Add business-card photo intake to the Matrix bot (M3)

The intake bot now accepts a photo of a business card in the intake room and
turns it into the same new-investor proposal a typed note would. The only new
step is image -> text; everything downstream (parse, fuzzy match, in-thread
approval, log-communication write) is reused unchanged.

M3 was deferred only because Spark Control had no vision model. That blocker is
gone: the daily-driver Qwen is vision-capable under the same model id, and the
gateway forwards OpenAI multimodal content untouched, so no gateway/server/s9pk
change is needed -- this ships bot-only (git pull + rebuild on the Spark).

Transcribe-then-reuse (not vision-straight-to-JSON) is deliberate: the
transcription becomes the source text the email-integrity rule checks against,
so a mis-read address can't reach the CRM unapproved -- same guarantee as the
text path. Card commits tag source="matrix_card" for the audit log.

- llm.chat_vision: multimodal /v1/chat/completions, same model, same gateway
- spark.transcribe_card: faithful card->text, "" on a non-card (NONE sentinel)
- bot.on_image/handle_card: download image, transcribe, hand to handle_intake
- crm_client: source provenance overridable via the proposal's _source key
- tests: test_spark.py + a provenance case; 41/41 suite green

docs/guides/matrix-intake.md

@@ -8,8 +8,9 @@ paths:
 Read this before editing `backend/matrix_intake/`. The bot turns a typed message in a
 dedicated Matrix room into a proposed fundraising-grid add/edit, gated on **in-thread human
 approval** before any write. Phase status: **M1 + M2 deployed & live** (text intake + approval + write; bot on the Spark,
-CRM endpoints on the box at **v0.1.0:86**; live-smoked 2026-06-17). **M3 (business-card photo)
-deferred** — Spark Control has no vision model yet.
+CRM endpoints on the box at **v0.1.0:86**; live-smoked 2026-06-17). **M3 (business-card photo) BUILT —
+bot-only, awaiting live-smoke** (the prior blocker — "Spark Control has no vision model" — is gone:
+the daily-driver model is now vision-capable; see *Business-card capture* below).
 
 **Post-deploy UX pass — DEPLOYED & LIVE 2026-06-17:** fuzzy investor matching (server-side,
 **v0.1.0:86**, installed to the box — `candidates` endpoint verified live) + in-thread
@@ -69,6 +70,47 @@ Spark). See *Fuzzy matching* below. Tests green (27/27 backend + the offline bot
    "reply in the thread" redirect (`store.any_pending()` guard in `handle_intake`), not a
    misparsed new intake.
 
+## Business-card capture (M3 — image intake)
+
+Send a **photo of a business card** into the intake room and the bot turns it into the same
+new-investor proposal a typed note would. The **only added step is image → text**; from there the
+existing flow (parse → match → disambiguate → approve → `log-communication`) runs **unchanged** —
+`handle_card` just calls `handle_intake` with the transcription.
+
+- **Trigger:** a top-level `m.image` event in the intake room (`on_image` → `handle_card` in
+  `bot.py`; registered via a second `add_event_callback(on_image, RoomMessageImage)`). Images in
+  the Q&A / email-review rooms, the bot's own uploads, and an image dropped **inside an existing
+  thread** are ignored. The card's own event becomes the proposal thread root, like a text message.
+- **The one new call** (`spark.transcribe_card` → `llm.chat_vision`): download the image
+  (`client.download(mxc=event.url)` — **unencrypted only**; an E2EE room delivers a different event
+  class we don't register for, so encryption is naturally excluded), base64-encode, and POST an
+  **OpenAI multimodal** `/v1/chat/completions` to **Spark Control** — *same endpoint, same model id*
+  (`CRM_CHAT_MODEL`, the daily-driver Qwen, `capabilities: [vision, reasoning]`), with the user
+  message's `content` an array of a text part + an `image_url` data-URI. Spark Control is a **dumb
+  passthrough** (`image/app/llm_proxy.py`), so **no gateway change** was needed. The model
+  **transcribes** the card; it does not emit JSON.
+- **Why transcribe-then-reuse (not vision-straight-to-JSON):** the transcription becomes the
+  **source text** the email-integrity rule checks against — `parse.normalize` only keeps an address
+  that *literally appears in the source*, never one the model mints. So a mis-read address can't
+  reach the CRM unapproved, exactly as on the text path, and 100% of parse/match/disambiguation/
+  approval is reused. The transcription is framed (`"New investor — from a business card:\n…"`) so
+  the extractor reads it as a new investor.
+- **Provenance:** a card commit tags `source="matrix_card"` (vs `"matrix_intake"` for a typed note)
+  in the audit log, threaded via the proposal's `_source` control key (`handle_intake(…, source=…)`
+  → `crm_client.build_commit_payload`, which defaults to `"matrix_intake"` when absent).
+- **UX:** the bot acks `📇 Reading the card…` before the (slower) vision call; an unreadable image
+  (model replies `NONE`, or transcription < 5 chars) gets a "try a clearer, well-lit photo" reply
+  instead of a garbage proposal.
+- **Deploy is bot-only** — the change lives in `backend/matrix_intake/` (`bot.py`, `spark.py`) +
+  `backend/ingest/llm.py` (bundled into the bot image), shipped on the **Spark** via `git pull` +
+  `docker compose up -d --build`. **No s9pk, no version bump, no new env** (same model; no auth on
+  the LAN). Contrast with M2 / email-review, whose server endpoints had to ship in the s9pk.
+- **Known limits (live-smoke checklist):** ① a StartOS reverse-proxy body cap could `413` a large
+  photo — the model already downscales server-side (`max_pixels` ≈ 2 MP), so if it trips, add a
+  client-side resize (would pull Pillow into the bot image); ② iPhone **HEIC** may not decode in
+  vLLM's PIL — most clients (Element iOS) transcode to JPEG on upload, but confirm on-device; ③ the
+  offline tests stub the vision call (`test_spark.py`); the download + real OCR is **live-smoke only**.
+
 ## Fuzzy matching (server-side, ships in the s9pk)
 
 `GET /api/intake/match` returns `{match, candidates}`. `find_intake_match` is unchanged —