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Unified AI data enrichment
Enrich API for AI Apps, Agents and LLM Workflows
EnrichAPI.com is an Ai Enrich LLM API that turns raw, incomplete and unstructured inputs into clean, categorized and application-ready JSON.
Entity resolutionSchema validationModel routingAgent-ready data
POST /v1/enrich · illustrative
const result = await enrich.enrich({
input: "Acme Robotics, San Francisco",
enrichments: ["profile", "industry", "location", "tags"],
output: "json",
schema: "agent_context_v1"
});
console.log(result.data);Schema-first outputsDefine fields before a model runs.
Provider-agnostic routingMatch each enrichment task to a suitable model route.
Agent-ready contextNormalize tool results before planning and action.
How it works
A controlled path from raw input to validated context
The Enrich API separates extraction, model selection and validation so each stage can be observed, tested and improved.
Select enrichments
Choose the attributes, classifications and summaries you need.
Route the task
Apply provider preferences, fallbacks and task-level policies.
Validate output
Check types, required fields, enums and confidence thresholds.
Return JSON
Send normalized context to an app, agent, CRM or database.
Interactive preview
See the Enrich API transform a record
Choose a sample input. The browser-only demo shows how a schema-first enrichment response can look. It does not call a live backend.
1. Raw inputUnvalidated
"Acme Robotics, San Francisco, builds warehouse autonomy software and fleet orchestration tools."
Entity detectedRoute selected
2. Structured outputJSON
{
"status": "completed",
"confidence": 0.984,
"entity": {
"name": "Acme Robotics",
"industry": "industrial automation",
"headquarters": "San Francisco, CA",
"summary": "Warehouse autonomy and fleet orchestration software.",
"tags": ["robotics", "warehouse-ai", "fleet-ops"],
"agent_ready": true
}
}Schema validAgent readyIllustrative
AI platform ecosystem
Connect enrichment workflows to leading AI platforms
Use one schema-driven orchestration layer while keeping provider selection, fallbacks and validation explicit.
OpenAI workflows
Use OpenAI-compatible model routes for extraction, classification, summarization and structured output.
View workflow patternGrok by xAI
Route selected research, context and classification tasks to Grok-compatible endpoints.
View workflow patternClaude by Anthropic
Apply long-context document analysis, careful extraction and structured summaries.
View workflow patternOpenRouter
Evaluate and route enrichment jobs across a broader model catalog with explicit fallbacks.
View workflow patternMicrosoft Azure AI
Design enterprise-oriented workflows around Azure-hosted AI services and application controls.
View workflow patternCursor IDE
Use copy-ready examples, typed payloads and debugging patterns inside modern developer environments.
View developer integrationThird-party names and trademarks belong to their respective owners. EnrichAPI.com is independent and does not claim endorsement or formal affiliation.
Enrichment capabilities
One Enrich API for many data workflows
Combine focused enrichment steps into reusable pipelines for applications, agents, research systems and operations teams.
Company enrichment
Normalize names, industries, domains, locations, descriptions and semantic tags.
Learn morePerson enrichment
Structure professional roles, organizations, expertise and record metadata.
Learn moreWebsite enrichment
Extract site purpose, topics, entities, language and content categories.
Learn moreDocument enrichment
Classify documents, extract key entities and create schema-bound summaries.
Learn moreCRM enrichment
Complete incomplete rows and prepare consistent data for sales and operations.
Learn moreAgent context
Normalize research, memory and tool output before an agent makes decisions.
Learn moreSupport intelligence
Detect intent, urgency, sentiment, topic and routing suggestions in tickets.
Learn moreStructured JSON
Use required fields, enums, types and validation results instead of free-form prose.
