type
summary
created
Tue Apr 07 2026 02:00:00 GMT+0200 (Central European Summer Time)
updated
Tue Apr 07 2026 02:00:00 GMT+0200 (Central European Summer Time)
sources
raw/articles/SERVICE-AS-SOFTWARE-PLAN
tags
strategy saas pivot revenue ai-broker

SaaS Plan Summary

abstract
Strategic plan to pivot B2BPaper from a self-service marketplace into an AI-powered paper broker that autonomously sources, matches, and closes deals for a 2-4% commission, targeting 50-100 deals/month at EUR 100k average deal size.

Overview

The SERVICE-AS-SOFTWARE-PLAN document lays out a fundamental business model shift. Instead of building a marketplace where mills list surplus and buyers browse, B2BPaper becomes an AI paper broker that replaces human brokers (who charge 3-8%) with automated agents charging 2-4%.

Revenue Model

How It Works

Buyer Flow

  1. Buyer submits a natural-language request (e.g., "40t uncoated woodfree, 80gsm, reels, Central Europe, 3 weeks")
  2. AI agent searches supply database and live scrapes marketplaces
  3. Agent presents 2-3 matching offers with pricing
  4. Buyer picks one; agent handles negotiation and paperwork
  5. Deal closes; B2BPaper takes commission

Mill Flow

  1. Mill submits surplus info (e.g., "60t off-spec coated going obsolete in 30 days")
  2. AI agent scans demand profiles for matching buyers
  3. Agent drafts personalized offers to qualified buyers
  4. First buyer to commit gets the deal
  5. Commission taken from mill side

Thierry's Role

Thierry remains the human relationship anchor for high-value deals (EUR 50k+), opens mill networks via personal contacts, validates industry knowledge (pricing, grades), and closes the ~20% of deals that require human trust. The AI handles the other 80%.

Technical Architecture

The plan builds on top of the existing marketplace codebase (Django REST + PostgreSQL). The architecture has five layers:

  1. Supply Side -- scrapers, mill API, manual entry
  2. Demand Side -- intake forms, outbound, CRM
  3. Matching Engine -- grade, GSM, format, volume, geography, price optimization
  4. Deal Pipeline -- Lead, Qualified, Offered, Negotiating, Closed
  5. Communication Layer -- email, WhatsApp, agent outreach

Existing assets at time of writing: Product models, mill data, Extractor pipeline (8,919 docs, 4,246 products, 440 mills).

Phase Breakdown (42 Tickets, ~44 Days)

Phase Focus Duration
Phase 0 Foundation (supply, demand, deal, match models + admin shell) 5 days
Phase 1 Supply scraping (Crawlee framework, 4 scraper targets, dedup, scheduler) 8 days
Phase 2 Demand collection (intake form, buyer profiles, outbound email, qualification) 5 days
Phase 3 Matching engine (algorithm, scoring, auto-triggers, review dashboard) 5 days
Phase 4 Communication layer (email templates, WhatsApp API, conversation tracking) 5 days
Phase 5 Deal management (kanban pipeline, contract PDFs, commission/invoicing, analytics) 5 days
Phase 6 Service landing pages (buyer-facing, mill-facing, SEO) 3 days
Phase 7 AI agent layer (qualification, matching, outreach, negotiation, daily digest) 5 days
Phase 8 Testing and hardening (E2E flow, load testing, error handling) 3 days

Go-to-Market Strategy

  1. Thierry calls 10 known mills for surplus lists (manual supply entry)
  2. Outbound to 500 converters/printers to build demand profiles
  3. First 10 deals manually assisted by Thierry + AI
  4. After 20 deals, let agents run 80% autonomously
  5. Scale scraping to cover all major paper marketplaces

Key Observations

Sources

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