivery teams.
Shorter path from product intent to executable proof
Consistent quality assets across teams
Auditable AI decisions and external actions
Reusable evidence across releases
Working session
Explore One quality engineering system with your own delivery context.
Bring one requirement set, workflow, or performance concern. We will map the governed path from source to executable evidence.