Learn moreDeveloper-first interface
Start with a clear request and receive predictable JSON
The examples are static interface specifications. Connect them to your production API, authentication and billing systems before launch.
import { EnrichAPI } from "@enrichapi/sdk";
const client = new EnrichAPI({
apiKey: process.env.ENRICH_API_KEY
});
const result = await client.enrich({
input: {
company: "Example Robotics",
website: "example.com"
},
enrichments: [
"company_summary",
"industry",
"keywords",
"location"
],
output_format: "json"
});
console.log(result.data);from enrichapi import EnrichAPI
import os
client = EnrichAPI(api_key=os.environ["ENRICH_API_KEY"])
result = client.enrich(
input={"company": "Example Robotics", "website": "example.com"},
enrichments=["company_summary", "industry", "keywords", "location"],
output_format="json",
)
print(result.data)curl --request POST \
--url https://api.enrichapi.com/v1/enrich \
--header "Authorization: Bearer $ENRICH_API_KEY" \
--header "Content-Type: application/json" \
--data '{
"input": {"company": "Example Robotics", "website": "example.com"},
"enrichments": ["company_summary", "industry", "keywords", "location"],
"output_format": "json"
}' Use cases
Enrich data before it reaches a critical workflow
A clear enrichment boundary reduces one-off prompt logic and gives teams a consistent place to validate AI-generated fields.
AI applications
Enrich user input and application records before sending context to a language model.
Learn moreSales and CRM
Complete, categorize and summarize company or contact records with consistent schemas.
Learn moreAI agents
Prepare normalized tool output, research context and memory records for agent workflows.
Learn moreResearch systems
Turn scattered findings into organized entities, summaries, labels and relationship maps.
Learn moreCustomer support
Classify tickets, identify intent and prepare routing context for support operations.
Learn moreData operations
Normalize incomplete datasets and create reusable structured fields for downstream systems.
Learn moreAbout EnrichAPI.com
A focused interface for AI data enrichment
EnrichAPI.com is positioned as a developer-first layer between raw application data and the models or systems that can add context.
The product concept is simple: send a record, define the enrichments you need, choose a routing policy and receive a schema-validated object. That separation makes enrichment easier to test than scattered prompts embedded throughout an application.
The static site does not invent customer logos, performance numbers or certifications. Instead, it explains the architecture, gives developers useful examples and creates an honest path from product discovery to documentation.
Explore the developer workflow or read the complete Enrich API guide.
Launch-ready static foundation
All content, CSS, JavaScript, images, metadata, sitemaps and feeds are static files. Add a real API backend, authentication provider and billing workflow when the service is ready.
Latest guides
Practical Enrich API architecture and implementation
Go deeper on structured output, agent context, model routing and CRM enrichment.
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 guide
Build Better AI Agent Context with an Enrichment API
Build better AI agent context with an enrichment API that normalizes tool output, research, memory and entity data before planning and action.
Read guide
Multi-Model AI Routing for OpenAI, Claude, Grok and More
Design multi-model AI routing for OpenAI, Claude, Grok, OpenRouter and Azure AI with task policies, fallbacks, schemas and evaluation.
Read guideEnrich API FAQ
Answers about AI enrichment, models and structured output
Understand the product concept, workflow and current static implementation status.
What is an Enrich API?
An Enrich API accepts a partial or unstructured input, adds useful context, and returns a normalized result. EnrichAPI.com is designed to produce structured JSON that applications, databases and AI agents can use directly.
What does EnrichAPI.com enrich?
The workflow patterns cover company records, people, websites, documents, products, CRM rows, support tickets, research notes and other text-based application data.
What is an Ai Enrich LLM API?
An Ai Enrich LLM API uses language models as part of a controlled pipeline. The pipeline detects entities, chooses a model route, applies a schema, validates the response and returns application-ready data.
Can the Enrich API return structured JSON?
Yes. The examples on this static site use explicit JSON schemas, required fields, enums and confidence metadata so downstream systems receive predictable objects instead of free-form prose.
Can I use more than one AI model provider?
The architecture is provider-agnostic. A routing policy can select a primary provider, a fallback provider and task-specific rules for latency, cost, context length and output quality.
Does EnrichAPI.com work with OpenAI, Claude or Grok?
The site documents compatible workflow patterns for OpenAI, Claude by Anthropic, Grok by xAI, OpenRouter and Microsoft Azure AI. Product names belong to their respective owners, and no affiliation is implied.
Can AI agents use the Enrich API?
Yes. Agent workflows can use enrichment to normalize tool output, prepare research context, attach entity metadata and validate memory records before planning or action.
Is this static website itself a live API service?
No. The website package is a fully static product and documentation site. Code samples describe the intended interface and must be connected to a real API backend, authentication system and billing service before production launch.
Build more intelligent data workflows with EnrichAPI.com
Use an Enrich API architecture designed for structured data, AI agents, LLM workflows and modern developer tools.