DeepCell provides a proprietary .deepcell file format that captures the full logical chain behind spreadsheet data—including formulas, sources, and decision pathways—and makes it readable and editable by both humans and AI agents such as Claude and GPT.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations lose critical reasoning behind spreadsheet data as formulas, sources, and decision logic are not captured, leading to tacit knowledge being trapped in individuals' heads and making models difficult to reuse or extend.
Solution
DeepCell introduces a proprietary .deepcell file format that records the full logical chain behind every spreadsheet value, including formulas, data sources, and decision pathways. The format is compatible with tools such as Excel, Notion, and AI agents like Claude and GPT, enabling both humans and AI to read, query, and build upon existing analyses. By preserving the evolution of models—from rough estimates to detailed driver and unit‑economics models—the platform ensures that context and rationale remain intact for future users. This structured knowledge base eliminates reliance on fuzzy embeddings or manual documentation, allowing seamless pivoting, roll‑up, and scenario analysis without rebuilding models. DeepCell’s integration layer also lets AI agents navigate the model directly, reducing hallucinations and improving the accuracy of automated reasoning.
Target Audience
Primary customers are finance, analytics, and strategy teams in enterprises that rely on complex spreadsheet models and seek to retain and extend the underlying reasoning, as well as AI developers building agents that need reliable access to structured financial logic.
Features
- .deepcell file format that stores values together with full context (formula, source, scenario, department, etc.)
- Native integrations with Excel, Notion, and AI agents (Claude, GPT) for bidirectional read/write access
- Versioned logic tracking that captures every draft of a model, preserving the reasoning evolution
- Queryable knowledge graph interface that lets users and agents retrieve facts by name rather than fuzzy search
- Automatic dependency mapping and formula extraction to maintain data lineage and prevent broken references
- Support for AI-driven scenario generation and iterative analysis without losing the underlying rationale