F2 vs. Model ML: Comparing a deal-execution workspace to an AI agent embedded in Excel

TL;DR: Model ML is a horizontal AI workspace that embeds an agent inside Excel, PowerPoint, and Outlook to automate deliverables across a firm’s front office. F2 is an agentic operating system, combining a server-side Excel engine, a formula-level audit trail, and a precedent library built from your firm's own deals. If your bottleneck is producing decks and documents at scale, Model ML might be a fit, while teams that underwrite credit and equity deals will find F2 built specifically for the workflows they need.

For a buyer weighing the two, the decision comes down to three differences: platform architecture, how each platform builds financial models, and whether your firm’s knowledge compounds over time.

Executive Summary: F2 vs. Model ML Core Differences

F2 is an agentic operating system built for buy-side underwriting. It combines:

• A native, server-side Excel engine that deterministically evaluates live workbook formulas, scoring 95.25% on SpreadsheetBench Verified
• A three-layer computational audit trail, tying every claim to its formula, source, and original file
• A precedent deal library that benchmarks new deals against your firm's own executed transactions

F2 serves private credit funds, commercial banks, and PE deal teams collectively managing $400B+ in assets.

Model ML is a horizontal AI workspace for financial services. Founded in 2023 by repeat Y Combinator founders Chaz and Arnie Englander, it combines:

• An AI agent embedded inside Excel, PowerPoint, and Outlook, plus a dedicated app
• Core products include workflows, Grids, Document Review, Chat, Notetaker, and Agentic Dashboards
• Four target verticals: investment banking, consulting, private equity and credit, and asset and wealth management
• $100M+ raised, including a $75M Series A led by FT Partners and a 2026 strategic investment from HSBC Asset Management

Platform Architecture: Dedicated Deal Workspace vs. Embedded AI Agent

Where the work lives and how teams use each platform differ fundamentally.

F2's Approach: One System of Record for the Deal

F2 consolidates firms’ disparate workspaces. The data room, model building, memo creation, and precedent context live in a single permissioned workspace:

• No copying and pasting between tools. A figure spread from the data room flows to the databook, the model, and the memo without being retyped.
• One permission model for everything deal-related, in a closed, browser-based environment built for bank infosec review.
• Excel-native computation happens server-side, so the live workbook stays the working artifact.
• Integrations with the surrounding stack, including PitchBook, FactSet, DealCloud, SharePoint, and Outlook, bring data into the workspace rather than scattering the deal across tools.

Model ML's Approach: An AI Agent Inside the Tools Your Team Already Uses

With Model ML, the agent surfaces inside Excel, PowerPoint, and Outlook, the tools analysts already have open:

• No new workspace to adopt. Where change management slows software rollouts, this is a real advantage.
• Deliverables arrive in your firm’s format, conforming to existing templates, language, and branding.
• Real-time data connectivity pulls PitchBook, FactSet, and Capital IQ alongside internal systems like SharePoint and DealCloud.

F2's workspace connects the entire deal, from source document through committee decision, in one place. Model ML's embedded agent speeds up each individual tool.

Excel Modeling and Audit Trails: Computing the Model vs. Generating the Deliverable

Underwriting teams need two things from an AI platform: numbers that come from running the live model's actual formulas, and a chain that shows an investment committee how each number was produced.

F2's Approach: Deterministic Computation With a Formula-Level Audit Trail

F2 runs a native, server-side spreadsheet engine that deterministically evaluates live .xlsx formulas:

• VLOOKUP, INDEX/MATCH, SUMIFS, cross-sheet and circular references resolve correctly, rather than being approximated by a language model
• 50+ deterministic operations, scoring 95.25% on SpreadsheetBench Verified
• When an analyst presents leverage assumptions in an LBO or credit model, F2 recomputes the actual dependency chain inside the actual workbook

Every figure also carries a three-layer audit trail, from claim to formula to source file. When an investment committee member asks how the analysis got from reported to adjusted EBITDA, the reviewer can walk the full chain from the memo back to the source cell and underlying document.

Model ML's Approach: Deliverable Generation With Referential Citations

Model ML generates financial models in a firm's existing formats. Its Document Review product has also posted strong results, completing verification checks in under 3 minutes that previously took over an hour of manual review.

Two gaps matter for underwriting buyers:

• No documented engine for evaluating formulas inside a firm's existing workbooks, and no published accuracy benchmark for its own spreadsheet output
• Provenance is referential rather than computational. Outputs cite the source filing, without a formula chain reconstructing how a number was computed, adjusted, and carried through the model.

For underwriting teams, the financial model is the core deliverable, and an investment committee needs to see how each figure was calculated.

Deployment Model and Institutional Knowledge: Compounding Product vs. Forward-Deployed Service

The two companies deliver customer value in structurally different ways, which determines what your firm owns after a year on each platform.

