Technical guides and field notes

Enrich API Guides for Developers and AI Teams

Practical architecture, evaluation and implementation guides for structured AI data enrichment, model routing and agent context.

ArchitectureEvaluationAI agentsCRM
Enrich API blog about AI data enrichment, model routing, agent context and structured JSON
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Build enrichment systems that are structured and measurable

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

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Explore the Enrich API information architecture

The site groups content around the jobs a developer must complete: define the API, choose enrichments, route models, integrate systems and validate output.

API architecture

Requests, authentication, jobs, errors, idempotency and trace metadata.

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Data enrichment

Company, person, website, document, product, CRM and support workflows.

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Model routing

Task policies, provider adapters, fallbacks and route evaluation.

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Integrations

CRM, database, webhook, automation and developer-tool patterns.

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Developer experience

Typed clients, fixtures, testing, security and observability.

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Structured JSON

Schemas, null policy, validation, repair, retries and versioning.

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