How to Connect Financial Data Providers to an LLM

To connect an LLM to financial data, first identify what the provider actually sells: a downloadable file, an API, or an MCP connection. Give the assistant permissioned access to the relevant records, return source and date information with each result, and check important numbers against their underlying evidence. A fluent answer is not a verified financial fact.

This matters most in private markets. A model can explain a precedent transaction or draft a buyer-research note, but it cannot fill in an undisclosed purchase price, infer a firm’s current acquisition appetite from an old holding, or turn a family-office directory entry into proof of available capital. The data layer determines what questions the assistant can answer; the review process determines whether those answers are usable.

Choose the connection that the data supports

Access route	Practical setup	Good fit	Main constraint
Licensed CSV or XLSX	Import permitted fields into a controlled table or search index; let the assistant retrieve matching rows.	A purchased directory or periodic research file.	Updates and citations are your responsibility; a file does not become a live provider API.
Provider API	Call approved read endpoints from a server-side tool and return only the fields needed for the question.	Repeatable queries against records that can change.	Access, rate limits, permitted storage and available fields depend on the contract.
Provider MCP server	Connect a compatible AI client to the provider’s published tools or resources, then test each tool’s result.	Analyst-led research inside an AI workflow.	MCP describes an interface, not data quality or permission to use every record.

The Model Context Protocol documentation describes how an AI application can use a server’s tools and resources. For comparison, the SEC’s EDGAR data APIs expose public filing histories and extracted XBRL financial facts through a conventional API. Neither connection method removes the need to check periods, entities and source documents.

Set up the route you actually have

1. For a spreadsheet: confirm that your licence permits the intended internal use, store the purchased file in a controlled workspace, and import it into a table or retrieval index. Give each row a stable ID and record the file version and refresh date. Expose a read-only search to the assistant; return matching rows, not the whole file by default.
2. For an API: create a server-side read-only adapter for the provider’s permitted search and record endpoints. Keep the credential in that service, map the response into your evidence fields, and return a clear error when a field is missing or an allowance is exhausted.
3. For MCP: use a compatible AI client and the provider’s current setup instructions on an eligible plan. Add the provider-issued server address and credential in the client’s protected settings, inspect the tools it exposes, and test one known record before relying on open-ended searches. Check whether calls consume a search or reveal allowance.

For every route, preserve an entity ID, record ID, observation date, value type, unit or currency, and a provider reference where available. Use an explicit empty value for an undisclosed metric. This small record contract makes it easier to spot a wrong entity match or a multiple calculated from mismatched periods.

What four private-market providers can contribute

Amafi and all four providers in this table share ownership. They illustrate different data and access models; inclusion is not an independent endorsement or a ranking. Capabilities below are provider-stated and were reviewed on 6 October 2026. Amafi’s methodology explains how it separates provider facts from editorial assessment.

Provider	Data the assistant could use	Access and boundary
SeekCompanies	Planned verified private-company records for target research and market mapping.	Coverage page: still in research and not yet on sale. There is no file, API or MCP connection to use today.
SeekFamilyOffice	A regional or global directory for family-office identification and public contact-route research.	Coverage and methodology: sold as a one-time spreadsheet download. A buyer could index an authorized copy internally; the provider does not advertise a native API or MCP server. A record does not prove current investment appetite.
SeekPrivateEquity	PE firm records and disclosed historical portfolio-company relationships for sponsor research.	Plans and access: Evaluation includes a limited relationship sample; Pro and Enterprise include MCP access. A historical holding does not prove present buyer interest.
PrivyLogic	Disclosed M&A precedent terms and valuation-multiple research.	Current pricing app: browser access today; API and MCP are described as planned, despite the marketing overview advertising them. Do not design a live connector around them until the provider confirms launch. Missing private-deal terms remain unknown.

These sources answer different questions. A private-company record is a possible target, a PE portfolio relationship is historical evidence about a sponsor, a family-office entry identifies an organization, and a precedent transaction is evidence about a disclosed deal. Joining them by a similar name without checking identity can produce a convincing but false narrative.

Build a research workflow that keeps the evidence attached

Suppose an analyst asks: “Which PE sponsors have owned software companies relevant to this seller, and what disclosed precedent multiples might inform an initial discussion?” A useful workflow is:

1. Define the entities and period. Record the seller’s sector, geography, size and the date of the research. Keep fund, management company, portfolio company and transaction as separate entity types.
2. Retrieve sponsor evidence. Query an authorized PE source for disclosed portfolio relationships. Return the sponsor, company, relationship type, observed dates and a record reference where available. Label a match as historical activity, not current investment appetite.
3. Retrieve precedent evidence separately. Search a transaction source for relevant announced deals. Keep the announcement date, target, buyer, currency, disclosed enterprise value, metric period and record reference. Do not assume every deal has enough public data for a multiple.
4. Calculate outside the prose. If both enterprise value and a period-matched EBITDA value are disclosed and comparable, calculate EV/EBITDA in code or a spreadsheet. Show the inputs, units, period and formula. Otherwise show “not disclosed” or “not comparable.”
5. Review the short list. Open available source records and, where material, the underlying filing or announcement before using the output in a valuation discussion or outreach decision. The SEC’s filing search is one public source for U.S. issuer disclosures; private transactions may have much less public evidence.

The assistant’s output should keep a compact evidence line for every material claim: entity, record type, reported or calculated value, currency/unit, relevant date or period, provider record reference, underlying source if available, and review status. A blank source field should stay blank. A provider record reference is useful for traceability, but it is not necessarily a link to the original announcement.

Put limits around the connection

Start with read-only access and the smallest dataset the task requires. Keep API credentials in the application or approved client configuration, not in a prompt or shared document. Check the provider’s licence before uploading a purchased file to a third-party AI service, retaining records, or redistributing an answer. For MCP, inspect the server and each exposed tool before authorizing calls; a compatible connection alone says nothing about the quality or permitted use of the result.

Treat retrieved text and documents as data to analyze, not instructions to obey. An announcement, spreadsheet note or web page can contain irrelevant or malicious directions. Keep the assistant’s action permissions separate from the retrieved content, and require a person to approve external messages, CRM writes and investment-facing conclusions.

The practical test is a small, known sample: ask questions whose answers your team can verify, measure missing or mistaken records, inspect the citations, and check whether the output says “unknown” when the source is silent. For a broader view of where this data layer sits in a deal workflow, see Amafi’s AI in M&A guide and sourcing-copilot comparison.

Frequently asked questions

Can I connect any financial data provider to an LLM?

Only if the provider offers an access route and your licence permits the proposed use. A downloadable file can support a controlled internal search index; API and MCP access depend on the provider’s product and plan.

Is MCP the same as an API?

No. An API is a provider’s programmatic interface. MCP is a standard way for an AI client to discover and call tools or read resources exposed by a compatible server. A provider may offer one, both or neither.

Does connecting a data source make an AI answer reliable?

No. The assistant still needs to identify the entity and record, preserve dates and units, distinguish reported facts from estimates, and verify material conclusions against available underlying evidence.