F2 vs. Rogo: Comparing AI Platforms for Buy-Side Underwriting and Sell-Side Research

F2 and Rogo are arguably the two most prominent vertical AI platforms built specifically for institutional finance. But they solve fundamentally different problems for fundamentally different users.

F2 is a deal-workflow platform for buy-side teams, giving you a deterministic spreadsheet engine, auditable databooks backed by real .xlsx files, and persistent deal workspaces. Rogo is a financial research platform primarily serving sell-side institutions, providing custom-trained financial reasoning models, deep research agents, and the industry’s broadest data partnership network.

Choosing the wrong one means investing in infrastructure that doesn’t address your team’s core bottleneck.

Executive Summary: F2 vs. Rogo Core Differences

• These platforms target different markets. F2 targets buy-side firms (private credit, commercial banks, PE) where the work is spreading financials, building models, and producing auditable memos. Rogo targets sell-side institutions (investment banks) where the work is research, pitch generation, and deal screening.
• F2 wins on spreadsheet depth. F2’s LLMExcel engine computes within existing Excel workbooks deterministically, scoring 95.25% on SpreadsheetBench Verified. Rogo’s Sheets Agent generates new models for export.
• Rogo wins on data breadth. No one matches Rogo’s data partnership network: S&P Capital IQ, LSEG, PitchBook, Preqin, Third Bridge, FactSet, and Crunchbase.
• F2 delivers a stronger IC audit trail. F2’s databook architecture traces every number through live formulas to source data (claim -> formula -> source). Rogo’s citations link to data sources but don’t show the computation.
• F2 is purpose-built infrastructure. F2 offers 100+ specialized tools, a dedicated spreadsheet engine, and durable workflow orchestration with 60-minute autonomous runs.

Spreadsheet Analysis and Native Excel Modeling Capabilities

The sharpest point of divergence between F2 and Rogo is how they handle Excel: F2 deterministically computes within existing, live workbooks, while Rogo generates new Excel models from scratch. For private market investors who spend 80% of their time auditing complex, borrower-provided models, F2 operates a native, server-side spreadsheet engine rather than forcing an LLM to read Excel files as flat text. By evaluating real formulas, cross-sheet references, and circular logic directly within the .xlsx file, F2 guarantees the same deterministic output every time, whereas Rogo's workflow is oriented toward building new analysis.

F2’s Approach: Deterministic Computation Within Live Excel Workbooks

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.

Rogo’s Approach: Generating New Excel Models for Export

Through its Subset acquisition, Rogo's Sheets Agent lets users build and export financial models with AI assistance. However, this is oriented toward generating new analysis (comp tables, precedent transactions) rather than computing within existing, multi-tab workbooks from a data room.

The Bottom Line: F2 operates on existing Excel files; Rogo generates new Excel outputs. For buy-side firms receiving complex borrower models, F2’s deterministic engine is the reliable path. Same input, same output, every time.

Data Integrations and Breadth: Market Research vs. Deal-Specific Analysis

No other finance AI platform matches Rogo’s data partnership network:

• S&P Capital IQ: fundamentals, estimates, ownership, transactions
• LSEG: company fundamentals, M&A transactions
• PitchBook: private company, deal, and fund data
• Preqin: private capital ecosystem intelligence
• Third Bridge: expert interview transcripts
• FactSet and Crunchbase: additional market and startup data

Plus 65M+ pre-loaded external data sources, including SEC filings, earnings transcripts, and investor presentations.

F2 currently integrates with FactSet and PitchBook — but these aren’t generic API wrappers. F2’s integrations are 30+ specialized tools, each with automatic citation tracking, retry logic, and error handling. Every data point automatically gets a traceable citation and flows into source tabs in the report’s backing spreadsheet. The data doesn’t just appear in chat — it becomes auditable, formula-ready input in a real .xlsx file.

Auditability and Provenance: Computational Formulas vs. Referential Citations

Both platforms provide source citations, but the architecture differs — and it matters for institutional governance.

F2’s Audit Trail: Formula-Backed Computational Provenance

The chain is Report claim -> Databook cell -> Source tab -> Original file. Every report is backed by a real .xlsx file with formula-based analysis tabs referencing source data tabs. A reviewer can inspect the formulas and trace each number to its source.

Rogo’s Audit Trail: In-Cell Referential Citations

Every cell in a Rogo-generated spreadsheet includes in-cell citations linking to the source data.

For IC settings where the CFO asks, "Show me the formula," F2’s databook model is more defensible. For research settings where the question is, "Where did this data come from?" Rogo’s citation model works perfectly.

