Rogo
Agentic research platform for investment banks and funds. Tuned on filings, credit agreements and transcripts, producing cited memos, Excel models and slide decks.
Overview
Rogo builds agents for capital markets work. The pitch is narrow on purpose: company profiles, competitive benchmarking, diligence summaries and investment memos, produced with citations back to the underlying filing and delivered as Excel or PowerPoint rather than as prose in a chat window. Data comes from licensed feeds including S&P Global, FactSet and Crunchbase alongside a document corpus the company puts above 50 million items, and connectors pull in a firm's own SharePoint and CRM so private deal history is in scope too.
Technically the work sits in two places. First, post-training on document types that defeat naive chunking, since credit agreements and long filings depend on cross-references and defined terms that a fixed window destroys. Second, evaluation. Big Finance Bench, published 27 May 2026, scores ten frontier systems across research, valuation, document analysis and investment synthesis using more than 15,000 rubric criteria, each question run three times in a minimal agent loop with EDGAR, web access and a Python sandbox. Publishing that under your own name, with your own product implicated, is not a common move.
The commercial trajectory is steep. A $75M Series C in January 2026 at $750M was followed by a $160M Series D led by Kleiner Perkins in April 2026 at roughly $2B, with J.P. Morgan Growth Equity Partners participating, and the company reports 50,000+ users across 350+ institutions with about 150,000 daily queries. Deployment is single-tenant, contracts are multi-year and pricing is quoted, not listed. For an engineer the useful takeaway is the benchmark and the output-format discipline, both of which transfer to any vertical agent you build yourself.
Key Features
- ✓ Agents that carry deal tasks end to end: company profiles, comparable analysis, diligence packs and first-draft investment memos
- ✓ Models post-trained on capital markets document types, specifically SEC filings, credit agreements and earnings transcripts
- ✓ Licensed data connections including S&P Global, FactSet and Crunchbase over a corpus of 50 million-plus financial documents
- ✓ Outputs land as Excel models and slide decks rather than chat text, which is what the workflow downstream actually consumes
- ✓ Connectors into internal SharePoint and CRM so proprietary deal history sits beside market data
- ✓ Single-tenant deployments with SOC 2, ISO 27001, GDPR and EU AI Act compliance posture
Where it holds
- • Big Finance Bench, published 27 May 2026, graded ten frontier systems against 15,000+ rubric criteria with EDGAR access and a Python sandbox. Public, and more evaluation work than most vertical vendors bother with
- • Answers carry traceable citations back to the source filing, which is the difference between usable and unusable in a diligence context
- • Genuine adoption rather than pilot theatre: 50,000+ bankers and investors across 350+ institutions, Lazard, Jefferies, Rothschild and Nomura among them
- • Output formats respect the real artifact chain, so analysts are not retyping into Excel
Where it breaks
- • Completely closed to outside evaluation. No API sandbox, no published pricing, external per-seat estimates near $3,300 per year before platform fees
- • Much of the value rests on licensed data a firm often already pays for through FactSet or S&P, so you may fund the same feed twice
- • The model layer is other people's frontier models with domain post-training on top. The defensible part is data plumbing and evals, and both are copyable
- • Narrow by design. Outside capital markets document work there is little reason to look at it
My Take
Finance verticals usually amount to a chat box bolted onto a data vendor. This one goes further, because the post-training targets exactly the documents that break generic retrieval: 300 page filings and credit agreements where the answer turns on a defined term buried in a schedule. The Big Finance Bench release in May 2026 is the thing worth reading, since it shows frontier models still stumbling on multi-step valuation work that a second-year analyst handles. Priced for a bank though, roughly $3,300 per seat per year by external estimate, so no small team pilots this casually.
Quick Info
- Pricing:
- paid
- Openness:
- Proprietary
- Starting at:
- Enterprise only, quote-based, sold on multi-year contracts with no self-serve entry. Sacra's external estimate lands near $3,300 per seat per year, with platform and implementation fees on top and single-tenant deployment for firms with data residency requirements. No published rate card, no trial.
- Added:
- Aug 2026
- Updated:
- Aug 2026
Use Cases
Judge it on your own work
The notes above say where Rogo holds and where it breaks. The fastest check is your own workload.
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