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Use Case

Turn partial lead records into full contact profiles for sales outreach, CRM enrichment, or lead qualification.

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Endpoint

POST https://api.sixtyfour.ai/enrich-lead

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Important: timeouts and parallelization

This endpoint performs deep research and is a long‑running operation. Typical P95 runtime is about 5 minutes and it can take up to 10 minutes in some cases. We are actively working on making performance improvements on this endpoint.
  • Set client timeouts appropriately: If you call the sync endpoint, configure your HTTP client with a timeout of at least 15 minutes.
  • Prefer async in production: Use POST /enrich-lead-async and poll GET /job-status/{task_id}.
  • Parallelize for throughput: Submit multiple async jobs in parallel (bounded concurrency) rather than waiting for each to complete sequentially.

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Request

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Headers

NameTypeRequiredDescription
x-api-keystringYesYour Sixtyfour API key
Content-TypestringYesMust be application/json

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Body

FieldTypeRequiredDescription
lead_infoobjectYesInitial lead information as key-value pairs
structobjectYesFields to collect about the lead
research_planstringNoOptional research plan to guide enrichment
The struct field defines exactly what data you want back. Each key is a field name you want returned in structured_data, and its value is either a plain-English description (e.g., "The individual's email address") or an object with description and optional type (e.g., {"description": "Person's age", "type": "int"}). The agent uses these descriptions to guide its research.

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Example Request

{
  "lead_info": {
    "name": "Saarth Shah",
    "title": "CEO & Co-Founder @ Sixtyfour AI",
    "company": "Sixtyfour AI",
    "location": "San Francisco",
    "linkedin": "https://www.linkedin.com/in/saarthshah"
  },
  "struct": {
    "name": "The individual's full name",
    "email": "The individual's email address",
    "phone": "The individual's phone number",
    "company": "The company the individual is associated with",
    "title": "The individual's job title",
    "linkedin": "LinkedIn URL for the person",
    "website": "Company website URL",
    "location": "The individual's location and/or company location",
    "industry": "Industry the person operates in",
    "github_url": "url for their github profile",
    "github_notes": "Take detailed notes on their github profile."
  }
}

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Response

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Success Response (200)

Returns enriched lead data with detailed information and sources. 
{
  "notes": "Saarth Shah is the Co-Founder and CEO of Sixtyfour AI, an AI company focused on building AI agents to help GTM (Go-To-Market) teams sell better and faster to SMBs. He is based in San Francisco, California. His LinkedIn profile is https://www.linkedin.com/in/saarthshah, which shows 500+ connections and highlights his education with Y Combinator and UC Berkeley...",
  "structured_data": {
    "name": "Saarth Shah",
    "email": "saarth@sixtyfour.ai",
    "phone": "",
    "company": "Sixtyfour AI",
    "title": "CEO & Co-Founder",
    "linkedin": "https://www.linkedin.com/in/saarthshah",
    "website": "https://www.sixtyfour.ai/",
    "location": "San Francisco, California, USA",
    "industry": "Artificial Intelligence and Sales Technology",
    "github_url": "https://github.com/SaarthShah",
    "github_notes": "Saarth Shah's GitHub profile demonstrates technical expertise with projects related to AI and full-stack development. Notable repositories include search spelling correction, AI chatbots, and data science models, corroborating his data science background."
  },
  "findings": [],
  "references": {
    "https://www.linkedin.com/in/saarthshah": "Saarth Shah LinkedIn profile with current role, location, and background information.",
    "https://www.sixtyfour.ai/": "Sixtyfour AI official company website detailing services and confirming company location and focus area.",
    "https://github.com/SaarthShah": "GitHub profile showing Saarth Shah's repositories and technical projects related to AI and full-stack development.",
    "https://www.saarthshah.com/": "Personal website of Saarth Shah with bio, projects, and contact email saarth@sixtyfour.ai."
  },
  "confidence_score": 9.5
}
The findings field is deprecated. It currently returns an empty list and will be removed in a future update.

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Score Definitions

FieldDescriptionRange
confidence_scoreGlobal Quality Score: Measures the overall quality, consistency, and correctness of the returned data.0-10

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Error Response (400)

{
  "error": "Bad Request",
  "message": "Invalid lead information"
}

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Sync Usage

Make a direct request and wait for the response. This is the simplest approach, but note that enrichment can take several minutes depending on depth of research. 
curl -X POST "https://api.sixtyfour.ai/enrich-lead" \
  -H "x-api-key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "lead_info": {
      "name": "Saarth Shah",
      "title": "CEO & Co-Founder @ Sixtyfour AI",
      "company": "Sixtyfour AI",
      "location": "San Francisco",
      "linkedin": "https://www.linkedin.com/in/saarthshah"
    },
    "struct": {
      "name": "The individual'\''s full name",
      "email": "The individual'\''s email address",
      "phone": "The individual'\''s phone number",
      "company": "The company the individual is associated with",
      "title": "The individual'\''s job title",
      "linkedin": "LinkedIn URL for the person",
      "website": "Company website URL",
      "location": "The individual'\''s location and/or company location",
      "industry": "Industry the person operates in",
      "github_url": "url for their github profile",
      "github_notes": "Take detailed notes on their github profile."
    }
  }'

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Async Usage

For production workflows, use the async endpoint to submit a job and retrieve results later. This avoids long-lived HTTP connections.

