Finster AI: Coverage Update — 21 Oct 2026

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.

Finster competes in the investment-banking segment of this market, most directly with Rogo, and with Hebbia and AlphaSense for document and content research. Its distribution through FactSet sets it apart: few independent vendors in our coverage reach clients through an incumbent data platform.

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

• Incumbent validation: FactSet invested and uses Finster as a core engine of FactSet AI for Banking; UBS Investment Bank joined as a strategic investor in the Series B.
• Distribution through FactSet: FactSet AI for Banking entered preview with select clients in March 2026 and is rolling out through 2026, giving Finster potential reach without a large sales force.
• Deployment flexibility: Single-tenant containerised VPC inside the client's infrastructure, bring-your-own model and US or EU regions — confirmed in the FactSet release.
• Identity and governance controls: SAML SSO, SCIM, MFA, Entra ID and Google Workspace sync, role-based access, a full audit trail and a contractual no-training clause.
• Event-driven agents: Triggers run multi-step workflows when events such as an earnings transcript arrive and deliver the result by email.
• AI and finance leadership: The founder spent seven years at Google DeepMind and worked on Gopher; the COO was Morgan Stanley's Global COO of Research.
• Licensed content: PitchBook, Preqin, Third Bridge, MT Newswires and Crunchbase, with FactSet data and broker research inside the FactSet edition.

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 Finster. 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.

• Customer evidence: We did not locate a named production customer, case study or user review. The company describes several unnamed tier-one banks and asset managers; buyers should ask for references.
• Attestation detail: SOC 2 Type II compliance is claimed without a report date, auditor or trust centre; ISO 27001 and a subprocessor list were not located.
• Information barriers: No explicit MNPI or information-barrier control was located, which matters for sell-side deployment.
• Data-use terms: The terms prohibit training on user data but allow its use to improve search and accuracy, and the sharing of anonymised aggregate usage data with data providers. Model providers are not named.
• Standalone versus FactSet edition: The strongest Excel, PowerPoint and broker-research features appear tied to FactSet AI for Banking. Buyers should confirm what the standalone product includes and which entitlements are separate.
• Scale: Headcount is not disclosed, no roles were open at our review, and the Series B amount was not announced.
• Evaluation evidence: No benchmark or accuracy methodology has been published.
• Pricing: Pricing is not published; fees are set by order form and paid annually in advance.

Finster AI, Inc., a Delaware corporation, was founded in 2023 by Sid Jayakumar, who spent seven years at Google DeepMind working on large language models including Gopher, after earlier experience in prime brokerage at Morgan Stanley. The company operates from London, with a New York presence. Chris Andrews, formerly Global COO of Research at Morgan Stanley, joined as COO in 2025.

Finster raised $15m across a seed round led by Peak XV and a Series A led by FinTech Collective, announced in October 2025. FactSet made a minority investment in March 2026 alongside a partnership in which Finster powers FactSet AI for Banking, and UBS Investment Bank joined as a strategic investor in a Series B announced in August 2026; the Series B amount was not disclosed.

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

Finster offers a standalone platform and powers FactSet AI for Banking. Figure 2 sets out our assessment of how the product maps onto the investment workflow.

Explorer and Dashboard

Cited search and Q&A, and a dashboard of covered companies combining filings, broker research, financials and internal research with change tracking.

Tasks and Triggers

Scheduled and event-driven multi-step agents, for example running a workflow when an earnings transcript arrives.

PowerPoint automation

Slides built in the firm's style, with interactive charts generated on demand.

FactSet AI for Banking

Finster as a core engine inside FactSet, with FactSet data, Excel formulae, broker research and slide tools.

Scope of the product today

We did not locate a public API or developer documentation, an MCP server, a standalone Excel add-in, or Word, Outlook or Teams add-ins.

Finster describes an LLM-agnostic agent orchestration platform with bring-your-own model, linking filings and internal data under bank controls, with citations to the table cell or sentence. Model providers are not named.

