Transform your AI assistants into engineering experts with real-time decision intelligence.
Engineering Memory Bank makes Claude, ChatGPT, and other AI assistants dramatically smarter about engineering decisions through invisible infrastructure.
The Magic: Instead of asking engineers to document decisions, we enhance their AI assistants with engineering intelligence.
Engineer asks Claude: "What power management worked well in similar IoT projects?"
Behind the scenes:
✓ Claude calls our MCP server
✓ Server searches engineering decisions + verifies current components
✓ Returns: "TI TPS62130 used in 3 projects, 4-week availability, $2.45"
✓ We earn $0.10 for the engineering intelligence
Engineer gets comprehensive answer. We get revenue. Everyone wins.
- 🎯 Market: $13.7B knowledge management space, 90% of teams suffer from "decision scatter"
- 💰 Revenue Model: Usage-based ($0.10/query) aligned with value delivery
- 📈 Projections: $150K ARR Year 1 → $4.8M ARR Year 3
- ⚡ Timing: 2025 MCP adoption wave creates first-mover advantage
- 📋 Executive Summary - Complete opportunity overview (5 min read)
- 📚 Master Documentation Index - Navigation guide to all materials
- 💰 MCP Business Model - Revolutionary infrastructure approach
- 📋 Product Requirements Document - Complete 30+ page PRD with market analysis
- ❓ Strategic Questions - Key decisions needing resolution
- 🤖 Multi-LLM Provider Architecture - Claude + OpenAI + local models
- 🌳 Tree-sitter Semantic Analysis - Automatic decision capture from git
- 🔌 MCP Integration Strategy - AI assistant enhancement
- 👔 Executive Dashboard Design - Business intelligence layer
- 📋 Review Guide - How to review all materials systematically
- ✅ Quick Start Checklist - Step-by-step development roadmap
- ✅ Comprehensive market research and competitive analysis
- ✅ Multi-stakeholder perspective analysis (engineers to executives)
- ✅ Technical architecture design and feasibility validation
- ✅ Business model breakthrough (MCP server infrastructure)
- ✅ Revenue projections and success metrics defined
- ✅ Python package foundation with core decision management
- ✅ Git integration framework for automatic decision capture
- ✅ Multi-LLM provider architecture designed
- ✅ MCP server specification and integration strategy
- 🔄 Import errors and missing modules need fixes (Week 1 task)
- ✅ Usage-based pricing model aligned with value delivery
- ✅ Clear path from $5K/month to $4.8M ARR
- ✅ Multiple customer segments identified (SME → Enterprise → Vendors)
- ✅ Competitive advantages established (network effects, real-time intelligence)
- Read Executive Summary (5 minutes)
- Review MCP Business Model (20 minutes)
- Fix import errors in
python/memory_bank/(technical) - Customer interviews with 5 engineering teams (validation)
- MCP server prototype with basic engineering intelligence
- Multi-LLM integration (Claude + OpenAI + Ollama)
- Private beta program with 3-5 engineering teams
- Usage analytics and pricing validation
- 25 organizations actively using private beta
- $5K+ MRR from usage-based pricing model
- Web search integration for real-time component verification
- Enterprise pipeline with private deployment options
- 2025 is the "iPhone App Store moment" for MCP servers
- MCP only went public late 2024 - market wide open
- Every engineer already using AI assistants daily
- Traditional: "Buy our documentation software" (fails due to adoption friction)
- Our approach: "Your AI assistants just got 10x smarter" (invisible infrastructure)
- Engineers who save the most time pay the most
- Revenue scales naturally with value delivery
- Network effects improve quality with more engineering decisions
- Not static documentation - live component availability, standards updates
- Web search integration provides dynamic engineering intelligence
- Supply chain alerts, patent monitoring, failure mode updates
Positioning as AI infrastructure rather than documentation software creates 10x higher customer lifetime value and eliminates adoption friction.
Supporting Claude, OpenAI, Ollama, and others prevents vendor lock-in while optimizing for different engineering tasks.
Automatic decision capture from git commits using semantic code understanding - no manual documentation required.
AI identifies successful patterns from automotive and applies to aerospace, IoT, medical devices, etc.
- $0.10 per complex engineering analysis
- $0.05 per component verification
- $0.02 per similarity search
- Free tier: 50 queries/month per engineer
- Engineer: $29/month (500 queries, basic features)
- Team: $199/month (5,000 queries, advanced features)
- Enterprise: $999/month (unlimited, compliance, private deployment)
- Year 1: $150K ARR (500 engineers × $25/month average)
- Year 2: $1.26M ARR (3,000 engineers × $35/month average)
- Year 3: $4.8M ARR (8,000 engineers × $50/month average)
- Gross margins: 90%+ (AI API costs ~10% of revenue)
engineering-memory-bank/
├── docs/ # Complete documentation suite
│ ├── EXECUTIVE_SUMMARY.md # Start here - complete overview
│ ├── MASTER_DOCUMENTATION_INDEX.md # Navigation guide
│ ├── strategy/ # Business model and strategy
│ ├── technical/ # Architecture and design
│ └── implementation/ # Development roadmap
├── python/ # Core Python package
│ ├── memory_bank/ # Main package
│ │ ├── core/ # Decision management
│ │ ├── ai/ # Multi-LLM integration
│ │ ├── git/ # Automatic capture
│ │ └── utils/ # Utilities
│ └── tests/ # Test suite
├── pyproject.toml # Package configuration
└── README.md # This file
This project is in the research-to-implementation transition phase. Key contribution areas:
- Foundation fixes - Import errors, missing modules
- MCP server development - AI assistant integration
- Multi-LLM provider implementation - Claude, OpenAI, Ollama support
- Customer validation - Engineering team interviews and feedback
Ready to build the future of AI-assisted engineering?
- Start with: Executive Summary
- Understand the model: MCP Business Model
- Review everything: Master Documentation Index
- Begin implementation: Quick Start Checklist
Engineering Memory Bank transforms scattered engineering decisions into AI-powered competitive advantages.
The infrastructure model positions us to capture the 2025 MCP adoption wave and build a venture-scale business in AI-assisted engineering intelligence.