Go beyond basic company lookups. Entity Enricher lets you define custom financial schemas covering funding rounds, market cap, risk indicators, subsidiary structures, and any other financial data point -- enriched by multiple AI models with conflict resolution for maximum accuracy.
Financial data is high-stakes. Incorrect funding round amounts, outdated market cap figures, or missed risk signals can lead to costly investment decisions. Traditional enrichment tools offer fixed B2B fields -- company size, revenue range, industry -- but miss the depth that financial analysis requires.
Entity Enricher addresses this with multi-model fusion. When Claude and GPT-4 disagree on a company's debt-to-equity ratio, the fusion engine detects the conflict and resolves it -- using either rule-based voting (median for numbers, majority for strings) or LLM arbitration with structured reasoning. Every decision includes a confidence score and audit trail.
This schema captures the data points most relevant to financial analysis. Create it in seconds with AI schema generation -- just paste a sample JSON of your financial data.
{
"name": "FinancialEntity",
"properties": {
"company_name": { "type": "string", "is_key": true },
"ticker_symbol": { "type": "string" },
"isin": { "type": "string" },
"market_cap_usd": { "type": "number" },
"sector": { "type": "string" },
"headquarters_country": { "type": "string" },
"funding_rounds": {
"type": "array",
"items": {
"type": "object",
"properties": {
"round_type": { "type": "string" },
"amount_usd": { "type": "number" },
"date": { "type": "string" },
"lead_investor": { "type": "string" }
}
}
},
"risk_indicators": {
"type": "object",
"properties": {
"credit_rating": { "type": "string" },
"debt_to_equity": { "type": "number" },
"bankruptcy_risk": { "type": "string" },
"sanctions_exposure": { "type": "boolean" }
}
},
"subsidiaries": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": { "type": "string" },
"jurisdiction": { "type": "string" },
"ownership_percentage": { "type": "number" }
}
}
}
}
}The multi-expertise strategy splits your schema into specialized domains. Financial metrics, risk assessment, and corporate structure are each handled by dedicated LLM prompts for deeper results.
| Field | Expertise | Description |
|---|---|---|
| company_name | General | Legal entity name and common trading names |
| ticker_symbol | General | Stock exchange ticker and listing venue |
| market_cap_usd | Financial | Current market capitalization in USD |
| funding_rounds | Financial | Venture capital and private equity funding history |
| risk_indicators | Risk | Credit ratings, leverage ratios, sanctions flags |
| subsidiaries | Corporate | Subsidiary entities with jurisdiction and ownership |
| sector | General | Industry classification (GICS, NAICS, or SIC) |
| isin | Financial | International Securities Identification Number |
Define the exact fields your analysis needs -- from ISIN codes and credit ratings to subsidiary ownership chains. AI generates the schema from sample data.
Provide company names, ticker symbols, or LEI codes. Batch process entire portfolios with up to 100 entities in parallel.
Run 2+ LLMs simultaneously. Each model enriches independently, giving you multiple data points per field for comparison.
The fusion engine merges results, resolving conflicts with rule-based logic or LLM arbitration. Export to JSON or Excel with full conflict reports.
Enrich target companies with funding history, risk indicators, and subsidiary structures for M&A analysis.
Batch enrich your entire investment portfolio with current market data, credit ratings, and sector classifications.
Enrich legal entities with sanctions exposure, beneficial ownership, and jurisdiction data for compliance checks.
Track competitor funding rounds, market cap changes, and strategic acquisitions across your sector.
Define your financial schema, run multiple models in parallel, and get cross-validated financial intelligence with full audit trails. Pay only for the tokens you use.
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