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

backend/matrix_intake/test_spark.py

@@ -0,0 +1,45 @@
+"""Tests for the business-card vision wrapper (pure logic, no network — chat_fn is stubbed)."""
+import os
+import sys
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+import spark  # noqa: E402
+
+
+def test_transcribe_card_returns_faithful_text():
+    captured = {}
+
+    def fake_chat(prompt, image_b64, mime="image/jpeg", system=None, max_tokens=600):
+        captured["image_b64"] = image_b64
+        captured["mime"] = mime
+        captured["system"] = system
+        return "Jane Doe\nGeneral Partner\nAcme Capital LLC\njane@acme.com\n+1 555 123 4567"
+
+    out = spark.transcribe_card("Zm9vYmFy", mime="image/png", chat_fn=fake_chat)
+    # The transcription is passed through verbatim — email survives for the integrity check.
+    assert "jane@acme.com" in out
+    assert "Acme Capital LLC" in out
+    # The image + mime reached the vision call; the card system prompt was used.
+    assert captured["image_b64"] == "Zm9vYmFy"
+    assert captured["mime"] == "image/png"
+    assert "business card" in (captured["system"] or "").lower()
+
+
+def test_transcribe_card_none_sentinel_becomes_empty():
+    # The model replies NONE for an unreadable / non-card image → we return "" so the bot can
+    # ask for a clearer photo instead of feeding garbage into the intake parser.
+    assert spark.transcribe_card("x", chat_fn=lambda *a, **k: "NONE") == ""
+    assert spark.transcribe_card("x", chat_fn=lambda *a, **k: " none ") == ""
+
+
+def test_transcribe_card_strips_whitespace():
+    assert spark.transcribe_card("x", chat_fn=lambda *a, **k: "  Acme\n  ") == "Acme"
+
+
+if __name__ == "__main__":
+    fns = [v for k, v in sorted(globals().items()) if k.startswith("test_") and callable(v)]
+    for fn in fns:
+        fn()
+        print(f"ok  {fn.__name__}")
+    print(f"\n{len(fns)} passed")