WorkSmart Docs
Strategy documentation

Product Roadmap

Where WorkSmart goes next — Now / Next / Later, the technical evolution from deterministic mock AI to real LLMs behind an unchanged seam, and three forward-looking UI concepts.

Shipped through Phase 5 Direction time-tracking → project control system Concepts non-functional UI mockups

Contents

  1. Now
  2. Next
  3. Later
  4. Technical evolution
  5. Concept mockups

01Now

Shipped and in use through Phase 5.

Shipped

Time tracking, with depth

The check-in grammar, multi-user data, keyset pagination, and a virtualized list that stays fast at 1,000+ entries; insights by tag, date, department, and user.

Shipped

Auth, roles, and the GenAI seam

Clerk authentication with an offline dev bypass and access roles; the five AI touchpoints wired through the api — Categorization, Document Analysis, Search, Anomaly Detection, and the Project Status Narrative.

Shipped

Documents and project management

Presigned, direct-to-storage document uploads with status tracking and check-in linking; projects and tasks on a board, with logged time rolling up through a nullable task link.

Shipped

Containerized deploy

The whole stack as one docker compose shape on a single EC2 host, with a manual-trigger CI gate and an OIDC → ECR → SSM deploy.

02Next

The near-term build — deepening the control-system foundation.

Next

Admin portal

Role-gated team analytics, member and access-role management, and a document approval queue — see the concept below.

Next

Lower-friction logging

Freeform and voice check-in: describe a day in plain language (or speak it) and have the AI split it into structured, confirmable entries.

Next

Real LLM inference

Swap the deterministic mocks for real models inside tm-worksmart-ai-service, behind the existing contract — see the technical evolution.

03Later

The full project-control-system vision.

Outcome / confidence layer

Track confidence in hitting outcomes, not just task status.

Cross-team dependency control

Make inter-team dependencies explicit and manageable.

Capacity-realism engine

Ground plans in logged-hour reality so commitments are achievable.

Native RAID + decision log

Risks, assumptions, issues, dependencies, and decisions captured in-product.

04Technical evolution

From deterministic mock to real LLMs — without touching the rest of the app.

Every AI feature already runs end-to-end through one seam: shared Zod contracts → the api's aiClienttm-worksmart-ai-service. The "mock" is deterministic inference, not fake plumbing. Becoming real means changing logic inside the service while the wire contract, the api routes, the validation, and the UI all stay exactly as they are.

web + api unchanged Zod contract packages/types/ai.ts · unchanged ai-service deterministic → LLM model provider new, behind the service
Why the seam holds

The api is the only caller of the service and validates every response against Zod before trusting it. Graceful degradation (timeout → 504, upstream → 502) already exists, so a real model's added latency or flakiness is handled the same way the mocks' is today.

05Concept mockups

Forward-looking UI — non-functional concepts, not shipped features.

Freeform smart check-in concept
Freeform check-in — describe your day in plain language; the AI splits it into structured, taggable entries you confirm. No grammar to learn.
Voice check-in concept
Voice check-in — tap, talk, done. Log time hands-free from the floor or between meetings; transcription and parsing run server-side, confirmed before logging.
Admin portal concept
Admin portal — admin-only team capacity analytics, member and access-role management, and a document approval queue.
Status

These three screens are non-functional concepts that illustrate direction. The shipped product is documented in the User Guide; the live interactive versions of these mockups live alongside the source.