Intent
Set the brief
Knowledge Engine
Standards & Prompts
How it works
One calm delivery workflow from intent to release.
Productelligence helps teams understand the project, create the foundation, and manage change safely without adding more status-chasing.
The app stays grounded in the work you already have: docs, notes, Jira exports, design evidence, code context, approvals, and release signals.
9
Workflow stages
4
Role swimlanes
3
Primary outcomes
Workflow visual
The real operating loop inside Productelligence
This is not a waterfall. The system shows the main path, the feedback loops, the primary owner for each stage, and the modules that support the work.
Main flow
Feedback loop
Product
Sets intent, aligns tradeoffs, approves direction.
Design
Shapes flows, states, patterns, and content structure.
Tech
Tests feasibility, plans implementation, builds safely.
Delivery
Verifies readiness, evidence, approvals, and launch confidence.
Shape
Pressure-test the work
Knowledge Engine
Project Pulse
Design
Turn intent into flows
Design-to-Code Studio
Knowledge Engine
Define
Make it build-ready
Ticket Studio
Standards & Prompts
Plan
Sequence the work
Project Pulse
Ticket Studio
Build
Execute with context
Project Pulse
Knowledge Engine
Design-to-Code Studio
Validate
Test the risky parts
Ticket Studio
Release Engine
Review
Confirm confidence
Release Engine
Project Pulse
Release
Launch and learn
Release Engine
Knowledge Engine
Project Pulse
Stage to module map
How the system helps at each stage
Each stage keeps the language simple: what the team is doing, which module helps, what AI handles quietly, and what happens next.
Stage
Primary owner
Module support
Next action
Intent
Define goals, scope, constraints, audience, dependencies, and success.
Product
Needs input
Knowledge Engine
Standards & Prompts
Confirm project intent
Set the brief
Shape
Check feasibility across product, design, and tech before churn starts.
Product
Risk detected
Knowledge Engine
Project Pulse
Resolve shaping risks
Pressure-test the work
Design
Create flows, UI states, content structure, and reusable patterns.
Design
Ready for review
Design-to-Code Studio
Knowledge Engine
Review design coverage
Turn intent into flows
Define
Create requirements, tickets, acceptance criteria, dependencies, and notes.
Product
Needs input
Ticket Studio
Standards & Prompts
Validate requirements
Make it build-ready
Plan
Map releases, milestones, sprints, architecture order, and dependency timing.
Tech
Ready for review
Project Pulse
Ticket Studio
Lock the plan
Sequence the work
Build
Build with active clarification between engineering, product, and design.
Tech
Risk detected
Project Pulse
Knowledge Engine
Design-to-Code Studio
Review active risks
Execute with context
Validate
Focus QA, regression, accessibility, compatibility, and release evidence.
Delivery
Needs input
Ticket Studio
Release Engine
Close coverage gaps
Test the risky parts
Review
Run UAT, stakeholder review, approvals, and release confidence checks.
Delivery
Waiting on approval
Release Engine
Project Pulse
Request approval
Confirm confidence
Release
Ship, monitor, communicate, and feed learning back into the next cycle.
Delivery
Ready to ship
Release Engine
Knowledge Engine
Project Pulse
Publish release package
Launch and learn
01
Intent
Set the brief
Get the team aligned on what this project is really trying to do.
Knowledge Engine
Standards & Prompts
- PI reads automatically
- Briefs, notes, intake docs, prior decisions, Jira exports, and project evidence.
- PI analyzes quietly
- Goal gaps, conflicting constraints, unclear scope, and missing dependencies.
- PI suggests
- A cleaned-up project summary, open questions, and a first baseline.
- PI asks only if needed
- Only when key constraints, audiences, or success criteria are missing.
- PI stays quiet for
- Minor wording changes that do not change scope.
- Next action
- Confirm project intent
02
Shape
Pressure-test the work
Find the hard parts early enough to change course cheaply.
Knowledge Engine
Project Pulse
- PI reads automatically
- Intent baseline, linked docs, dependency history, prior delivery patterns.
- PI analyzes quietly
- Feasibility risks, missing owners, dependency pressure, and volatile assumptions.
- PI suggests
- Tradeoffs, scope cuts, sequencing fixes, and missing decisions to close first.
- PI asks only if needed
- Only when feasibility depends on an unknown assumption or external dependency.
- PI stays quiet for
- Routine discussion that does not affect feasibility or delivery order.