F2's Approach: A Productized Platform That Compounds With Every Deal

F2's precedent deal library turns your firm's own institutional knowledge into the product:

• Ingests your IC memos, models, CIMs, and CRM data
• Benchmarks new deals against your executed transactions, filtered by industry, size, structure, and leverage
• Surfaces the risks and mitigants your firm flagged the last time it saw a similar credit
• Compounds with every completed deal, so the value of the library gets smarter as your team keeps underwriting

Model ML's Approach: A Forward-Deployed Agent, Configured to Your Firm

Model ML describes itself as “built bespoke, and forward deployed by industry experts.” In practice:

• Industry experts configure the platform to your templates, house style, and workflows
• Deliverable quality reflects that bespoke setup, a real strength for deployment
• Memory covers a user's role, conventions, and formatting preferences, plus reusable templates.

After a year on F2, a firm has a searchable library of its own completed deals that it can benchmark new transactions against. After a year on Model ML, a firm has a platform configured to its templates and workflows, with preference memory that persists across conversations, but no library of its own deal history that Model ML has documented.

Where Each Platform Is the Stronger Fit

Both platforms can provide meaningful value, but the right choice depends on your firm's work.

Where F2 Is the Stronger Fit

• Underwriting depth. Spreading financials, building LBO models, and running sensitivity cases inside live Excel workbooks rather than exports that break when assumptions change.
• Committee-grade auditability. IC memos in the firm's own format, with every figure traceable back to the source cell and document.
• Compounding deal history. Benchmarking every new transaction against the firm's own executed precedents, with the library growing on each completed deal.
• Institutional security posture. Closed, browser-based environment, zero data retention, and no training on client data, which simplifies bank infosec and procurement review.

This makes F2 the fit for private credit funds, commercial banks, and PE deal teams whose core work product is the underwrite itself.

Where Model ML Is the Stronger Fit

• Breadth across the front office. Six products spanning meeting capture to real-time dashboards, serving IB, consulting, PE/credit, and asset and wealth management. One AI layer for the whole organization.
• Deliverable automation at scale. Workflows run manually, on schedules, or on triggers like earnings releases, producing tearsheets, CIM drafts, and management presentations in firm format.
• Embedded adoption. The agent lives inside Excel, PowerPoint, and Outlook, so bankers work in the tools they already know.
• Enterprise credibility. ISO 27001, SOC 2 Type II, GDPR, model-agnostic routing, and a customer base that includes Big Four deal advisory teams.

This makes Model ML the fit for investment banking and advisory teams, consulting and Big Four deal advisory practices, and asset and wealth managers seeking one horizontal AI layer across many functions.

F2 vs. Model ML: Feature Comparison Matrix

Conclusion: An AI Teammate or Deal-Execution Infrastructure

Model ML is a horizontal AI workspace that automates decks, documents, and analysis inside Excel, PowerPoint, and Outlook for banking, consulting, private equity, and asset management teams.

For underwriting teams, F2 is the better fit on all three differences covered above. It keeps the data room, model, memo, and precedent history in one system of record. It runs the live model's actual formulas and gives the investment committee a record of how each figure was calculated. It also builds a searchable library of the firm's completed deals that grows with each new transaction.

Frequently Asked Questions: F2 vs. Model ML

Is Model ML a direct competitor to F2?

Partially. Model ML is a horizontal AI workspace spanning IB, consulting, PE/credit, and asset management, and F2 is vertical infrastructure for buy-side underwriting. They overlap most where private credit and PE teams evaluate AI for deal execution, similar to how F2 compares with Claude Cowork on the horizontal side and Rogo on the sell-side research side.

Can Model ML evaluate Excel formulas inside a live underwriting model?

Its public materials describe building models with live formulas, but do not document an engine for evaluating formulas inside a firm's existing workbooks or a published accuracy benchmark for its own output. F2's server-side engine deterministically evaluates live formulas, including circular references, and scores 95.25% on SpreadsheetBench Verified.

Does Model ML have a precedent deal library?

No publicly documented precedent deal library. Model ML documents preference memory across conversations and reusable templates, but not a library of the firm's past deals; F2's precedent library benchmarks new deals against a firm's executed transactions, growing with every deal.

Which platform has better auditability for investment committees?

F2. Its claim to a formula-to-source-file chain lets reviewers trace any figure to the source cell and document. Model ML's outputs cite source documents without reconstructing how a number was computed.

Does F2 train on client data?

No. F2 operates under zero data retention and never trains on client data. F2 is SOC 2 Type I & II certified, and Model ML holds ISO 27001, SOC 2 Type II, and GDPR compliance.

Can a firm use both platforms?

Yes. Model ML can serve as the horizontal AI layer for front-office deliverables while F2 runs deal execution. They compete directly only where an embedded assistant is asked to do an underwriting platform's job.

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