Rogo: Initiation of Coverage

GAII Intelligence defines the AI Research & Intelligence market as software that applies generative and agentic AI to the research process of professional investors: locating candidate securities, assembling and interrogating evidence, producing written analysis with traceable sources, and monitoring positions once held. Buyers are hedge funds, long-only managers, multi-manager platforms, family offices and allocators; the economic buyer is typically the chief technology officer, the head of research, or a portfolio manager with a technology budget.

Capabilities we treat as mandatory

• Evidence retrieval at scale: the ability to search and reason across regulatory filings, transcripts and news, and across documents the client supplies.
• Source traceability: every material claim in an output must be reachable back to the passage that supports it.
• Institutional data handling: a stated position on where client data goes, how long it is held, and whether it is used for model training.

Capabilities we treat as differentiating

• Persistent state: the system remembers a thesis, a coverage list or a position between sessions and acts on it without being re-prompted.
• Workflow surfaces: delivery inside the applications analysts already use — the spreadsheet, the research management system, email and messaging.
• Owned or exclusive content: material that a competitor cannot obtain by signing the same third-party contract.
• Published evaluation: a measured, disclosed accuracy methodology rather than an assertion of quality.

Rogo competes most directly with Hebbia for document-heavy finance work and with Endex for Office-native deliverables. Its banking focus distinguishes it from buy-side research specialists such as Rogo and LinqAlpha. Frontier labs — Anthropic, OpenAI and Microsoft Copilot — are moving into the same workflows.

The following are the capabilities and characteristics that, in our assessment, most distinguish Rogo within the AI Research & Intelligence market. Each is drawn from publicly documented product capability, published interviews with management, or independent hands-on review.

• Capital and vendor viability: About $310–345m raised from Khosla, Thrive, Sequoia and Kleiner Perkins, with a reported ~$2bn valuation in April 2026 and a September 2026 strategic round from global banks. Near-term continuity risk is the lowest in our coverage.
• Deliverable fidelity: Comps, DCF, LBO and merger models, pitchbooks, CIMs and memos are produced in Excel, PowerPoint and Word in the firm's own template and house style, with cell-level source links.
• Autonomous agents: Felix takes tasks by email and works continuously on deal screening, CIM generation, buyer outreach and data-room diligence.
• Deployment and isolation: Dedicated Kubernetes clusters and data stores, single-tenant, multi-cloud and on-premises options are described, with private model copies and permissions inherited from Datasite and Snowflake.
• Model-agnostic routing: Workflows route across OpenAI, Anthropic and Google models, and new frontier models are added within days. Rogo's published BigFinanceBench shows routing gains over any single model.
• Named client base: Lazard, Rothschild & Co, Jefferies, Moelis, Nomura, Baird and Truist are named by the company or its investors, with more than 50,000 users reported.
• Adoption model: Forward-deployed teams of former bankers drive change management, supported by offices in New York, London, Singapore and Sydney.

The following are factors that prospective clients should weigh, and areas where our public review did not find sufficient evidence to reach a settled view. Several are common across vendors at this stage of the market and are not specific to Rogo. We encourage prospective clients to raise each of them directly with the company; we have done the same, and will update this note as answers arrive.

• Attestation evidence: SOC 2 Type I/II and ISO 27001/42001 are listed by third-party indexes of the trust centre; we could not read the trust centre directly. Prospective clients should request the reports, scope and period.
• Model-provider terms: No-training and private-copy commitments are documented for the Amazon Bedrock path; zero-data-retention terms for the OpenAI and Google paths were not located. We encourage buyers to obtain these for each provider.
• Information barriers: MNPI controls, information-barrier configuration and audit-log export were not documented publicly. Banks should pilot-test against their wall-crossing requirements.
• Buy-side workflow fit: The product is built around investment-banking deliverables. Persistent thesis monitoring and public-equity idea generation are less developed than at buy-side specialists.
• Benchmark scope: BigFinanceBench is Rogo-authored and evaluates foundation models rather than the Rogo product; a third-party data-quality allegation was unresolved at our review.
• Leadership and integration: A new chief technology officer was appointed in July 2026, the co-founder CTO's current role was not located, senior AI staff moved to OpenAI, and three acquisitions are being integrated during international expansion.
• Pricing and metrics: Pricing is not published, and user and revenue figures are company-reported with shifting definitions; no audited ARR has been disclosed.
• Frontier-lab competition: Anthropic, OpenAI and Microsoft are moving directly into finance workflows. Buyers should assess data portability and lock-in through institutional memory features.

