Run multiple AI models in parallel on the same entity, detect field-level conflicts between their outputs, and fuse the results into a single high-confidence record. This is Entity Enricher's core differentiator: instead of trusting a single LLM, you cross-validate across providers for maximum data accuracy.
Entity Data + Schema
Enriches independently
Enriches independently
Enriches independently
Field-by-field comparison across all model outputs
Rule-Based Merge
Majority vote, median, union
LLM Arbitration
AI resolves with reasoning
Single high-confidence record with audit trail
When you select multiple models for an enrichment job, Entity Enricher sends the same entity data and schema to each model simultaneously. Each model runs independently with no knowledge of other models' outputs, ensuring truly independent data points.
The system supports any combination of providers -- Anthropic Claude, OpenAI GPT-4, Google Gemini, Mistral, or self-hosted models via Ollama. Per-provider rate limiting ensures you stay within each provider's API limits while maximizing throughput.
Real-time SSE streaming shows progress as each model completes, including per-expertise progress when using the multi-expertise strategy. You can see partial results before all models finish.
After all models complete, the conflict detection engine compares their outputs field by field. The comparison is type-aware -- different field types use different comparison rules:
| Field Type | Comparison Method | Agreement Rule |
|---|---|---|
| String / Scalar | Exact match (normalized) | All values must be equal after case/whitespace normalization |
| Number | Exact numeric match | All values must be identical numbers |
| Boolean | Exact match | All models must agree on true/false |
| Multilingual | Per-language comparison | Each language key compared independently |
| Array | Set comparison (order ignored) | Same items regardless of order |
| Object | Recursive per-property | All nested fields must match |
| Null values | null == missing | Null and absent are treated as equivalent |
Deterministic resolution using voting rules. Fast, predictable, and requires no additional LLM call.
An arbitration model reviews each conflict with the entity context and field descriptions, then makes a structured decision.
Every fused record includes arbitration metadata with complete provenance:
This metadata is stored alongside the fused record and exported in the Excel conflict sheet, making it suitable for compliance workflows where decision provenance matters.
Financial due diligence, pharmaceutical safety profiles, and compliance screening where errors have material consequences.
Entities with conflicting information across sources -- funding amounts, founding dates, or regulatory statuses that different sources report differently.
When no single model has complete knowledge. Different LLMs are trained on different data, so running multiple models fills gaps.
When downstream consumers need confidence scores and provenance for each data point, not just the final values.
Select 2+ models, run them in parallel, and see how fusion resolves conflicts. No monthly commitment -- bring your own API keys and pay per token.
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