Detect the company claim
Extract the company name, jurisdiction, identifier and the specific fact the agent needs to establish.
AI AGENT COMPANY GROUNDING
AI grounding with verified company records.
Ground AI-agent answers in company records. Resolve names, VAT numbers and EINs through API or MCP, then return the legal entity, source and retrieval context.
START WITHCompany mention, name or identifierREVIEWResolved entity + evidence context
Design the agent around evidence and policy.Define the agent host, tool scope, countries, evidence fields, exceptions and permitted actions.
Reviewed
THE PRACTICAL ANSWER
What does it mean to ground an AI answer in company data?
Company-data grounding gives an AI system evidence about the specific legal entity it is discussing. Before answering, resolve the name or tax identifier and country through VATFind, then provide the relevant returned fields and source context to the model. This reduces reliance on a remembered brand name or an unsupported tax ID. Keep a missing fact missing and ask for more information when several entities fit.
- An agent answers questions about a company, supplier, customer or merchant.
- A model must cite the legal entity and identifiers behind a generated answer.
- An agent is allowed to trigger onboarding, payment, risk or data actions.
THE WORKFLOW
Ground an AI answer in company evidence
Resolve the legal entity
Match the input to the most likely source-labelled company record before retrieving additional attributes.
Return structured evidence
Supply identifiers, status, address, source and retrieval time as fields the agent can reason over.
Apply an action policy
Allow, block or route the next agent action according to confidence, missing data and business rules.
WHAT VATFIND DOES
VATFind gives the agent the VAT and EIN verification step.
The agent sends a company name, country and any submitted VAT number or EIN through the VATFind API or MCP tools. VATFind resolves the legal entity, checks whether the identifier is associated with that company record, and returns structured evidence the agent can cite or use in policy.
Evidence meaning: a verified association means the submitted VAT number or EIN is linked to the returned company record. It is not, by itself, confirmation of current tax-registration status, tax compliance or approval of the wider transaction.
For API checks, use vat_number outside the US and tax_identifier for US EINs. Optional fields can be absent when a source does not provide them.
- 01Canonical legal entity
- The registered company the agent should use in its answer and downstream calls.
- 02Company identifiers
- Registration number and available VAT, EIN or tax identifier, labelled by jurisdiction.
- 03Source-labelled attributes
- Address, legal form, incorporation details and source-reported status when available.
- 04Provenance
- Source and retrieval time retained beside the generated claim.
- 05Resolution outcome
- Matched, ambiguous, not-matched or unavailable state for policy and human review.
CHOOSE THE RIGHT APPROACH
Choose how the agent obtains company evidence
Model knowledge
Useful for general language and concepts. It should not be treated as a current company record or a source of invented identifiers.
Web or document retrieval
Useful for discovering contextual evidence. Resolve the entity and keep source dates before applying a document to a company.
VATFind API or MCP
Retrieve structured company-record evidence for the submitted name or tax ID, with a result that your application can inspect and cite.
CONTROL OWNERSHIP
Give each team one clear responsibility.
A reliable workflow names who owns the input, who interprets the company evidence and who makes the wider decision. That separation prevents a data result from becoming an unexplained approval.
AI product owner
Define when a company claim requires a tool call and what actions are permitted.
Data and retrieval engineering
Resolve company mentions to canonical entities and preserve evidence provenance.
Risk and model governance
Set confidence, escalation, logging and review requirements.
DECISION DESIGN
Define the evidence and action at each stage.
| Stage | Evidence to retain | Controlled action |
|---|---|---|
| Detect the claim | Company mention, identifier, jurisdiction context and intended action. | Call a company tool when the answer or action depends on external identity evidence. |
| Ground the entity | Resolved legal company, identifiers, match state, source and retrieval time. | Cite the evidence or state ambiguity instead of completing the name from model memory. |
| Govern the action | Policy rule, tool result, confidence and human-review state. | Proceed only within the agent's authorised scope; escalate consequential uncertainty. |
EXAMPLE WORKFLOW
An assistant is asked for a supplier's VAT number
The prompt contains a brand name and asks the assistant to complete a supplier profile. The agent should first establish the registration country and legal company, then use a VATFind company search. If several candidates appear, ask for the registration number or address. After the correct record is selected, return the available VAT number with the company name and retrieval context. If the tax ID is absent, explain the gap instead of constructing a plausible number.
Connect the company-data toolsIMPLEMENTATION CHECKLIST
AI grounding checklist
- Make entity resolution an explicit tool step rather than hidden prompt context.
- Require jurisdiction or ask the user when country materially changes the result.
- Expose ambiguous, conflicting and unavailable states to the model.
- Log the tool inputs, result ID, evidence time and resulting action.
- Test adversarial prompts that encourage the agent to overstate a partial result.
WHAT TO MEASURE
Prove the control is useful.
Track operating quality rather than claiming that a company match guarantees the downstream outcome.
- Company claims backed by a tool result
- Ambiguity escalated instead of guessed
- Actions traceable to evidence and policy
READ THE RESULT CORRECTLY
Grounding supports an answer; it does not make every conclusion true.
The company record establishes entity identity and available source-backed attributes. The agent must keep unsupported conclusions separate and use additional controls for ownership, sanctions, bank accounts, tax-authority status or transaction risk.
Review data and limitationsFREQUENTLY ASKED QUESTIONS
AI and LLM grounding questions
What does company-data grounding mean?
It means resolving a company mention to a specific legal entity and returning structured facts with source and retrieval context before the model answers or acts.
Does grounding eliminate hallucinations?
No single source can eliminate every model error. Grounding constrains the agent to evidence, exposes ambiguity and gives the workflow a basis for refusing or escalating unsupported claims.
Should an agent receive raw search results?
Usually no. Return a structured match outcome, the selected entity, relevant evidence fields and any exceptions so the agent does not silently choose the first similar name.
Can this be delivered through MCP?
Yes. MCP can expose company-resolution and evidence-retrieval capabilities to compatible agent hosts, subject to agreed authentication, scopes, coverage and usage limits.
How is AI grounding different from fine-tuning a model?
Fine-tuning changes model behaviour using training examples. Grounding supplies evidence for the current task. A company-data tool can provide a record at answer time without relying on the model to remember a company's latest identifiers.
Should an AI agent use REST API access or MCP?
Use REST when your application manages the request and response directly. Use the documented VATFind MCP connection with a compatible agent client. Both approaches still need entity selection, missing-data handling and clear rules for downstream actions.