Requirement Intelligence
Convert fragmented source material into grounded, testable, reviewable delivery tickets.
- Ambiguity and gap detection
- Acceptance criteria and NFRs
- Versioning and change impact
Autonomous quality engineering
Turn product intent into executable confidence.
QACkt connects requirements, functional validation, performance engineering, execution evidence, and release intelligence in one governed quality circuit.
Private enterprise or managed cloud. Local and cloud AI. Human control where consequences matter.
Quality circuit / Release 7.2 Agents active
Requirement agent8 tickets grounded / 2 findings ready
REVIEWFunctional agent68 cases / 14 scripts validated
RUNNINGPerformance agentCampaign peak model awaiting approval
READYThree connected suites
One circuit from intent to operational evidence.
Each suite has specialized skills and guardrails. Together they preserve lineage across planning, validation, execution, and learning.
Functional Testing
Design risk-aware coverage, generate API and UI automation, execute, and diagnose failures.
- Web, mobile, and API
- Automation and test data
- Execution evidence and analysis
Performance Engineering
Model production-like demand, generate safe workloads, and explain system limits.
- HAR, logs, and API collections
- Workload and script generation
- Bottleneck and capacity analysis
Platform design
Specialized suites. Shared context. Governed action.
QACkt brings requirement, functional, and non-functional quality work into one connected system without hiding decisions or separating evidence from outcomes.
01Focused experiences for each quality discipline
02One visible workflow from intent to analysis
03Shared evidence that improves every release
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.
Visible agentic workflow
Automation you can inspect, interrupt, and approve.
The platform represents each stage explicitly. Teams see current work, evidence, findings, next actions, and approval ownership rather than waiting on a black box.
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.
Quality command center
One operational view of release confidence.
A realistic, role-aware workspace brings requirement health, automation coverage, performance evidence, quality risks, queues, agent state, and release readiness together.
Living quality flowUpdated 3 minutes ago
1IntentComplete
2RequirementsApproved
3Test designIn review
4Automation14 / 18
5ExecutionQueued
Quality risks2 blocking
Duplicate payment guardThreshold missing in RQ-483
HIGHSafari fallback coverageTwo journeys not automated
MEDIUMRisk service degradationWorkload approval pending
MEDIUMExecution agentStatusCurrent workElapsed
Requirement criticComplete8 tickets / 2 findings01:42
Functional automationRunningPlaywright validation06:18
Performance modelerReadyCampaign peak model02:09
Execution agents
Domain-specialized agents, not one overloaded assistant.
Each agent owns a defined quality capability contract and requests bounded tools through the message backbone.
Requirement agent
Extracts, critiques, corrects, versions, and maps grounded delivery requirements.
Functional agent
Designs traceable coverage, automation assets, execution requests, and failure analysis.
Performance agent
Models workloads, generates safe scripts, and correlates bottlenecks with telemetry.
Knowledge tools
Provide scoped hybrid retrieval, graph context, and evidence sufficiency metadata.
Integration tools
Perform approval-aware import and writeback through typed provider contracts.
Execution fabric
Runs approved assets in isolated environments and captures complete artifacts.
Deployment
Choose who operates the platform without changing how teams work.
Keep infrastructure, databases, models, storage, and secrets inside your environment, or use a managed QACkt deployment.
Customer-controlled
Designed for regulated environments, private networks, data residency, and customer-operated AI.
- Kubernetes and OpenShift direction
- Private cloud, data center, or air-gapped
- Customer-owned data, models, storage, and secrets
- Dedicated observability and operations
QACkt-operated
Accelerate adoption with managed infrastructure and tenant isolation, with a dedicated tenant option.
- Managed upgrades and platform operations
- Multi-tenant application architecture
- Subscription-based consumption
- Dedicated tenant option
Enterprise controls
Autonomy bounded by identity, policy, and evidence.
Scope isolation
Resolve tenant, project, role, environment, and run context on every path.
Secret references
Keep raw provider and integration credentials outside APIs, queues, and agent context.
Approval checkpoints
Pause state-changing AI actions and external writeback when policy requires human control.
Model governance
Control providers, models, route classes, task types, budgets, and fallback behavior.
Audit metadata
Track actor, policy, provider, model, tokens, latency, cost, target, and outcome.
Evidence controls
Apply provenance, filters, conflict detection, schema checks, and retrieval sufficiency.
FAQ
Questions engineering and architecture teams ask first.
No. QACkt accelerates evidence assembly, generation, critique, execution, and analysis. Accountable decisions and consequential external actions remain governed by roles, policy, and approval checkpoints.
The Private Enterprise deployment direction supports Kubernetes, OpenShift, private cloud, data center, and air-gapped patterns with customer-owned databases, storage, secrets, and AI models.
The architecture combines scoped retrieval, source provenance, schema validation, conflict detection, sufficiency checks, typed outputs, critique/correction stages, and human review.
No. The AI execution boundary is provider-neutral and designed for local models, approved cloud providers, and bring-your-own-key inheritance from platform through run scope.
The product direction includes Playwright, Selenium, Cypress, Appium, Maestro, JMeter, k6, Gatling, Locust, and Artillery through explicit framework contracts. Availability depends on the deployed release.
Working session
See the quality circuit working on your delivery context.
Bring one requirement set, workflow, or performance concern. We will map the governed path from source to executable evidence.