Finster AI

What is Finster AI?

Finster AI is an enterprise AI research platform built for investment banking, asset management and private credit professionals. It pulls in filings, earnings transcripts and premium datasets, then answers questions, drafts research and builds slides with citations that trace each figure back to its source. The company says its proprietary data pipeline covers more than 1 million documents across over 8,000 companies, including EMEA and APAC coverage.

Finster AI was founded in 2023 by Sid Jayakumar, who previously spent seven years at Google DeepMind, and directories list its headquarters in London. It raised $15 million across Seed and Series A rounds in October 2025 to expand in New York. In August 2026 UBS Investment Bank and FactSet made a strategic investment, described in press coverage as its Series B. In October 2026 the platform was live and sold through enterprise contracts.

Key Features

• Cited research answers: Finster AI answers research questions with citations that link to the exact source sentence or spreadsheet cell. Analysts can open each reference and check the figure before it reaches a client deck or investment memo.
• Proprietary financial data pipeline: Finster AI runs its own document pipeline, which the company says covers more than 1 million documents across over 8,000 companies. The coverage includes filings, investor relations material and EMEA and APAC companies.
• Proactive agents and Tasks: Finster AI runs bespoke agents on a schedule or when a set event occurs, such as a new filing. These Tasks carry out multi-step workflows and surface developments without waiting for a fresh prompt.
• PowerPoint and Excel output: Finster AI turns research into slides that follow a firm’s own branding and templates. Press coverage of the FactSet partnership says the platform connects data and analytics with Microsoft Excel and PowerPoint.
• Coverage dashboard: Finster AI includes a dashboard that tracks filings and research changes across the companies a team covers. Bankers can monitor live deals and investment theses from one workspace instead of several terminals.
• Explorer search and screening: Finster AI’s Explorer interface combines natural-language questions with keyword search across its corpus. Directory analysis also notes screening that mixes quantitative filters with plain-English queries.
• Model-agnostic design: Finster AI works with several large language models and lets firms bring their own model. That design avoids lock-in to one AI vendor as model quality and internal approval lists change.
• Premium data connectors: Finster AI connects to FactSet and Morningstar data streams, real-time SEC filings and a PitchBook Premium Connector, according to case studies and directory listings. Firms can also link internal data systems and CRMs.

Pros

• Auditable outputs: Finster AI ties every answer to a sentence or table cell, which makes review and compliance checks faster. Airframe’s analysis singles out this traceability as the product’s main strength.
• Finance-specific security: Finster AI states SOC 2 Type II compliance and says it never trains its models on customer data. It also lists SAML single sign-on, SCIM provisioning, MFA and role-based access controls.
• Flexible deployment: Finster AI offers single-tenant environments and containerised VPC deployments inside a client’s own infrastructure. US and European data regions suit firms with data residency rules or material non-public information.
• Strong institutional backing: Finster AI counts UBS Investment Bank and FactSet as strategic investors as of August 2026. Earlier rounds were led by FinTech Collective and Peak XV, which suggests the product has runway.
• Built for banking deliverables: Finster AI produces briefing decks, comps, earnings updates and company primers rather than generic chat replies. Its templates fit repeatable work that junior bankers and analysts handle every week.

Cons

• No public pricing: Finster AI does not publish plan prices, and directories report custom contracts paid annually in advance. Smaller firms cannot judge cost without a sales conversation and procurement review.
• No self-serve trial: Finster AI is sold to institutions through sales-led onboarding, and directories found no free tier. Individual analysts and students cannot test it before their employer signs a contract.
• Few independent reviews: Finster AI has sparse third-party reviews on sites such as G2 and TrustRadius, according to Airframe. Most performance claims come from the company and its partners rather than user reports.
• Narrow audience: Finster AI is designed for investment banks, asset managers and private credit teams. Corporate finance staff or retail investors may find the scope and contract model a poor fit.
• No mobile app: Finster AI runs on the web and inside Microsoft Office, and directories found no mobile or browser extension apps. Bankers who need answers on the move must rely on a laptop.

Who is Using Finster AI?

• Investment bankers: build briefing decks, comps and pre-transaction company models.
• Asset managers: monitor holdings, filings and market-moving events across coverage lists.
• Private credit teams: research borrowers and draft credit memos with traceable sources.
• Research and analytics teams: automate earnings updates and recurring sector primers.

Pricing

Finster AI uses custom enterprise pricing and does not publish plan prices on its website (checked October 2026).

Directory listings report that contracts are paid annually in advance and vary with services and the number of authorised users. Any figure you see elsewhere is an estimate, so request a quote from Finster AI before budgeting.

What Makes Finster AI Unique?

Sentence-level and cell-level citations set Finster AI apart from general chat assistants used for finance research. Each claim in an answer, memo or slide can be traced to the filing line or spreadsheet cell behind it, which matters when outputs go to clients or investment committees. Pairing that with scheduled agents, private VPC deployment and a founder from Google DeepMind gives banks an assistant shaped around audit and confidentiality needs.

Compatibilities and Integrations

• Platforms: Web application plus Microsoft Excel and PowerPoint.
• Data sources: FactSet, Morningstar, SEC filings, PitchBook Premium Connector and the company’s own document corpus.
• Enterprise identity: SAML SSO, SCIM provisioning, MFA and directory sync with Microsoft Entra ID and Google Workspace.
• Models and deployment: Several LLMs or your own model, in single-tenant or VPC environments.

Finster AI Tutorials

Getting started with Finster AI runs through an enterprise onboarding process rather than a sign-up form.

1. Request a demo through the Finster AI website and outline your team’s workflows.
2. Agree on deployment, data regions and single sign-on with your IT and compliance teams.
3. Connect licensed data sources such as FactSet or PitchBook and any internal systems.
4. Ask a research question in Explorer and open the citations to check the sources.
5. Set up a scheduled Task, such as an earnings update, and export the output to PowerPoint.

How We Rated It

Summary

Finster AI suits investment banks, asset managers and private credit teams that need fast research with sources they can defend. Its main limitation is access: there is no public pricing or self-serve trial, and independent user reviews remain scarce. Firms already licensing FactSet or PitchBook will get the most from it. Ask for a pilot on one recurring deliverable, such as earnings updates, and measure time saved before a wider rollout.