F2 vs Rogo for Deal Teams in 2026

F2 and Rogo are both AI analysts for finance, but they are built for different deal teams. F2 is an underwriting and Excel analysis platform for private markets investors, meaning private credit funds, private equity firms, and commercial banks. Rogo is an enterprise AI platform for investment banks and large financial institutions that produces research, models, memos, and slide decks. If your day is credit underwriting and spreadsheet-heavy diligence, F2 is the closer fit. If you run a bank or a large advisory team that needs institutional-grade deliverables at scale, Rogo is built for you.

Everything in this comparison comes from the two vendors' own sites and independent coverage fetched on August 17, 2026. Where a number is a third-party estimate rather than a published fact, it is labeled as one.

F2 vs Rogo at a glance

Coverage of the diligence workflow

A deal moves through screening, data room review, modeling, IC materials, and post-close monitoring. F2 claims coverage of that full arc for private markets: screening and full-context analysis against firm history and market comps, covenant and downside analysis during underwriting, AutoReports for IC, and portfolio monitoring with covenant performance tracking after close. Its Institutional Knowledge layer is the distinctive piece, because it turns closed deals into searchable precedent for the next one.

Rogo covers the front and middle of the deal: sourcing insight from market data, working through filings and internal documents, auditing spreadsheets, and generating memos and decks. Its site describes end-to-end deal and investment work with auditable outputs. What Rogo does not advertise is private credit specifics such as covenant stress testing or borrower monitoring, because that is not its market. For a deeper look at how these workflows decompose, see our page on AI for financial due diligence.

Verification and source linking

Both vendors know that an unverifiable number is worthless in front of an investment committee, and both lead with auditability. F2's Audit Mode promises that every number, assumption, and output is traceable back to its source, down to the cell formula, and the company claims the top position on SpreadsheetBench, a benchmark for AI spreadsheet work, along with 95.25 percent accuracy on its internal benchmark built from real private markets files. Those are vendor claims, but they are specific and testable in a pilot.

Rogo positions its Excel models and analyses as auditable and offers spreadsheet auditing as a feature. Its verification story is institutional rather than formula-level in its public materials: single-tenant deployment, a long compliance certification list, and named bank customers whose risk teams have presumably already done the vendor diligence. In a pilot, ask both vendors to trace a specific output number to its source document in front of you. That single test separates real audit trails from marketing.

Pricing

Neither company publishes list pricing. Both f2.ai/pricing and rogo.com/pricing return no public page, and both sell through demos and enterprise contracts. The only public estimate is from Sacra, which pegs Rogo at roughly $3,300 per seat per year, with the negotiated number varying by firm size, modules, and contract length. No comparable third-party estimate exists for F2. For a ten-person deal team, the Sacra estimate implies something in the low tens of thousands per year for Rogo before enterprise minimums, but treat that as directional, not quoted.

Which one fits your team

Choose F2 if you are a private credit fund, a direct lender, a commercial bank lending team, or a private equity firm whose bottleneck is underwriting throughput. Its Excel-native engine, covenant analysis, and deal-history search are built for exactly that job, and its customer list on its own site skews toward lenders and credit teams.

Choose Rogo if you are an investment bank or a large institution buying for hundreds or thousands of seats, where compliance certifications, single-tenant deployment, and breadth across research, models, memos, and decks matter more than depth in any one credit workflow. Its named customers are banks, and its funding and scale reflect that market.

The third option: building your own

There is a version of this decision that neither vendor page mentions: for a fund with a specific process, a custom system can beat a seat license. At consultance.ai we build custom finance AI for deal teams, with a first system live in about six weeks, and the client owns the code, so the underwriting logic and deal history stay yours rather than living inside a vendor's platform. That path makes sense when your process is your edge; a vendor seat makes sense when you want standard capability fast. You can see how we approach this on our AI deal desk systems page and our work in AI for private equity, or book a call to talk through your stack.

Sources

• F2 homepage: product, customers, security, benchmark claims (fetched August 17, 2026)
• F2 2.0 announcement: Excel Intelligence, Audit Mode, AutoReports (fetched August 17, 2026)
• AlleyWatch: F2 $14M raise, founder, origin, customer count (fetched August 17, 2026)
• Rogo homepage: product, user metrics, customers, Series D, security (fetched August 17, 2026)
• Sacra on Rogo: pricing estimate, interface, deployment model (fetched August 17, 2026)