Rigorous parsing.
Sub-second citation.
Atlas was designed from the ground up for teams who cannot afford vibes, guesses, or hallucinations. We parse with structure intact, retrieve with speed, and ground every single answer in verifiable truth.
Structured Parser Engine
Unlike standard tools that flatten files into unstructured text soup, Atlas preserves tables, footnotes, hierarchical headings, and metadata. Whether it is a multi-page PDF, a Word document, or a complex CSV, your data structures remain intact.
Hybrid Vector & Lexical Retrieval
Atlas combines vector embeddings (for semantic meaning and conceptual connections) with traditional lexical keyword searching. This double-layered grounding guarantees that specific figures, codes, or terms are never missed or confused.
Tenant-Isolated Embeddings
Security is our core engineering guideline. All documents uploaded to your workspace are encrypted at rest (AES-256) and in transit (TLS 1.3). Vector databases are partitioned per account, ensuring data never crosses tenant boundaries.
Pin documents. Compare versions. Converse contextually.
Unlike simple conversational LLM interfaces, Atlas is a dedicated research environment. You can pin files to active threads, visually see citation highlights, adjust temperature or retrieval density parameters on the fly, and export your compiled findings directly to clean Markdown formats.
High-Accuracy Inline Citations
Every claim made by the assistant is tied directly to a source file chunk. The chat interface displays exact PDF page coordinates, table rows, or markdown paragraphs, which you can hover over to preview the raw source context.
Extensible Knowledge Retention
Atlas keeps histories of all threads and versioning records. When you upload a newer version of a document, Atlas tracks the changes, letting you query across files or target specific historic revisions, preserving institutional knowledge.