Monce Selfservice

Memory-augmented extraction for Outlook & email workflows. Drop a file, get structured data. Every extraction teaches the service about your patterns — client routings, factory preferences, recurring quirks — so the next one is smarter.

per-user memory extract + remember + recall auto-insights

Endpoints

POST /v1/extract

multipart: files, user_id, optional industry, email_subject, email_body, auto_memory.

POST /v1/remember

json: {user_id, text, source?, tags?}

GET /v1/recall

query: user_id, q, limit. Keyword-scored memory search.

POST /v1/chat

json: {user_id, message}. Memory-grounded Q&A (Sonnet).

GET /v1/history

query: user_id. Past extractions (task_id, trust, routing).

POST /v1/feedback

json: {user_id, task_id, kind, payload?}. kind: accept / reject / correct / note.

SDK

from monceai import Extraction, Outlook

ex = Extraction("quote.pdf", user_id="7a3f9b2c", industry="glass")
ex.lines               # structured rows
ex.trust               # {"score": 82, "routing": "auto_accept"}
ex.insights            # Haiku-distilled memory bullets

ol = Outlook(user_id="7a3f9b2c", auto_memory=True)
ol.extract_email(attachments=[pdf_bytes], subject="Devis VIP", body="comme d'hab")
ol.recall("what does this user usually do with VIP files?")
ol.remember("client always wants 44.2 rTherm as intercalaire")

Outlook add-in

/addin/ — installable via manifest.xml, posts attachments to /v1/extract.

Interactive

/ui — drop a PDF, watch memory + insights unfold · /addin/ — Outlook taskpane

Docs

/dashboard — live ops view · /paper — concept and reflex loop · /architecture — diagram, endpoints, layout · /economics — cost and capacity

API docs

/docs · /redoc