F2

What do they actually do

F2 is a SaaS platform for private credit, private equity, and commercial bank deal teams that turns an uploaded data room (PDFs, Excel, PowerPoints) into an audit-ready underwriting workspace. It parses documents, extracts and reconciles financials, computes standard metrics (e.g., EBITDA, leverage, coverage) with links back to the original source cells/pages, and drafts firm‑formatted memos users can edit via an in‑product chat/agent experience. The product includes enterprise controls (e.g., SOC 2, encryption, MFA) and states customer data is not used to train models by default product page, launch announcement.

The company launched publicly in 2025 after spinning out of Arc, with the product already in use at “dozens” of funds and “hundreds of active users” across private credit, PE, and banks; the spin‑out was accompanied by a seed round to scale the platform and go‑to‑market launch announcement, PR Newswire, YC profile.

Who are their target customer(s)

• Private credit deal analysts: They spend hours copying figures from PDFs/Excel, reconciling inconsistent tables, and re-checking extractions, which slows screening. F2 parses data rooms, extracts and reconciles financials, and links every computed number back to its source product page, launch announcement.
• Private equity associates: They must draft firm‑formatted memos and benchmark new opportunities against past deals, but historical context is scattered across decks and spreadsheets. F2 maps opportunities to firm criteria and past transactions and auto‑drafts memos in templates to reduce re‑work F2 2.0 blog, product page.
• Commercial bank underwriters/loan teams: They need fast, auditable underwriting for credit approvals under regulatory scrutiny and can’t rely on opaque outputs. F2 emphasizes audit‑ready calculations with a clear source trail and enterprise controls (e.g., SOC 2, encryption) product page, PR Newswire.
• Heads of deal teams / partners: They are bottlenecked by inconsistent analysis and slow analyst cycles when making go/no‑go decisions. F2 standardizes extractions, surfaces key metrics, and accelerates comparison to prior deals to shorten time from data room to decision launch announcement, product page.
• Compliance, risk and operations managers: They worry about data leakage, model training usage, and secure deployment. F2 provides enterprise controls and states customer data isn’t used to train models by default, supporting secure enterprise use product page.

How would they acquire their first 10, 50, and 100 customers

• First 10: Convert existing Arc relationships into paid, tightly scoped pilots using one or two live deals per team, delivering measurable time savings and an audit trail, and capture short case studies and testimonials launch announcement, PR Newswire.
• First 50: Hire industry‑experienced reps and scale a repeatable 2–4 week pilot that ingests a real data room, produces a firm‑format memo, and demonstrates auditability for compliance sign‑off; pair targeted outbound with webinars and early case studies to speed procurement product page, F2 2.0 blog.
• First 100: Package offerings and build channels: standardize deployment templates and security/legal bundles to shorten procurement cycles, and partner with data‑room vendors, placement agents, and law firms; combine an enterprise success team for large accounts with a lighter‑weight mid‑market tier product page, PR Newswire.

What is the rough total addressable market

Top-down context:

Private markets AUM totaled $13.1T as of June 30, 2023, with private debt AUM at $1.7T—indicating large, ongoing volumes of underwriting and diligence work across PE, private credit, and banks that rely on document‑based analysis McKinsey 2024 Global Private Markets Review, McKinsey 2024 summary.

Bottom-up calculation:

Assume ~3,500 PE/private credit firms (a subset of 17k PE investors tracked by PitchBook and ~2,900 active private debt firms per Preqin) plus ~1,500 banks with active corporate lending teams (subset of >4,400 FDIC‑insured institutions) as buyers, with an average $120k annual contract per institution—yielding roughly $600M in annual software spend TAM PitchBook, Preqin private debt, FDIC.

Assumptions:

• Only mid‑to‑large PE/private credit managers and banks with material underwriting workflows are in scope (not all 17k PE investors or 4.4k+ banks).
• Average deployment equates to ~25 seats or an enterprise license blending to ~$120k ACV per institution; larger funds/banks pay more.
• Expansion into adjacent workflows (monitoring, portfolio ops, sell‑side) could increase TAM but is excluded from this base estimate.

Who are some of their notable competitors

• Eigen: Enterprise document-intelligence/IDP used in financial services to extract data and tables from unstructured documents; strong generalized extraction but not a private‑markets underwriting workspace like F2.
• Datasite: Widely used virtual data‑room platform with ML features (search, redaction, analytics); focused on deal execution and sell‑side processes, not audit‑ready underwriting calculations or firm‑specific memo templates.
• Alkymi: Automates ingestion and normalization of investment and financial documents into structured workflows and integrations; emphasizes data pipelines for funds rather than in‑product underwriting dashboards and memo generation.
• Dili (Dili.ai): AI diligence platform offering automated data‑room summarization, metric extraction, and instant memos for PE/real estate; positioned as a turnkey diligence assistant rather than an audit‑trail‑first, firm‑custom underwriting system.
• Kira (Litera): Contract review and clause extraction at scale for legal/diligence teams; strong for legal clause identification, but not focused on reconciling financial line items into audit‑ready underwriting metrics.