Rogo Technologies, Inc. was founded in 2021 by Gabriel Stengel, formerly an analyst at Lazard, and John Willett, formerly at JPMorgan, both Princeton graduates, with Tumas Rackaitis as founding CTO. It emerged from stealth in early 2024 and is headquartered in New York, with offices in London, San Francisco and Singapore and hiring in Tokyo and Sydney.

Rogo has raised a $7m seed (AlleyCorp, 2024), $18.5m Series A (Khosla, 2024), $50m Series B (Thrive, 2025), $75m Series C (Sequoia, January 2026) and $160m Series D (Kleiner Perkins, April 2026), plus a strategic round from global banks in September 2026. Rahul Rekhi, formerly of Lazard and the US Treasury, is President, and Joe Xavier, formerly CTO of Grammarly, became CTO in July 2026.

Company-reported figures are shown as such and are not independently verified. Verification is carried into the vendor questionnaire.

Rogo presents an end-to-end agentic platform for finance, organised around the deliverables bankers and investors produce. Figure 2 sets out our assessment of how it maps onto the investment workflow.

Research and chat

Search and deep research across filings, transcripts, news and licensed data, with deep-linked citations.

Excel, PowerPoint and Word agents

Model builds, roll-forwards and sensitivities (from the Subset and Offset acquisitions); pitchbooks, CIMs and memos in the firm's template.

Felix

An autonomous agent assigned by email that handles deal screening, CIM generation, buyer outreach and data-room diligence.

Deal Room and Intelligence

Data-room and CRM integration, a 360-degree view of deals and relationships, institutional memory, and meeting notes from Arvo.

Scope of the product today

We did not locate a public developer API, a Rogo-hosted MCP server or native Outlook and Word add-ins. Rogo acts as an MCP client to Snowflake and is a first-wave partner for Copilot in Excel skills.

Rogo routes each workflow across OpenAI, Anthropic and Google models, has fine-tuned OpenAI models, runs a private Claude model on Amazon Bedrock and uses Gemini on Vertex AI. Retrieval runs over a proprietary pipeline indexing 50–65 million sources, and output carries deep-linked citations.

Rogo is one of few vendors to publish an evaluation dataset. BigFinanceBench tests foundation models and routing rather than the Rogo product itself, and a third-party data-quality challenge was unresolved at our review.

Licensed sources connected include S&P Global, FactSet, LSEG, PitchBook, Preqin, Crunchbase and Dow Jones Newswires, alongside SEC and international filings, transcripts and news. Client content comes from Intralinks and Datasite data rooms, Snowflake, SharePoint, CRM and prior-deal databases.

Rogo connects to the client's own data licences rather than reselling them, which buyers should confirm in contract. Expert-network content and aggregated broker research were not located.

Rogo's workflow strength is the banker's production cycle: models, comps, pitchbooks, CIMs and diligence, delivered in the firm's files and supported by forward-deployed former bankers.

For our buy-side readers the fit is narrower. Monitoring arrived as email-scheduled updates in May 2026, and we did not locate a persistent thesis object or public-equity idea generation comparable to buy-side specialists.

Rogo presents the most extensive enterprise posture in our coverage: dedicated-cluster isolation, single-tenant, multi-cloud and on-premises deployment, private model copies, SCIM and customer-managed keys, and a Security Advisory Board of bank CISOs and CTOs. Figure 3 sets out what our public review located.

