Atlas

Product Introduction

1. Definition: Atlas by Nanonets is an AI Context Layer platform. It is a specialized SaaS product designed to function as a centralized knowledge backbone for enterprise AI tools. It extracts, structures, and serves a company's unique operational, data, and brand context to AI models and agents, ensuring outputs are accurate and on-brand.
2. Core Value Proposition: Atlas exists to eliminate the repetitive, generic outputs from AI tools like Claude, ChatGPT, and Cursor in a business environment. Its primary value is providing persistent, company-specific context, transforming generic AI assistants into domain-aware collaborators that follow established workflows, understand internal data schemas, and adhere to brand guidelines without constant re-prompting.

Main Features

1. Three-Layer Company Context Engine: Atlas automatically extracts and structures a company's knowledge into three distinct, machine-readable layers. Process Context answers "how do we do X here?" by mapping real onboarding flows, approval chains, and procedures from internal documents. Data Context maps where data lives, its structure, and metric definitions, allowing AI tools to navigate complex schemas without asking "where is X?". Brand Context ingests visual assets, fonts, color palettes, and design principles to ensure AI-generated content (like slide decks) is visually consistent.
2. Universal Integration Backend: It operates via a single backend that connects to diverse source materials (company websites, Notion workspaces, employee handbooks, brand guidelines) using a quick 60-second setup. The extracted context is made available via a Model Context Protocol (MCP) URL for direct integration with AI tools like Claude, Cursor, and ChatGPT, or through a REST API endpoint for custom-built AI agents. All rules are stored in plain Markdown, ensuring portability and no vendor lock-in.
3. Privacy-by-Design Context Management: Atlas implements a dual-layer privacy architecture. Personal Context rules are isolated to an individual user's account and are never shared with teammates or administrators. Company Context is a shared, admin-curated layer that provides uniform rules for the entire team. This design ensures sensitive personal adjustments remain private while maintaining organizational consistency.

Problems Solved

1. Pain Point: Enterprises face severe inefficiency and output degradation when every employee uses multiple AI tools that start from zero context. This leads to "AI slop"—generic, off-brand slide decks, incorrect metric calculations based on misunderstood internal schemas, and answers based on public web knowledge instead of company-specific procedures.
2. Target Audience: AI Tool-Enabled Teams in Growing Companies (10-50 employees). Specifically, this includes Operations Managers, IT Administrators, Marketing Leads, and individual Contributors who use Claude, ChatGPT, and Cursor daily. It targets companies where AI adoption is high, but governance and contextual training of these tools is manual and inconsistent.
3. Use Cases: Essential for scenarios like an employee asking an AI, "How do I get prod DB access?" (where Atlas provides the real onboarding flow), a data analyst requesting, "Pull churn rate for last quarter" (where Atlas defines the correct schema and metric formula), or a marketer generating a "Q4 launch deck" (where Atlas ensures the correct fonts, logos, and color scheme are applied automatically).

Unique Advantages

1. Differentiation: Unlike generic RAG (Retrieval-Augmented Generation) pipelines that require significant engineering effort to build and maintain, Atlas provides a pre-built, structured extraction and serving layer. It contrasts sharply with manual, repetitive prompting or the creation of unwieldy, monolithic system prompts by offering a managed, updateable, and portable context service. It is not an AI model itself but an essential context plumbing layer for all models.
2. Key Innovation: The key innovation is its triple extraction methodology powered by Nanonets' core document-understanding technology (used by enterprises like Volkswagen and Bayer). It doesn't just retrieve documents; it parses them to extract structured process, data, and brand context into a unified backend. Combined with the zero-lock-in, plain-Markdown export and dual-privacy architecture, this makes it a uniquely secure and interoperable enterprise AI enabler.

Frequently Asked Questions (FAQ)

1. What AI tools does Atlas integrate with? Atlas works as a universal context layer for any AI tool. It has direct integrations via MCP for Claude, Claude Code, Cursor, ChatGPT, and Codex. It also provides a REST endpoint for integration with any custom-built AI agent or internal tool, ensuring your company rules are accessible everywhere.
2. How does Atlas extract my company's context, and is it secure? Atlas uses advanced document extraction technology to analyze your provided sources (like Notion docs, websites, and PDFs). It structures this information into process, data, and brand layers. For security, it is SOC 2 Type II certified, features a dual-privacy system separating personal and company data, and you can export all rules as plain Markdown at any time.
3. Is there a setup fee, and what is included in the $99/mo subscription? There is no setup fee. The $99/month subscription per company includes the core Atlas platform, access for your team, white-glove setup assistance, and the ability to connect your knowledge sources. The first 200 companies can sign up now with no payment upfront; Nanonets will reach out within 24 hours to begin onboarding.