- Next action
- Resolve shaping risks
03
Design
Turn intent into flows
Make intent concrete enough for clean handoff and implementation.
Design-to-Code Studio
Knowledge Engine
- PI reads automatically
- Intent baseline, design evidence, existing patterns, and related decisions.
- PI analyzes quietly
- Flow gaps, state coverage, pattern reuse, and design-to-implementation risk.
- PI suggests
- Reusable patterns, missing states, implementation notes, and linked design evidence.
- PI asks only if needed
- Only when a flow choice changes scope or creates technical risk.
- PI stays quiet for
- Small visual refinements with no delivery impact.
- Next action
- Review design coverage
04
Define
Make it build-ready
Write tickets and requirements that engineering and QA can trust.
Ticket Studio
Standards & Prompts
- PI reads automatically
- Intent, shaping decisions, design evidence, standards, and prior ticket history.
- PI analyzes quietly
- Missing AC, contradictions, duplicates, undefined dependencies, and unclear language.
- PI suggests
- Draft requirements, cleaner AC, clearer ticket language, and validation fixes.
- PI asks only if needed
- Only when an unresolved decision blocks build-ready requirements.
- PI stays quiet for
- Cosmetic ticket edits that do not change intent or testability.
- Next action
- Validate requirements
05
Plan
Sequence the work
Put work in the right order so delivery does not stall later.
Project Pulse
Ticket Studio
- PI reads automatically
- Validated tickets, dependencies, milestones, release data, and drift signals.
- PI analyzes quietly
- Blocker chains, sequencing risk, milestone pressure, and scope volatility.
- PI suggests
- Safer sequencing, milestone adjustments, and tickets that need earlier clarification.
- PI asks only if needed
- Only when schedule choices depend on a tradeoff the team has not accepted.
- PI stays quiet for
- Normal status movement that does not change delivery risk.
- Next action
- Lock the plan
06
Build
Execute with context
Keep execution moving without drifting away from approved intent.
Project Pulse
Knowledge Engine
Design-to-Code Studio
- PI reads automatically
- Approved baseline, ticket set, linked design evidence, code context, and delivery signals.
- PI analyzes quietly
- Meaningful drift, contradictions, blocker growth, and implementation impact.
- PI suggests
- Clarifications, implementation proposals, and the few changes worth escalating.
- PI asks only if needed
- Only when a code or architecture choice changes the approved baseline.
- PI stays quiet for
- Every tiny code change or normal implementation progress.
- Next action
- Review active risks
07
Validate
Test the risky parts
Prove the work is ready without wasting time on low-risk noise.
Ticket Studio
Release Engine
- PI reads automatically
- Approved requirements, tickets, release checklists, known issues, and test evidence.
- PI analyzes quietly
- Coverage gaps, regression risk, accessibility risk, and missing release evidence.
- PI suggests
- High-risk test paths, missing cases, and what should block release.
- PI asks only if needed
- Only when the evidence is too weak to judge readiness.
- PI stays quiet for
- Low-signal test updates that do not affect release confidence.
- Next action
- Close coverage gaps
08
Review
Confirm confidence
Give stakeholders confidence without a long status meeting.
Release Engine
Project Pulse
- PI reads automatically
- Validation evidence, release packet, pulse summary, known issues, and approvals.
- PI analyzes quietly
- Approval blockers, unresolved risks, and release confidence level.
- PI suggests
- A concise approval packet, known-issue view, and what still needs sign-off.
- PI asks only if needed
- Only when an approver must resolve an explicit go/no-go tradeoff.
- PI stays quiet for
- Routine commentary once the evidence already says the same thing.
- Next action
- Request approval
09
Release
Launch and learn
Launch with evidence, then bring the learning back into planning.
Release Engine
Knowledge Engine
Project Pulse
- PI reads automatically
- Approved release data, launch notes, known issues, project memory, and pulse signals.
- PI analyzes quietly
- Readiness, post-release risk, open follow-ups, and future planning signals.
- PI suggests
- Release notes, confidence summary, known issues, and next-cycle learnings.
- PI asks only if needed
- Only when launch risk crosses an agreed threshold.
- PI stays quiet for
- Every small post-launch signal unless it changes release confidence.
- Next action
- Publish release package
Three simple ways teams use PI
Keep the product simple at the top layer
Under the hood there are multiple modules. At the UX layer, the product should still feel like three clear jobs.