We would encourage any firm evaluating Rogo to obtain the SOC 2 Type II report and ISO certificates directly, zero-data-retention terms for each model provider, the subprocessor list, and documentation of information-barrier controls and audit-log export.

The offsetting factors are substantial. Nine global banks have invested after their own diligence, Truist moved from structured pilot to rollout, and Rogo is hiring dedicated governance, risk and compliance staff for SOC 2, ISO 27001, ISO 42001 and the EU AI Act.

Rogo does not publish pricing. Third parties estimate about $3,300 per seat per year, and a credit centre governs usage. Rogo connects to existing data licences, and forward-deployed services are part of the delivery model.

The value case rests on banker-hour substitution, with vendor-reported evidence such as Baird's 600 users and 95% engagement. Buyers should request a multi-year cost schedule covering seats, credits and services.

Rogo is in the Leaders quadrant of the GAII Intelligence Matrix, with the highest Market & Innovation Strength in the cohort on capital, enterprise posture and momentum. It ranks first in the inaugural cohort.

GAII initiation universe, 20 October 2026. Scores as currently published on the GAII Intelligence page; preliminary and based on public information.

11.1 Where Rogo is strongest relative to peers

• Against research platforms, on the finished deliverable in the firm's own template.
• Against smaller specialists, on capital, deployment isolation and the scale of its client base.
• Against general-purpose copilots, on banking-specific agents, data-room integration and forward-deployed adoption.

11.2 Areas of narrower functional focus

Rogo has built depth in banking workflows and enterprise scale rather than in buy-side research persistence or proprietary content.

• Buy-side research: persistent thesis monitoring and public-equity idea generation less developed.
• Content: no expert-network or aggregated broker research located.
• Developer access: no public API or hosted MCP server located.
• Evidence: attestations and ARR not confirmed first-hand.

Scores are on a 0–10 scale. Each dimension score is the mean of its sub-criteria, and the GAII Overall Score is the weighted mean of the six dimensions. The detailed sub-criteria scores that build each dimension are set out in Appendix A.

These are the areas where our public review did not find sufficient evidence to form a settled view. We have put them to the company and will update this note when they are answered. Prospective clients may find them a useful agenda for their own evaluation call.

• The SOC 2 Type II report, audit period and auditor, and the ISO 27001 and ISO 42001 certificates.
• Zero-data-retention and no-training terms for each model provider.
• The subprocessor list.
• Information-barrier and MNPI controls, and audit-log export.
• SSO/SAML availability alongside SCIM.
• Pricing for seats, credits and forward-deployed services, and standard term.
• Company-stated ARR and the definition of active users.
• The share of revenue and users from buy-side clients.
• Roadmap for persistent thesis monitoring and public-equity idea generation.
• Plans for a public API or hosted MCP server.
• Status of the BigFinanceBench data-quality challenge and any evaluation of the Rogo product itself.
• Integration status of Subset, Offset and Arvo, and the founding CTO's current role.

This note is an assessment of a vendor against a defined framework, not a recommendation to buy or to decline. The following are the considerations we would put at the centre of an evaluation of Rogo specifically.

If you are a fundamental equity manager

• Test Felix and the model agents on a live deliverable in your own template.
• Probe monitoring and idea generation if your process is buy-side fundamental.
• Ask for a buy-side reference alongside the banking names.

If you are responsible for technology or vendor risk

• Open with Figure 3 and request the trust-centre documents directly.
• Obtain zero-data-retention terms for every model path, not only Bedrock.
• Pilot information-barrier controls before a firmwide rollout.

If you are assessing the category rather than the company

• Capital concentration is accelerating: the leader now raises at a multiple of the cohort.
• Banks investing in their vendors changes the diligence baseline for everyone else.
• Frontier labs entering finance make data portability a procurement question.

Each dimension score in Section 12 is the mean of the sub-criteria below.