# Praxis — Voice-first AI Apprenticeship Platform

**Milestone:** v0.1 (foundation)
**Status:** complete
**Autonomy:** full

## Vision

Praxis is a voice-first, AI-tutored skill platform for learners in resource-constrained environments. Instead of courses, videos, and quizzes, learners practice real job scenarios through real-time spoken conversation with AI tutors. The platform treats every learner as an apprentice to a master craftsperson — open the app, talk, do the job, get better at it.

**One-line pitch:** Praxis turns every smartphone into a master craftsperson that talks to you, challenges you, and helps you get good at your job.

## Objective

Build a voice-first AI apprenticeship platform where learners engage in spoken role-play scenarios with AI tutors, receive coaching debriefs, and progress via mastery gates — working on low-cost phones over constrained bandwidth.

## v0.1 Scope (Foundation)

v0.1 establishes the minimal viable voice loop on which all later capabilities build. v1.0 is reserved for a working, tested product; v0.1 is the foundation milestone.

**v0.1 in scope:**
- Phase 0: pre-execution (specify, clarify, research, plan, grill)
- Phase 1: minimal viable voice loop — one persona, one branching scenario, ASR + TTS round-trip (<600ms target), single learner state, Ollama-hosted LLM foundation

**v0.1 out of scope (deferred to later milestones):**
- Mastery scoring, competency rubrics, verifiable credentials
- Multi-language support (launch: Canadian English; French-Canadian noted for later)
- Employer / program dashboard
- Live Assist on-the-job companion mode
- WhatsApp / SMS bot, USSD fallback
- Drill Mode, Review Mode
- Open scenario authoring marketplace
- B2B SaaS
- Voice cloning of real individuals
- Early childhood education, medical procedures (permanently out of scope per PRD §11.6)

## Product Principles (non-negotiable)

1. **Voice is the primary interface.** Text is fallback, not default.
2. **Doing > Knowing.** Every session produces observable action, not passive consumption.
3. **One skill, one outcome.** Each path is a job someone can get.
4. **Works on a cheap phone, on 2G.** Engineering constraints are product features.
5. **The AI is a master, not a chatbot.** Personality, standards, opinions.
6. **Mastery gates progression.** Move on when you can do the thing.
7. **Failure is the curriculum.** AI provokes mistakes, then coaches recovery.

## Requirements (summary — see REQUIREMENTS.md for formal REQ-IDs)

- Voice conversation engine: real-time ASR + streaming TTS, <600ms round-trip, interruptible, persona switching
- Scenario engine: branching role-plays with failure-injection and dynamic difficulty (v0.1: one scenario)
- Learner state: progress, session history, mastery accumulation (v0.1: single-learner state, no mastery scoring yet)
- LLM foundation: Ollama-hosted open-weights models `gemma4:cloud` and `deepseek-v4-flash:cloud`
- Low-bandwidth surfaces (later milestones)
- Employer dashboard (later milestones)

## Constraints

- C-1 Voice is primary interface; text is fallback only
- C-2 Must work on $100 Android phone over 2G/3G
- C-3 Cost ≤ $3/active learner/month (target markets; v0.1 is Canada launch — relaxed for pilot)
- C-4 Audio-only in v1 (no large video assets)
- C-5 Open-weights LLM via Ollama catalog — `gemma4:cloud` + `deepseek-v4-flash:cloud`
- C-6 Domain safety guardrails + human-in-the-loop + disclaimers for safety-sensitive domains
- C-7 Scenarios authored by domain experts + learning designers; AI generates variations only
- C-8 Latency budget < 600ms end-to-end (ASR → LLM → TTS)