Workflow Durability: Deal Execution Infrastructure vs. Research Agents

F2’s durable workflow orchestration enables multi-step execution with automatic retry and recovery. A report generation job can run autonomously for 60 minutes across dozens of tool calls. Automatic context compaction at 150k tokens keeps recent items verbatim while summarizing older context. For a 40-tab financial model requiring 100+ tool calls to spread, F2 treats this as a production infrastructure problem to ensure the work gets done reliably.

Rogo provides deep research agents that autonomously find precedent transactions and generate industry reports. These are highly powerful, but they are oriented toward research generation rather than the multi-hour computational workflows that characterize deal diligence.

F2 vs. Rogo: Feature Comparison Matrix

Which AI Finance Platform Should You Choose?

The decision between F2 and Rogo ultimately hinges on your firm's position in the market: buy-side underwriting versus sell-side research. While both platforms are designed for the complexities of finance, they solve fundamentally different problems. F2 goes deep on auditable financial modeling for structured deals, whereas Rogo goes broad on market intelligence and pitch generation at enterprise scale. Here is a quick framework to help you align your platform choice with your team's core daily operations:

Who Should Choose F2?

• Private credit funds and commercial banks running structured underwriting workflows where the bottleneck is spreading, modeling, and producing auditable IC memos.
• Deal teams of 3+ people who need shared workspaces, versioned reports, and a system of record.
• Organizations that live in Excel and need F2’s LLMExcel engine for complex financial models and sensitivity scenarios.
• Buy-side firms processing data rooms where the primary input is a 200-file VDR and the output is a diligence memo with auditable calculations.

Who Should Choose Rogo?

• Investment banks that need AI-powered research, pitch generation, and deal screening deployed across hundreds of bankers.
• Firms that value data breadth for market research, precedent transactions, and earnings synthesis.
• Enterprise deployments at scale where per-seat cost must be manageable across hundreds of seats.

Conclusion: Choosing Between Deal Execution and Sell-Side Research

Rogo is the leading AI research platform for Wall Street, with the broadest data network in the industry and 25,000+ users at marquee banks. For sell-side research, pitch generation, and deal screening, it’s earned its position.
 

But for the work that defines buy-side investing — interrogating a borrower’s model, spreading financials with cell-level precision, producing IC-ready memos backed by auditable computation trails, and compounding institutional knowledge across every deal — F2 is purpose-built infrastructure that research platforms aren’t designed to replicate.

The question isn’t whether Rogo is smart enough or well-connected enough. It’s whether your team needs a research tool or a deal-execution platform. For institutional investors, the answer is usually the platform — and the firms that get the most value from AI will likely use both.

FAQs

Is Rogo a direct competitor to F2?

Partially. F2 identifies Rogo as its closest competitor, but the strategic focus differs significantly. Rogo is optimized for sell-side research; F2 is optimized for buy-side deal execution. The overlap is narrow, and the platforms serve different phases of the deal lifecycle.

Can Rogo spread financials from a data room like F2?

Not in the same way. Rogo’s Sheets Agent generates new financial models, but it doesn’t compute within existing multi-tab workbooks from a data room. F2’s LLMExcel engine opens the actual .xlsx file, evaluates real formulas natively with 50+ deterministic operations, and uses edge verification to prevent truncation of large models. For buy-side firms receiving complex borrower models, F2’s architecture is built for the problem.

Why is F2 more expensive per seat?

Different deployment models. F2 serves smaller teams running intensive multi-hour analyses on individual deals with unlimited usage and durable workflow orchestration. Rogo serves hundreds of bankers running high-volume, shorter research queries. The total annual spend per firm is often similar despite the per-seat gap.

Does F2 train on client data?

No. F2 operates under a strict Zero Data Retention agreement. Your data is never logged, stored, or used to train or fine-tune any AI models by us or our underlying API providers. Your data is processed in-memory to execute your immediate request and is instantly discarded. F2 is SOC 2 Type II compliant.

Does Rogo have a precedent deal library?

No. F2’s precedent deal library stores structured data from every deal underwritten on the platform — financials, leverage metrics, valuations, and industry classifications. Each new deal makes the next one faster. This is a compounding advantage that research platforms don’t offer.

Which platform has better data integrations?

Rogo has broader partnerships — its 7+ institutional data partners and 65M+ pre-loaded sources are unmatched. But F2’s FactSet and PitchBook integrations aren’t generic API wrappers — they’re 30+ tools each with automatic citation tracking that feeds directly into the auditable databook system. Breadth favors Rogo; depth of integration favors F2.

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

The routing layer is commodity — F2 is upfront about that. The proprietary value is the 100+ purpose-built tools, the LLMExcel engine, the durable workflow orchestration, and the databook/citation architecture. These aren’t RAG pipelines — they’re production infrastructure that took 18+ months to build. F2 also routes across multiple providers with progressive fallback (Gemini, GPT, Claude) for reliability.

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