A step-by-step walkthrough of how Entity Enricher processes a single entity — from input through classification, parallel model execution, to structured output.
Open the Schema Editor page and set up your enrichment. You will interact with four key areas:
Paste sample JSON in the “New Schema” tab to generate a schema, or switch to the “Enrich” tab to fill in entity search keys (name, website, country, etc.).
Interactive property tree showing your schema structure. You can edit properties, add expertise domains, and mark fields as search keys or preserved.
Select your enrichment strategy (single-pass or multi-expertise), pick one or more LLM models, choose output languages, and optionally enable pre-flight classification.
Shows real-time progress and results for each model. When using multiple models, a “Merge Results” button appears for fusion.
If you selected a classification model, a fast, inexpensive LLM call runs first to verify the entity matches the schema type. This prevents wasting tokens on enrichment when the entity does not match. Read more in the Classification documentation.
Each selected model processes the entity using your chosen strategy. When multiple models are selected, they run in parallel across providers (Claude and GPT-4 run simultaneously) while models from the same provider run sequentially to respect rate limits.
Each LLM response is validated against your schema in real-time. When the output does not match the expected types or constraints, the system automatically sends errors back to the LLM for correction.
Up to 5 automatic retry attempts per LLM call. Each retry includes the specific validation error so the LLM knows exactly what to fix.
Entity Enricher uses Server-Sent Events (SSE) to stream progress in real-time. You do not have to wait for all models to complete — results appear progressively as each expertise domain or model finishes.
Each model gets its own result panel showing the structured JSON output, per-expertise progress badges, token usage, cost, and processing time. When using the multi-expertise strategy, expertise badges update in real-time as each domain completes.
When using the multi-expertise strategy, some expertises may fail while others succeed. Rather than discarding everything, Entity Enricher returns the merged output from successful expertises with a “Partial” status. You can then retry only the failed expertises without re-running the entire enrichment.
After enrichment completes, your results are saved to the Records page for future reference. If you used multiple models, you can merge the results using Multi-Model Fusion.