whetstone_RSA/docs/legache_smartwatch_mvp_proposal.md

# Legache Smartwatch MVP Proposal

This document proposes a narrowly scoped MVP for exploring a collaboration
between Legache and `whetstone_RSA`.

The goal is not to build general-purpose AI on a smartwatch.

The goal is to validate whether a **bounded local knowledge interface** can
deliver useful enterprise value on wearable hardware with local latency and a
tightly constrained semantic interpretation layer.

---

## 1. MVP Goal

Build a smartwatch-scale interface that allows a user to issue short voice or
text requests against a locally synced Legache knowledge corpus and receive fast,
bounded, useful results.

The MVP should demonstrate:

- local or near-local latency
- useful interpretation of messy short requests
- deterministic retrieval and display behavior
- clear separation between semantic interpretation and execution

---

## 2. Product Hypothesis

If Legache already provides efficient local indexing, metadata, and retrieval for
messy business information, then a small RSA-style semantic layer can turn short
watch interactions into structured retrieval actions without needing a large
generative model on-device.

In short:

Legache supplies the local knowledge substrate.  
RSA supplies the bounded semantic interface.

---

## 3. MVP User Experience

The user can:

- speak or type a short request
- receive a fast answer card, result list, or target action
- interact with local information without waiting on a cloud LLM round-trip

Example requests:

- "What's our refund deadline?"
- "Open the PTO policy."
- "Find the latest warehouse checklist."
- "Who owns the Acme account?"
- "Show yesterday's client note."

Example outputs:

- policy answer card
- latest matching document card
- filtered results list
- ownership/contact card
- open-on-phone or continue-on-device handoff

---

## 4. Narrow MVP Scope

Pick one domain first.

Recommended options:

- policy lookup
- latest-note retrieval
- checklist retrieval
- account/contact ownership lookup

Recommended starting choice:

`policy lookup`

Why:

- bounded answer style
- easy to evaluate
- useful in enterprise settings
- strong fit for short watch interactions

---

## 5. What RSA Would Own

For the MVP, RSA would handle:

- request type classification
- entity / topic extraction
- filter extraction when relevant
- confidence / abstain behavior
- generation of a bounded retrieval contract

Example contract:

```text
mode: policy_lookup
topic: refund_deadline
document_family: finance_policy
time_scope: current
confidence: 0.94
```

RSA would **not** own:

- full document indexing
- full-text retrieval
- synchronization
- long-form answer generation
- general reasoning over the entire corpus

---

## 6. What Legache Would Own

Legache would provide:

- synced local document corpus or local-accessible subset
- metadata and document organization
- retrieval/index/query interface
- source-of-truth content
- watch-side or companion-device data delivery path

Legache remains the knowledge substrate.

RSA sits on top as the semantic interpretation layer.

---

## 7. Proposed Architecture

```text
voice/text request
    ->
speech-to-text if needed
    ->
RSA semantic gate
    ->
bounded retrieval contract
    ->
Legache retrieval/index layer
    ->
deterministic answer rendering
    ->
watch UI
```

Possible deterministic UI outputs:

- answer card
- snippet card
- result list
- "open on phone"
- "low confidence, refine query"

---

## 8. Suggested Technical Shape

### Input

- short voice command or short text query

### Interpretation Layer

- tiny transformer or other compact bounded decision model
- bounded output space
- optional abstain/escalate behavior

### Retrieval Layer

- Legache local retrieval over synced documents and metadata

### Output Layer

- deterministic answer card or result list
- no freeform generative answer requirement in MVP

---

## 9. Success Criteria

The MVP is successful if it shows:

- fast response time on target hardware or realistic companion-device setup
- good interpretation accuracy on a small bounded domain
- low-friction user experience for short enterprise knowledge requests
- reliable handoff from semantic interpretation to deterministic retrieval

Suggested measurable targets:

- median latency acceptable for interactive watch use
- high accuracy on bounded request classes
- safe abstain behavior on out-of-scope requests
- usable result presentation on small-screen UI

---

## 10. Evaluation Plan

Use a small curated query set for the chosen domain.

For example:

- 50 to 200 natural-language requests
- multiple phrasings per intent
- in-scope and out-of-scope examples
- confidence and abstain evaluation

Evaluate:

- intent accuracy
- slot extraction accuracy
- retrieval success rate
- end-to-end usefulness
- latency

---

## 11. Risks

### Risk 1: Watch UI is too constrained

Mitigation:

- use answer cards and handoff flows
- avoid trying to show too much text

### Risk 2: Voice input is noisier than expected

Mitigation:

- begin with text or constrained voice
- keep request classes narrow

### Risk 3: Query interpretation is not the real bottleneck

Mitigation:

- start with a bounded domain where retrieval quality is already strong

### Risk 4: Open-ended expectations creep in

Mitigation:

- keep the MVP framed as bounded local knowledge lookup

---

## 12. Proposed MVP Deliverable

A prototype demonstrating:

- one bounded knowledge workflow
- short voice/text input
- RSA-based request interpretation
- Legache-backed local retrieval
- watch-scale answer rendering

This could be:

- a native watch prototype
- a wearable mockup with companion device execution
- or a phone-hosted prototype that simulates the target watch interaction model

---

## 13. Why This MVP Is Interesting

This MVP would test a meaningful product claim:

Can compact local retrieval plus bounded semantic interpretation create a useful
enterprise knowledge interface on wearable hardware without depending on a large
generative model?

If yes, that creates a strong story around:

- privacy
- latency
- local-first operation
- bounded AI behavior
- practical enterprise utility

---

## 14. Suggested Collaboration Framing

This should be positioned as a tightly scoped exploration, not a large product
commitment.

Suggested framing:

"Let's test one narrow wearable knowledge workflow where Legache supplies the
local retrieval substrate and RSA supplies the bounded semantic interpretation
layer. If the prototype shows strong latency and usability, that gives us a
clear basis for deciding whether a broader wearable interface is worth building."