
What Is an Enrich API? A Developer’s Guide
Learn what an Enrich API is, how data enrichment pipelines work, which records benefit, and how to design structured JSON outputs for applications.
Read 6 minute guidePractical architecture, evaluation and implementation guides for structured AI data enrichment, model routing and agent context.

Each guide focuses on an implementation decision that affects quality, reliability or operating cost.

Learn what an Enrich API is, how data enrichment pipelines work, which records benefit, and how to design structured JSON outputs for applications.
Read 6 minute guide
Build better AI agent context with an enrichment API that normalizes tool output, research, memory and entity data before planning and action.
Read 6 minute guide
Design multi-model AI routing for OpenAI, Claude, Grok, OpenRouter and Azure AI with task policies, fallbacks, schemas and evaluation.
Read 5 minute guide
Use structured JSON and schema validation with LLM APIs to control fields, types, nulls, retries and safe application write-back.
Read 5 minute guide
Use a company data enrichment API for CRM records with field ownership, refresh rules, schema validation, provenance and privacy controls.
Read 5 minute guideThe site groups content around the jobs a developer must complete: define the API, choose enrichments, route models, integrate systems and validate output.
Requests, authentication, jobs, errors, idempotency and trace metadata.
Learn moreCompany, person, website, document, product, CRM and support workflows.
Learn moreTask policies, provider adapters, fallbacks and route evaluation.
Learn moreCRM, database, webhook, automation and developer-tool patterns.
Learn moreTyped clients, fixtures, testing, security and observability.
Learn moreSchemas, null policy, validation, repair, retries and versioning.
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