Hebbia vs Model ML vs o11: Document Analysis vs Deliverables

Asset management involves two core activities: Reading (processing information) and Writing (producing deliverables).

The AI market has split along these lines.

On the Reading side, we have Hebbia and Model ML. They are “Matrix Engines”—capable of ingesting massive amounts of text and answering complex questions across thousands of documents.

On the Writing side, we have o11.

Here is why you likely need both, but why o11 solves the “Last Mile” problem that Hebbia leaves behind.

The “Matrix Engines”: Hebbia & Model ML

Hebbia

The pioneer of “Matrix AI.” You upload 50 credit agreements, and ask: “Does this contain a negative pledge?” It outputs a spreadsheet (Matrix) with Yes/No/Snippet for every file.

• Superpower: Massive parallel processing of unstructured text.
• Target: Due Diligence teams, Credit Investors.

Model ML

A specialized agent for Private Equity. It focuses heavily on the “screening” phase—ingesting CIMs and Teasers to determine if a deal fits the firm’s mandate.

• Superpower: Fast triage of inbound deal flow.
• Target: PE Associates and Principals sourcing deals.

The Gap: The “So What?”

Hebbia and Model ML are incredible at Input Processing. They help you digest the firehose of data.

But once you know the answer… what do you do with it?

You have to:

1. Build a Financial Model to value the asset.
2. Write an Investment Committee Memo advocating for the deal.
3. Create a Pitch Deck to raise capital for it.

Hebbia doesn’t write your memo. Model ML doesn’t build your LBO model.

o11: The Output Engine

o11 picks up where the analysis leaves off. We focus on Deliverable Creation.

The Integrated Workflow

Imagine a world where Reading and Writing are connected.

1. Phase 1 (Analysis): You use a tool like Hebbia (or o11’s own analysis module) to review the data room and extract key risks involves the lease terms.
2. Phase 2 (Creation with o11):
   • Prompt to o11 in Word: “Draft the ‘Real Estate Risk’ section of the IC Memo, citing the lease term findings we just extracted.”
   • Prompt to o11 in Excel: “Build a Rent Roll schedule using these lease rates, projecting a 3% annual escalator.”

Why “Full Stack” AI Wins

While specialized analysis tools are powerful, o11 offers a “Full Stack” capability for the deal team.

• We Read: Our VLM (Vision Language Model) can read PDFs, charts, and tables as well as anyone.
• We Write: We are the only platform that natively controls the Microsoft Office suite to produce the final asset.

Conclusion

If your only job is to read documents and fill out a checklist, Hebbia is fantastic. But if your job is to close deals, you need to produce memos and models. That is where o11 is essential.

Close the loop with o11.