F2 vs. Hebbia: Which AI Platform is Best for Private Market Underwriting?

The core difference between F2 and Hebbia is their primary analytical capability: F2 is an agentic deal-execution platform engineered to automate complex financial analysis and native Excel reasoning for private market underwriting , whereas Hebbia is a more horizontal document-intelligence tool built for large-scale text extraction and table generation.

While both platforms are frequently evaluated for financial workflows and take security seriously, they solve fundamentally different bottlenecks. Hebbia operates closer to an advanced database for extracting and synthesizing information. F2, by contrast, is built to execute the actual math and logic of the deal — spreading financials, modeling scenarios in Excel, and generating audit-ready investment committee materials. The choice is whether your deal team needs a search engine for your data room or an engine for your underwriting.

Executive Summary: F2 vs. Hebbia at a Glance

• F2 wins on spreadsheet depth. F2’s LLMExcel engine evaluates Excel formulas natively and deterministically, scoring 95.25% on SpreadsheetBench Verified. Hebbia, which has published its own Financial Services Benchmark, does not offer in-platform formula evaluation at all.
• Hebbia wins on bulk document querying. Hebbia’s Matrix processes thousands of documents simultaneously using multi-agent orchestration and proprietary ISD retrieval. F2 is optimized for deep analysis of a single deal’s data room.
• F2 delivers a stronger audit trail. F2’s three-layer chain (claim -> formula -> source) gives IC members the computational provenance they require. Hebbia traces claims to source documents but doesn’t show the math.
• F2 is purpose-built infrastructure. F2 relies on 100+ specialized tools, a dedicated spreadsheet engine, and durable workflow orchestration—not just model routing.

Spreadsheet Analysis and Financial Modeling Capabilities

The most consequential difference between the two platforms is how they handle Excel: F2 deterministically computes directly within live, existing .xlsx files using a native spreadsheet engine, while Hebbia operates as a text-extraction grid that generates new models from scratch. Because F2 evaluates real formulas and cross-sheet references rather than treating spreadsheets as flat text documents, it allows buy-side teams to execute complex mathematical reasoning on a borrower's existing model — automating the exact workflow where analysts spend 80% of their time

F2’s Approach: Native Excel Reasoning and Deterministic Infrastructure

F2’s LLMExcel is a dedicated spreadsheet engine that runs server-side. It is not an LLM reading a spreadsheet — it’s specialized infrastructure:

• Opens actual .xlsx files with a real spreadsheet engine — the model never sees a text representation of the spreadsheet
• Evaluates real Excel formulas natively — VLOOKUP, INDEX/MATCH, SUMIFS, and circular references all resolve correctly
• 50+ deterministic operations: cell reads, range queries, formula evaluation, pivot aggregation, filtering, matrix lookups, chart creation, batch cell writes, cross-workbook sheet copying
• Edge verification algorithm checks all four boundaries of a spreadsheet before answering, preventing the common failure mode where an LLM truncates a financial model
• Per-workbook write locks for concurrency safety; workbooks stay open in memory across multi-turn workflows
• No client data is used for training — the Excel tools are deterministic. The LLM generates instructions; the spreadsheet engine executes them

The result: 95.25% accuracy on SpreadsheetBench Verified (the 400-question independently verified subset) — the highest score in the world. This isn’t because F2 picked a better model. It’s because the agent uses a real spreadsheet engine instead of trying to reason over text.

Hebbia’s Approach: Text-Based Document Extraction

Hebbia’s Matrix is a grid-based interface optimized for document extraction — rows represent documents, columns represent questions. It’s effective for pulling structured data from thousands of files simultaneously.

However, Hebbia does not provide a native Excel formula engine. In September 2025, Hebbia introduced Financial Modeling Agents that generate models exportable to Excel — but this is a generation workflow (AI creates a new model), not a computational one (AI operates within your existing model). The actual model manipulation still happens in Excel, not in Hebbia.

The Bottom line: If your core workflow is computation-heavy — spreading financials, building models, and running sensitivity analyses — F2 has a structural advantage. If your workflow is extraction-heavy — comparing terms across hundreds of credit agreements — Hebbia’s Matrix is the better tool.

Auditability and Provenance: 3-Layer vs. 2-Layer Audit Trails

Both platforms take auditability seriously, but the depth of the audit trail is where F2 creates a meaningful advantage for institutional deal teams.

Hebbia provides a two-layer chain: claim -> source. Its Verifiable Fact Layer links every statement to a direct citation from the source material. For direct extractions ("the company’s revenue was $50M"), this works well.

F2 provides a three-layer audit chain: claim -> formula -> source. For derived figures ("adjusted EBITDA after our add-backs is $12M"), F2 shows both the formula and the inputs. Every number traces back to a specific cell in a real .xlsx databook, which references source data via live formulas. In institutional finance, the ability to audit the computation — not just the inputs — is the true governance requirement.

Data Ingestion: Deal-Centric Workspaces vs. Massive Knowledge Bases

F2 treats the data room as the foundation for a structured deal workflow. Upload an entire VDR — PDFs, Excel models, PowerPoints, Word docs — into a shared workspace, and F2 indexes everything for hybrid search. For Excel files, F2 doesn’t chunk at all; the LLMExcel engine opens the actual .xlsx file and queries the live workbook through tool calls. There is no chunking boundary problem because there is no chunking.