## Key Decisions

| ID | Decision | Rationale | Confidence | Alternatives |
|----|----------|-----------|------------|--------------|
| D-001 | Launch market = **Canada** (path: Customer Service) | User-directed; Canada as initial market for v0.1 pilot. PRD named Kenya — overridden. | 0.70 | Kenya + Customer Service (PRD default) |
| D-002 | Milestone = **v0.1 foundation** (v1.0 reserved for working/tested product) | User-directed; v0.1 is the foundation slice (Phase 0 + Phase 1 minimal voice loop). v1.0 is a future milestone. | 0.90 | v1.0 = Phase 0 + Phase 1 (too ambitious for first milestone) |
| D-003 | LLM foundation = **Ollama catalog** — `gemma4:cloud` + `deepseek-v4-flash:cloud` | User-directed; open-weights via Ollama, two base models for edge/cloud split. Research phase to verify exact catalog IDs. | 0.75 | Llama-family, Mistral-family, Qwen-family |
| D-004 | Defer monetization model decision to Phase 1 | PRD §11.5 explicitly lists this as a Phase 1 decision (B2C paid, B2B per-seat, donor-funded, government). | 0.85 | Decide now (insufficient data) |
| D-005 | Single-project mode | Fresh repo with one project; no multi-project need. | 1.00 | Multi-project mode |
| D-006 | "One persona" = one voice persona; scenario role-play uses the same TTS voice as mentor (no distinct character voice in v0.1) | Minimizes v0.1 surface area; PRD's full persona-switching (REQ-VOICE-06) is deferred. Same voice avoids a second TTS configuration to validate. | 0.70 | Two voices (mentor + character) — adds TTS config risk |
| D-007 | "Single learner state" = local single hardcoded profile, no auth, no multi-tenant; persisted via SQLite on-device (or local file fallback) | v0.1 is a pilot harness, not a production multi-user system. Auth/multi-tenant is a later-milestone concern. SQLite chosen as the default local store; research phase may refine. | 0.80 | In-memory only (no persistence), server-side Postgres (premature) |
| D-008 | Interruptibility = abort-and-yield (learner speech cuts AI TTS immediately, AI yields the floor, no pause/resume state machine in v0.1) | Matches real-conversation semantics per PRD §6.1; pause/resume adds state-machine complexity inappropriate for v0.1. | 0.75 | Pause/resume state machine |
| D-009 | Failure-injection hook = architecturally present (scenario declares a `failure_mode` field) but NOT actively provoked in v0.1 sessions | v0.1 validates the data model and one scenario's success criteria; provoking failures is a coaching-debrief feature tied to mastery (deferred). Hook present so Phase 2+ can activate it without schema change. | 0.70 | Active failure injection in v0.1 (couples to deferred mastery engine) |
| D-010 | v0.1 Canada Customer Service scenario = "Angry customer requesting refund on a damaged product" (retail context, single branch point) | Concrete, universally recognizable, low safety-risk (non-medical/non-electrical). One branch point (customer escalates vs accepts resolution) keeps scenario runtime minimal while exercising branching. | 0.65 | "Customer with wrong booking" (hospitality — less universal for Canada pilot) |
| D-011 | Coaching debrief = included in v0.1 as a single end-of-session text+voice summary (not the full PRD §5.1 multi-moment replay) | The debrief is part of the core daily loop and cheap to include at a basic level. Full replay/multi-moment coaching is tied to mastery (deferred). | 0.70 | Exclude debrief entirely (loses core loop identity), full replay (over-scoped) |
| D-012 | v0.1 cost ceiling = no enforced ceiling (pilot); architecture must not bake in assumptions that would prevent meeting ≤$3/learner/month post-pilot | C-3 is a target-market constraint. Canada pilot is a foundation/tech-validation milestone, not a unit-economics milestone. Logging actual cost per session is a v0.1 NFR to inform later milestones. | 0.85 | Enforce $3 ceiling in v0.1 (premature optimization, wrong market) |
| D-013 | ASR = **Deepgram Nova-3** streaming (cloud, WebSocket) | Research-verified: streaming-native, ~200-300ms first partial, accent-robust for Canadian English, first-class Pipecat integration, Canada data-residency available. Fallback: Groq-hosted Whisper. | 0.85 | whisper.cpp (breaks <600ms budget), OpenAI Whisper API (batch) |
| D-014 | TTS = **Cartesia Sonic** (cloud, ~120ms first audio) primary; **Piper** (self-hosted, ~80ms) fallback behind interface | Research-verified: Cartesia #1 on Speech Arena; Piper is open-weights post-pilot ≤$3/learner path. R4 risk: all-cloud path ~670ms — Piper local may be required for production v0.1 latency. | 0.80 | ElevenLabs (quality but higher latency/cost), Amazon Polly |
| D-015 | Client = **React + WebRTC** via Pipecat client SDK | Research-verified: Pipecat ships React/RN/Swift/Kotlin SDKs; web client = fastest v0.1 iteration, no app-store distribution, upgrades to React Native for Android later. | 0.85 | Python CLI harness (dev-integration only), native Android Kotlin (premature) |
| D-016 | Transport = **WebRTC** (UDP, sub-50ms audio); WebSocket dev fallback | Research-verified: WebRTC is Pipecat's production transport; adaptive bitrate, UDP. SSE/HTTP rejected (unidirectional/high overhead). | 0.85 | WebSocket-only (higher audio latency), custom raw HTTP/2 |
| D-017 | Orchestration = **Pipecat** (not custom, not Vocode) | Research-verified: 13.8k★, active, integrates Deepgram+Cartesia+Piper+Ollama natively, has VAD/interrupt/Flows for branching. Vocode stale since Nov 2024. Custom orchestration rebuilds solved problems. | 0.85 | Vocode (stale), custom from scratch |
| D-018 | Scenario format = **YAML DSL → Pydantic → Pipecat Flows** | Research-verified: YAML is human-authorable + diffable + supports comments (critical for learning-designer rationale per C-7); Pydantic gives typed runtime; Pipecat Flows consumes the schema for branching. JSON is wire format only. | 0.85 | JSON DSL (no comments), code-authored (couples authoring to engineering) |
| D-019 | v0.1 guardrail layer = **pluggable interface** with Customer Service ruleset implementation | Research: v0.1 is low-risk (Customer Service) but architecture must support pluggable guardrails for later high-risk domains (health/electrical). Ruleset: no legal/financial/medical advice, no real-company employee impersonation, stay-in-role, session-start disclaimer audio, no PII beyond hardcoded profile. | 0.80 | No guardrails (violates C-6), hardcoded non-pluggable rules (blocks future domains) |
| D-020 | LLM access = **Ollama Cloud direct API** (`https://ollama.com/api/chat` + `OLLAMA_API_KEY`) — no local daemon | Research-verified: `:cloud` tags are real Ollama hosted-inference on NVIDIA cloud partners. Direct API eliminates local-daemon deployment dependency. `gemma4:cloud` (256K ctx) → role-play fast path; `deepseek-v4-flash:cloud` (1M ctx, no-think mode) → debrief. Self-host `gemma4:e4b` is the post-pilot cost-reduction path. | 0.85 | Local Ollama daemon proxy mode (adds deployment dependency) |