Understand the project
Bring evidence together, remove ambiguity, and make the current state obvious.
Workflow stages
Intent
Shape
Design
Module support
- Knowledge Engine
- Project Pulse
- Design-to-Code Studio
Create the foundation
Turn intent into build-ready requirements, sequencing, and shared delivery structure.
Workflow stages
Define
Plan
Module support
- Ticket Studio
- Standards & Prompts
- Project Pulse
Manage change safely
Track drift, validate risk, keep approvals clear, and ship with evidence.
Workflow stages
Build
Validate
Review
Release
Module support
- Project Pulse
- Release Engine
- Knowledge Engine
Mode awareness
What PI knows, what mode you are in, and what it can do
The system should make project state visible before it starts suggesting changes. Users should not have to infer the rules.
Visible state, not assistant chatter
Show the project mode, connected evidence, approved baseline, risk state, and next best action in the page chrome. Keep generic chat hidden unless the user explicitly opens it.
Project mode
- Import: bring in existing docs, Jira exports, and historical evidence.
- Connected: work against live project memory with linked sources and approvals.
- Greenfield: start with a brief, standards, and proposed implementation structure.
Connected sources
- Docs and notes
- Jira exports and ticket history
- GitHub and code context
- Figma and design evidence
- Decisions, briefs, and release artifacts
Approved baseline
- Current approved intent
- Build-ready requirement set
- Tracked decisions and standards
- Release packet and sign-off state
AI permission boundary
- Analyze only
- Propose changes
- Approval-required write
Helpful by default. Chatty only when asked.
- One primary CTA per step.
- One next action for the current user state.
- No generic AI chat panel by default.
- Alerts only for high-signal issues.
- Digest summaries instead of constant interruptions.
- Advanced controls collapsed into secondary space.
Suggested labels and microcopy
Needs input
Ready for review
Risk detected
Waiting on approval
Ready to ship
Good prompts
- Review the three blockers affecting this release.
- Approve the current baseline before planning changes.
- Add the missing design state before engineering starts.
Avoid
- Generic assistant chatter.
- Notifications for every small update.
- Asking users to restate what the evidence already says.
Less ambiguity. Better handoffs. Clearer confidence.
- Less ambiguity because intent, design, tickets, and decisions stay linked.
- Less rework because weak requirements get pressure-tested before build churn starts.
- Less status-chasing because PI summarizes the project state with cited evidence.
- Fewer late surprises because drift, blocker chains, and missing approvals stay visible.
- Better handoffs because each stage has a clear owner, baseline, and next action.
- Clearer accountability because approvals and release confidence are explicit.
Module library
Ticket Studio
Create and validate requirements, tickets, dependencies, and acceptance criteria before work moves downstream.
Project Pulse
Watch blockers, drift, volatility, milestones, and sequencing risk without turning the app into a noisy dashboard.
Knowledge Engine
Keep docs, notes, decisions, code context, and cited answers in one durable project memory.
Release Engine
Create review packets, checklists, known-issue views, and release outputs with approvals.
Standards & Prompts
Set DoR, DoD, templates, prompt profiles, alert tuning, and other project rules.
Design-to-Code Studio
Link design evidence to implementation proposals and preserve design intent through handoff.
Run this in the app
Use the workflow as the entry point, then drop into the right workspace
The workflow gives teams a shared frame for project state, then the app opens the right workspace for the current delivery problem without losing evidence, approvals, or traceability.
Available in the app today
Start with the workflow question
Teams usually begin in the project home, ingestion, briefs, or the module that matches the current delivery problem.
Open the focused workspace
Work then moves into Ticket Studio, Project Pulse, Knowledge Engine, Release Engine, Standards & Prompts, or Design-to-Code Studio.
Follow evidence to action
Users move from source evidence to recommendation, then to approval-gated outputs without losing traceability.
Why workflow-first helps
Workflow view as the default entry
Lead with the current stage, risk state, approved baseline, and one next best action before users jump into deeper tools.
Focused workspace per stage
Each stage opens a workspace that keeps evidence, risks, approvals, and linked modules in the same frame.
Module jump-links, not module-first navigation
Let experienced users jump straight into tools, but always preserve a path back to workflow context.
01 · Intent
Intent workspace with brief summary, source evidence, and open gaps
Entry point
Project home, intake, briefs, and knowledge import
Primary action
Approve intent summary
Secondary action
Add source evidence
AI summary card
What PI knows, what is still unclear, and what should be locked next.