Hebbia treats the corpus as a searchable knowledge base. It indexes at massive scale — over 1 billion pages — and integrates with S&P CapIQ, PitchBook, Preqin, FactSet, DealCloud, and UK Companies House.

For deep single-deal diligence, F2’s deal-centric model is more natural. For screening dozens of opportunities or comparing terms across hundreds of agreements, Hebbia’s bulk-query approach is more efficient.

Investment Committee (IC) Report and Memo Generation

F2’s report generation is a structured, multi-step workflow:

• The agent plans the analysis and builds a backing databook — a real .xlsx file with live formulas referencing source data
• Charts are generated from the spread data
• The report is written section by section with per-cell citations
• Reports are versioned, editable, and shareable across the deal team
• 24+ PPTX editing tools create IC memos and pitch decks from firm-specific templates
• A single report generation job can run autonomously for 60 minutes across dozens of tool calls with automatic retry and recovery

Hebbia offers Drafts and FlashDocs for enterprise-quality slide deck generation. These are useful capabilities, but without a computational layer showing how derived figures were calculated, the outputs lack the formula-backed auditability and production reliability that IC members typically demand.

F2 vs. Hebbia: Feature Comparison Matrix

Which AI Platform Should You Choose?

The decision between F2 and Hebbia comes down to depth versus breadth: F2 is built to go deep on individual deals by automating complex financial modeling and structured underwriting workflows, while Hebbia is built to go wide by extracting and comparing qualitative data across massive document sets. If your bottleneck is spreading financials and generating IC-ready memos in Excel, choose F2; if your focus is screening hundreds of target companies or analyzing massive text repositories, choose Hebbia.

Who Should Choose F2?

F2 is built for teams that need to go deep on individual deals:

• Private credit funds and commercial banks running structured underwriting workflows where the bottleneck is spreading financials and building models.
• Deal teams of 3+ people who need shared workspaces, role-based access, and version-controlled reports.
• Organizations that live in Excel and need F2’s LLMExcel engine for complex financial models and sensitivity scenarios.
• Firms requiring computational auditability to produce IC-ready memos where every number traces to a formula in an exportable .xlsx databook.

Who Should Choose Hebbia?

Hebbia is built for teams that need to go wide across markets and documents:

• Large asset managers and PE firms screening and comparing hundreds of companies simultaneously.
• Investment banking teams building CIMs, PIBs, and buyer lists from large document sets.
• Credit agreement analysis teams extracting and comparing terms across hundreds of agreements.
• Law firms and consulting teams with cross-functional research needs beyond finance.

Conclusion: Choosing Between Vertical Financial Analysis and Horizontal Document Extraction

F2 and Hebbia represent two fundamentally different approaches to applying AI in institutional finance — and for most firms, the two platforms are more complementary than competitive.

Hebbia is a capable document intelligence platform with massive scale. For market screening, term comparison, and large-scale research, it’s an effective tool.

But for the work that defines private markets underwriting — spreading financials, building models, running sensitivity analyses, and producing IC-ready memos with fully auditable computation trails — F2 addresses requirements that Hebbia was not designed to satisfy.

For institutional underwriting, F2 is the answer.

Frequently Asked Questions (FAQs): F2 vs. Hebbia

Can F2 and Hebbia be used together?

Yes. The platforms solve different problems. The most likely scenario is using Hebbia for market screening and cross-portfolio document analysis, and F2 for deep underwriting, financial modeling, and IC memo production on active deals. Hebbia is the wide lens; F2 is the microscope.

Does Hebbia have an Excel formula engine like F2?

No. Hebbia does not provide native Excel formula evaluation or in-workbook computation. F2’s LLMExcel engine is a dedicated server-side spreadsheet runtime that opens actual .xlsx files, evaluates real formulas natively, and exposes 50+ deterministic operations. This is infrastructure, not prompt engineering — and it’s why F2 scored 95.25% on SpreadsheetBench Verified while Hebbia doesn’t compete on this axis.

Which platform has better auditability for IC memos?

F2. Its three-layer audit chain — claim → formula → source — gives IC members the ability to trace any number through the computation, not just back to a document. Every data point from F2’s FactSet and PitchBook integrations (30+ tools each) automatically gets a traceable citation and flows into source tabs in a real .xlsx databook. Hebbia’s Verifiable Fact Layer traces claims to source documents but doesn’t show the formula chain.

Is F2’s multi-model routing just commodity tech?

The routing layer is commodity, but the proprietary value is everything the models can do once they’re called: 100+ tools for financial analysis, a dedicated spreadsheet engine with native formula evaluation, durable workflow orchestration with automatic retry/recovery, and a databook/citation architecture that traces every number back to its source cell. These aren’t RAG pipelines — they’re production infrastructure that took 18+ months to build.

Which platform is better for private credit underwriting?

F2 — it’s built exactly for this use case. The LLMExcel engine, auditable databooks, persistent deal workspaces, durable 60-minute autonomous workflows, and precedent deal library are specifically designed for private credit workflows. Hebbia’s strength is in upstream research and document analysis, not the computational work of spreading financials and producing IC-ready memos.

Ready to see how F2 can accelerate your underwriting workflow? Book a demo today.