Fabriq Product Blueprint: From Workspace OS to Feature Delivery Loop
Fabriq is building a local-first execution system for product engineering: stable workspace assets, isolated feature execution, confirmation-driven closure, and auditable knowledge promotion.
Most dev tools optimize one moment: code editing, chat generation, test execution, or repo management.
Fabriq optimizes the full engineering loop.
Our blueprint is simple to describe but hard to execute well:
- Keep the product knowledge base and codebase stable at workspace level.
- Execute each initiative inside an isolated feature dimension.
- Use AI to accelerate decisions, but keep every critical mutation explicit and confirmable.
- Close feature work through a promotion ceremony that writes useful knowledge back to workspace assets.
This is how we turn "AI helped me code" into "the system keeps getting smarter after each feature."
1) Product Mission
Fabriq is a local-first desktop workspace for product engineering teams that need:
- deterministic operations (repo, stage, close)
- AI-accelerated analysis and planning
- auditable knowledge output
- repeatable feature execution
In short: Workspace launcher + Product cognition browser + Feature execution cockpit.
2) Core Model: Two Dimensions, One Loop
Fabriq treats engineering as two dimensions that interact through explicit contracts.
- Workspace dimension:
- long-lived product assets
- repos, docs, knowledge, audit timeline
- acts as the stable operating base
- Feature dimension:
- short-lived execution workspace
- scoped docs, tasks, context, reports, mounted repos/worktrees
- closed through explicit promote/merge decision
Why this matters:
- It makes blast radius visible before action.
- It allows stage gates and close checks to be enforceable.
- It keeps AI work contextual without contaminating base assets.
3) End-to-End User Journey
Phase A: Enter and Prepare Workspace
User can:
- open or initialize workspace
- inspect runtime mode and capability readiness
- add/sync repos and inspect repo/worktree state
Outcome:
- workspace base is healthy enough for feature creation
Phase B: Create and Scope Feature
User can:
- create feature with staged wizard and preflight checks
- choose related repos (or start exploration-first)
- define objective and initial stage
Outcome:
- feature execution unit is created with clear ownership and intent
Phase C: Execute in Feature Cockpit
User can:
- navigate docs/review/checks/edit surfaces
- attach agent and drive conversation against feature context
- run stage mutation with gate semantics
- produce outputs and evidence
Outcome:
- implementation and decision artifacts are generated in feature dimension
Phase D: Validate and Close
User can:
- run close preflight
- choose promote artifacts (decision notes, architecture notes, reports, cognitive unlink actions)
- close feature with promote
Outcome:
- promoted knowledge lands in workspace docs; feature execution becomes auditable history
4) System Blueprint (Execution + Trust)
UI shell
- launcher, dashboard, repos, knowledge, feature workbench, timeline, settings
- action guard and runtime diagnostics to prevent blind clicks
Service layer
- workspace/repo/feature/knowledge/pipeline/advisor services
- contract-first IPC boundaries
Engine and local fallback
- capability execution through typed contracts
- mode-aware availability matrix
- deterministic handlers for local-first guarantees
Agent and context channel
- backend registry (real + fallback)
- attach/send/detach flows
- context envelope handoff from knowledge to feature workbench
Audit and promotion
- close wizard and promote report
- timeline-level visibility of key feature outputs
5) Design Principles
- Local-first before cloud-dependent.
- Deterministic mutation before generative suggestion.
- Confirm-before-impact for high-blast actions.
- Explicit dimensions over implicit state coupling.
- Knowledge must be promotable, not disposable.
6) What Users Should Expect Next
Near-term product direction is focused on three things:
- unify capability behavior across runtime modes
- deepen feature validation and review closure
- strengthen knowledge promotion quality and discoverability
Fabriq is not just adding more AI panels.
We are building a compounding engineering system where each feature leaves the workspace better than before.
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