Unabyss

Product Introduction

1. Definition: Unabyss is a universal context management layer for AI agents and large language models (LLMs). Technically, it's a SaaS platform that acts as a central data integration and retrieval engine, connecting via the Model Context Protocol (MCP).
2. Core Value Proposition: It solves AI context chaos by automatically extracting, structuring, and updating user context from daily apps, then providing segmented, permissioned access to any connected AI tool. The primary value is "set it up once and never re-explain yourself to AI again," enabling persistent, up-to-date, and secure personal and professional context for AI assistants.

Main Features

1. Automated Context Extraction & Structuring: Unabyss connects to hundreds of productivity and communication integrations (like Notion, Slack, Gmail, Google Drive). It automatically extracts raw data, segments it by topic, confidence, sensitivity, and source, and structures it into a continuously updated, retrievable knowledge base. This eliminates manual context management.
2. Granular Permission & Segmentation Layer: Users control AI access at an item-level. Features include toggles to exclude private information, company-confidential data, or entire source apps. This permission layer applies at retrieval time, ensuring blocked context never reaches the AI model, addressing critical data privacy and security concerns in enterprise AI use.
3. MCP (Model Context Protocol) Integration & Efficient Retrieval: Unabyss serves structured context via MCP, a standard for tool-to-LLM communication, making it instantly available in compatible AI tools like Claude Code, Cursor, and others. Its retrieval engine uses advanced scoring to extract only the most relevant lines for a query, claiming up to 10x fewer tokens than standard RAG, reducing cost and preventing context dilution.

Problems Solved

1. Pain Point: Fragmented and outdated AI context. Users currently manually copy-paste information between tools, leading to agents working with stale, incomplete, or invisible data. This results in inconsistent outputs and lost productivity.
2. Target Audience: Technical builders (developers using Cursor, Codex, Claude Code), founders/executives, angel investors, GTM/marketing teams, and knowledge workers who use multiple AI agents daily and need consistent, brand-aligned, and informed AI interactions.
3. Use Cases: A developer maintaining context across coding agents (Cursor, Claude Code); a founder ensuring investor updates and company strategy are reflected in all AI communications; a marketer generating on-brand copy using latest guidelines; an investor triaging startup pitches with full historical context.

Unique Advantages

1. Differentiation: Unlike simple chat history or manual context prompts, Unabyss provides automated, live syncing from source applications. Compared to other RAG systems, it emphasizes granular user-controlled permissions and topic-based segmentation, not just semantic search.
2. Key Innovation: Its three-layer context engineering pipeline: raw signal ingestion, automatic multi-axis tagging/segmentation, and permission-gated, efficient retrieval. This structured approach to "context engineering" moves beyond simple data dumping to create a managed, scoped context layer.

Frequently Asked Questions (FAQ)

1. What is Unabyss and how does it work with AI? Unabyss is a universal context layer that automatically pulls your data from apps like Notion and Slack, structures it, and makes it available to AI tools like Claude and Cursor via the MCP standard, so your AI always has current, relevant information.
2. Is my data safe with Unabyss? Yes, Unabyss employs a granular permission layer where you can exclude private or confidential items. Blocked data is filtered at retrieval time and never sent to the AI model, ensuring sensitive information remains secure.
3. What is MCP and why is it important for Unabyss? MCP (Model Context Protocol) is a standard developed by Anthropic for tools to provide context to LLMs. Unabyss uses MCP to serve your context, making it compatible with a growing ecosystem of AI agents and developers without requiring custom integrations for each tool.
4. How does Unabyss handle context from multiple different apps and topics? Unabyss automatically tags every piece of incoming context across axes like topic, source app, and sensitivity. This allows its retrieval system to surface only the relevant segment (e.g., "work" vs. "personal") for each query, preventing information overload.
5. What is the pricing model for Unabyss? Unabyss offers a free start with $5 in credits. It operates on a pay-as-you-go model after the credit is used, with no upfront credit card required and no feature tiers, charging based on usage.