### Confidence updates from research

| ID | Before | After | Reason |
|----|--------|-------|--------|
| D-003 | 0.75 | **0.95** | Both Ollama model IDs verified in catalog as real, current, cloud-hosted tags |
| D-007 | 0.80 | **0.90** | SQLite confirmed appropriate for v0.1 single-learner scale; no evidence favors alternatives |

## Target Users (v0.1 pilot: Canada)

| Persona | Description | Pain |
|---------|-------------|------|
| Aspiring Adebayo → "Aspiring Alex" | 19–28, Canada. Recent secondary school grad. Smartphone, limited data. Wants a service job. | Can't afford vocational school. Needs to actually do the job. |
| Upskilling Ursula → "Upskilling Uma" | 25–40, Canada. Retail, hospitality, healthcare. Wants promotion/new role. | No time for courses. Learns on the job. |
| Frontline Felix | Customer service / sales / field tech agent, hired recently. | Manager has no time to coach. Wants quick on-shift practice. |

## Success Metrics (Year-1 targets, post-v0.1)

| Metric | Target | Why |
|--------|--------|-----|
| Active weekly learners | 100k | Engagement, not downloads |
| Sessions per learner / week | ≥5 | Habit formation |
| Mastery rate per path | ≥40% completion | Real learning |
| Median session length | 6–10 min | On-the-go use |
| Cost / active learner / month | ≤$3 | Sustainable |
| Reported job/promotion outcome | ≥25% | North star |
| NPS (learner) | ≥50 | Word-of-mouth growth |

## Open Questions (for research/clarify phases)

1. Will learners talk to their phone in public? (earbuds + "no one will know" framing)
2. How to certify mastery credibly? (employer/agency recognition)
3. Domain safety minimum HITL for health/electrical scenarios
4. Voice cloning / impersonation disclosure
5. Monetization model (deferred to Phase 1)
6. Skills that should remain out of scope

## References

- PRD v0.1 (this document's source)
- ARCHITECTURE.md — system architecture
- ROADMAP.md — phase breakdown
- REQUIREMENTS.md — formal requirements with REQ-IDs