The team's pedigree is strong, but no benchmark, error rate or evaluation methodology has been published. Buyers should test accuracy on their own documents.

Finster licenses PitchBook, Preqin, Third Bridge expert transcripts, MT Newswires and Crunchbase, and inside FactSet AI for Banking adds FactSet data and broker research from more than 1,800 brokers. It claims more than a million documents across 8,000+ companies.

None of these partnerships is exclusive — Third Bridge and PitchBook also supply peers — and the transcript, filings and consensus vendors for the standalone product are not named.

Finster fits banker workflows: comps, primers, strip profiles, CIMs, auto-updating decks and alerts, with event-driven agents and slides in the firm's style.

For buy-side readers the fit is narrower. Idea generation and screening were not located, and Excel and Office delivery outside the FactSet edition is unclear.

Finster presents one of the most complete deployment stories in our coverage: single-tenant containerised VPC in the client's infrastructure, bring-your-own model, US and EU regions, SAML SSO, SCIM, role-based access, a full audit trail and a contractual no-training clause. Figure 3 sets out what our public review located.

We would encourage any firm evaluating Finster to request the SOC 2 Type II report, penetration-test summary and subprocessor list, confirm information-barrier controls and model providers, and negotiate the data-use clause where needed.

The offsetting factors are substantial. FactSet and UBS both invested after their own diligence, and the VPC deployment is confirmed in FactSet's release rather than only in marketing.

Finster does not publish pricing. Fees are set by order form and paid annually in advance in US dollars, and a free evaluation is available under the terms.

The value case is banker-hour substitution, with FactSet distribution reducing procurement friction for FactSet clients. Buyers should confirm which data entitlements are separate.

Finster sits in the Emerging quadrant of the GAII Intelligence Matrix. Deployment controls and momentum support market strength, but evaluation and information-barrier evidence hold it below the line; investment utility is held back by buy-side fit and data breadth outside the FactSet edition.

GAII coverage universe, 21 October 2026. Scores as currently published on the GAII Intelligence page; preliminary and based on public information.

11.1 Where Finster is strongest relative to peers

• Against independent peers, on distribution through an incumbent data platform and strategic bank investment.
• Against SaaS-only vendors, on single-tenant VPC deployment and bring-your-own model.
• Against general assistants, on banking deliverables and event-driven agents.

11.2 Areas of narrower functional focus

Finster has built depth in banking workflows and deployment flexibility rather than in public proof or buy-side research.

• Evidence: no named customers, case studies or published evaluation located.
• Buy-side: no screening or idea-generation product located.
• Developer access: no public API or MCP located.
• Controls: no explicit information-barrier feature or trust centre located.

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.

• Two named production references, including whether UBS is a deployed client.
• The SOC 2 Type II report period and auditor, and ISO 27001 plans.
• The subprocessor list and the model providers used.
• Information-barrier and MNPI controls.
• Zero-data-retention terms with model providers.
• The scope of the data-use clause in the terms.
• Which features are standalone and which require FactSet AI for Banking.
• Data entitlements included versus licensed separately.
• The Series B amount and runway.
• Headcount and hiring plans.
• Plans for an API or MCP server.
• Pricing and standard contract terms.

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 Finster specifically.

If you are a fundamental equity manager

• If you are a FactSet client, evaluate through FactSet AI for Banking first.
• Test Triggers on a live earnings cycle.
• Ask for a reference at a firm like yours.

If you are responsible for technology or vendor risk

• Open with Figure 3; the VPC and BYO-model options are the starting point.
• Request the SOC 2 report and subprocessor list.
• Confirm deal-team walls before any sell-side deployment.

If you are assessing the category rather than the company

• Incumbent data platforms are choosing AI-native engines rather than building alone.
• Single-tenant deployment is becoming a requirement for bank-grade AI.
• Public customer proof is the gap for most newer vendors.

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