Project Overview

GraphRAG AI-Agent Specify

"Why do document tools only 'record' and never 'think'?"

Specify is an enterprise AI brain born from that question. It structures fragmented enterprise data with a graph-based ontology—going beyond simple storage to infer causal relationships across knowledge and surface actionable insights.

💡 Core Problem & Solution

The Problem: Data Silos and Volatile Knowledge

Data grows explosively in modern work environments but remains scattered across Notion, Slack, Google Docs, and more. As a result, project context breaks down and past mistakes repeat, leading to inefficient knowledge management.

The Solution: A Palantir-Style Ontology Document Engine

Inspired by how Palantir structures data, we built an engine that defines entities and relations inside documents.

• Assetization: Automatically turns scattered text into a living 'knowledge asset'
• Contextual Reasoning: The AI infers conflicts or connections across documents and surfaces them back to the user

🛠 Key Features

• Multi-Source RAG System: Connects internal docs with GitHub (code), Notion (plans), and Google Docs (specs) in real time for cross-referencing and answer generation.
• Semantic Linking (Real-time Context): As you write, the AI suggests related past history, technical specs, and potential risks in a sidebar.
• Insight Dashboard: Visualizes project 'health' and 'technical debt' in a Knowledge Graph to speed up decision-making.

🏗 Technical Architecture

1. Cloud Native & Event-Driven Architecture

• Scalability: A serverless design using AWS Lambda, SQS, and EventBridge adapts to traffic changes and keeps operational complexity low.
• Cost Optimization: Usage-based resource allocation delivers a high-performance, low-cost setup suited for early-stage startups.

2. AI & Real-time Collaboration

• Gen AI Orchestration: AWS Bedrock and LangChain power an agentic workflow that autonomously explores external data and reasons over it—beyond single-shot answers.
• CRDT (Yjs) Engine: Enables seamless real-time collaborative editing and keeps team data consistent.

🚀 Traction & Status

• Market Validation: With no paid marketing, organic search alone drove a 3.2% CTR, validating real demand.
• Current Status: MVP is built on Next.js and AWS Bedrock. We're running an open beta, collecting user feedback to refine the ontology engine.

Developed with Next.js, AWS Bedrock, and a passion for knowledge management.