Evidence view
Brief, notes, docs, decisions, Jira import summary
Risk state
Scope ambiguity, hidden dependency, missing decision
Upstream links
Imports, prior releases, source docs
Downstream links
Shape, Design, Define
Done state
Intent approved and baseline captured
02 · Shape
Feasibility review with blockers, tradeoffs, and dependency signals
Entry point
Pulse signals and linked intent evidence
Primary action
Decide scope or sequencing changes
Secondary action
Route an issue to the right owner
AI summary card
What could break later if the team keeps going as-is.
Evidence view
Dependency map, delivery history, cited risks
Risk state
Feasibility risk, missing owner, dependency blocker
Upstream links
Intent
Downstream links
Design, Define, Plan
Done state
Known risks accepted or reduced
03 · Design
Design coverage view with linked states, evidence, and implementation notes
Entry point
Design Studio intake and decision timeline
Primary action
Approve flow or state coverage
Secondary action
Generate an implementation brief
AI summary card
What the design now covers, what is still missing, and what engineering should know.
Evidence view
Figma references, design decisions, implementation brief
Risk state
Missing state, handoff gap, design drift
Upstream links
Intent, Shape
Downstream links
Define, Build
Done state
Design intent linked and review-ready
04 · Define
Define workspace with validation results, linked evidence, and next fixes
Entry point
Requirements, tickets, dependencies, and standards
Primary action
Approve build-ready ticket set
Secondary action
Generate draft tickets
AI summary card
What is ready, what is risky, and what still needs clarification.
Evidence view
Requirement sources, ticket drafts, standards, citations
Risk state
Ambiguity, contradiction, missing AC, untracked dependency
Upstream links
Intent, Shape, Design
Downstream links
Plan, Build, Review
Done state
Requirements approved and traceable
05 · Plan
Planning view with dependency order, risk summary, and readiness state
Entry point
Pulse, roadmap, milestones, tickets, and dependencies
Primary action
Approve milestone or sprint sequence
Secondary action
Open a blocker chain
AI summary card
What is on the critical path and where the plan is fragile.
Evidence view
Roadmap, milestone links, dependency graph, pulse signals
Risk state
Blocker chain, schedule pressure, volatility
Upstream links
Define
Downstream links
Build, Validate, Review
Done state
Plan agreed with dependencies visible
06 · Build
Execution workspace with drift, blockers, and approved context
Entry point
Execution console, code atlas, pulse, and linked design context
Primary action
Resolve a meaningful drift item
Secondary action
Open linked evidence
AI summary card
What changed, why it matters, and whether it threatens the baseline.
Evidence view
Ticket, code, design, decision, and signal context in one place
Risk state
Drift detected, blocker growing, contradiction found
Upstream links
Design, Define, Plan
Downstream links
Validate, Review
Done state
Implementation aligned with approved baseline
07 · Validate
Validation workspace with risk-ranked coverage and open evidence gaps
Entry point
Requirements validation, test cases, release checklist, known issues
Primary action
Approve or reject readiness gap list
Secondary action
Open linked release artifact
AI summary card
Where QA should focus now and what evidence is still missing.
Evidence view
AC coverage, test cases, release checklist, known issues
Risk state
Coverage gap, regression risk, missing evidence
Upstream links
Build, Plan
Downstream links
Review, Release
Done state
Risk-ranked validation complete
08 · Review
Review workspace with confidence summary, approvers, and blocking issues
Entry point
Release packet, confidence center, approval tasks
Primary action
Approve release packet
Secondary action
Share known issues
AI summary card
What is approved, what is still open, and what could still stop launch.
Evidence view
Approval packet, confidence notes, known issues, checklist status
Risk state
Approval blocker, unresolved issue, low confidence
Upstream links
Define, Build, Validate
Downstream links
Release
Done state
Release confidence approved
09 · Release
Release workspace with package, approvals, launch notes, and follow-ups
Entry point
Release Engine and post-release signal summary
Primary action
Approve and publish release output
Secondary action
Create follow-up intent
AI summary card
What shipped, what to watch, and what should feed the next cycle.
Evidence view
Release notes, checklist, known issues, launch evidence, follow-up graph
Risk state
Launch risk, unresolved issue, follow-up required
Upstream links
Validate, Review
Downstream links
Intent
Done state
Release published and learning captured