Hebbia's Workflow Moat for Enterprise AI
Danny Wheller, VP of Business & Strategy at Hebbia, on vertical vs horizontal enterprise AI
Hebbia is betting that the durable layer in enterprise AI is not the model itself, but the workflow engine wrapped around it. The practical job in finance and legal is not just reading one huge file, it is splitting a messy task into many smaller steps, pulling from SharePoint, CRM, filings, and deal docs, then turning that into a memo, diligence matrix, or pitch deck with permissions and audit trails intact.
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Matrix is the concrete expression of that moat. It is both a spreadsheet style workspace and the agent framework underneath Hebbia, where users configure repeatable jobs like VDR screening, contract analysis, or earnings call memo generation, and the system decomposes them into sub tasks across many documents.
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This is why stronger base models do not erase the product. Hebbia is model agnostic and already routes across providers, while a former product manager described the company using Fireworks to swap in new open models quickly, preserve throughput, and let customers choose the best model for each workload.
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The comparison is less versus ChatGPT and more versus search and workflow vendors. Some firms use Glean for broad enterprise search, then use Hebbia when they need work done over retrieved documents. At the same time, OpenAI and AWS are both moving up into multi step agent building, which makes enterprise control, domain templates, and deployment depth the key battleground.
The next phase of enterprise AI will reward products that own real operating workflows, not just model access. As model vendors add native agent builders and orchestration, Hebbia's edge will come from being the system of record for how analysts and lawyers actually run repeatable high stakes work, with domain tuned templates, human review points, and enterprise controls already embedded.