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Start Async Job

POST https://api.sixtyfour.ai/enrich-lead-async
The request body is identical to the sync endpoint. Response: 
{
  "task_id": "bdd69815-a1c0-480d-bfa5-d5fbb9745893",
  "status": "pending"
}

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Check Job Status

GET https://api.sixtyfour.ai/job-status/{task_id}
Pending/Processing: 
{
  "status": "pending",
  "processed_items": 0,
  "total_items": 0,
  "task_type": "enrich_lead"
}
Completed: 
{
  "status": "completed",
  "result": {
    "notes": "Saarth Shah is the Co-Founder and CEO of Sixtyfour AI, an AI company focused on building AI agents to help GTM (Go-To-Market) teams sell better and faster to SMBs. He is based in San Francisco, California. His LinkedIn profile is https://www.linkedin.com/in/saarthshah, which shows 500+ connections and highlights his education with Y Combinator and UC Berkeley...",
    "structured_data": {
      "name": "Saarth Shah",
      "email": "saarth@sixtyfour.ai",
      "phone": "",
      "company": "Sixtyfour AI",
      "title": "CEO & Co-Founder",
      "linkedin": "https://www.linkedin.com/in/saarthshah",
      "website": "https://www.sixtyfour.ai/",
      "location": "San Francisco, California, USA",
      "industry": "Artificial Intelligence and Sales Technology",
      "github_url": "https://github.com/SaarthShah",
      "github_notes": "Saarth Shah's GitHub profile demonstrates technical expertise with projects related to AI and full-stack development. Notable repositories include search spelling correction, AI chatbots, and data science models, corroborating his data science background."
    },
    "findings": [],
    "references": {
      "https://www.linkedin.com/in/saarthshah": "Saarth Shah LinkedIn profile with current role, location, and background information.",
      "https://www.sixtyfour.ai/": "Sixtyfour AI official company website detailing services and confirming company location and focus area.",
      "https://github.com/SaarthShah": "GitHub profile showing Saarth Shah's repositories and technical projects related to AI and full-stack development.",
      "https://www.saarthshah.com/": "Personal website of Saarth Shah with bio, projects, and contact email saarth@sixtyfour.ai."
    },
    "confidence_score": 9.5
  },
  "task_type": "enrich_lead"
}

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Example Async Usage

import requests
import time

# Start async job
response = requests.post(
    "https://api.sixtyfour.ai/enrich-lead-async",
    headers={
        "x-api-key": "YOUR_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "lead_info": {
            "name": "Saarth Shah",
            "title": "CEO & Co-Founder @ Sixtyfour AI",
            "company": "Sixtyfour AI",
            "location": "San Francisco",
            "linkedin": "https://www.linkedin.com/in/saarthshah"
        },
        "struct": {
            "name": "The individual's full name",
            "email": "The individual's email address",
            "phone": "The individual's phone number",
            "company": "The company the individual is associated with",
            "title": "The individual's job title",
            "linkedin": "LinkedIn URL for the person",
            "website": "Company website URL",
            "location": "The individual's location and/or company location",
            "industry": "Industry the person operates in"
        }
    }
)

response.raise_for_status()
task_info = response.json()
task_id = task_info["task_id"]

# Poll for results
while True:
    status_response = requests.get(
        f"https://api.sixtyfour.ai/job-status/{task_id}",
        headers={"x-api-key": "YOUR_API_KEY"}
    )
    status_response.raise_for_status()

    status_data = status_response.json()

    if status_data["status"] == "completed":
        results = status_data["result"]
        break
    elif status_data["status"] == "failed":
        print(f"Job failed: {status_data.get('error', 'Unknown error')}")
        break

    time.sleep(10)  # Wait 10 seconds before checking again

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Type Casting

The API automatically handles type casting for structured data output with intelligent type preservation and conversion.

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Type Priority Order

  1. Explicit Type Definitions (Highest Priority) - Specified in struct field definitions
  2. Original Input Types - Types from lead_info when not explicitly overridden
  3. Inferred Types - From example values in struct
  4. Default to String (Lowest Priority)

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Supported Types

TypeExample Values
"str" or "string""John Doe"
"int" or "integer"30, 1000
"float"95.5, 3.14
"bool" or "boolean"true, false
"list"["item1", "item2"]
"list[str]"["item1", "item2"]
"list[int]"[1, 2, 3]
"list[float]"[1.5, 2.7, 3.14]
"dict"{"key": "value"}

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Examples

Type Override: 
{
  "lead_info": {"age": 30, "is_active": true},
  "struct": {
    "age": {"type": "str"},  // Overrides original int type
    "location": "Current location"  // New field as string
  }
}
// Output: {"age": "30", "is_active": true, "location": "New York"}
Explicit Type Definition: 
{
  "struct": {
    "age": {"description": "Person's age", "type": "int"},
    "is_verified": {"description": "Status", "type": "bool"}
  }
}
// Output: {"age": 30, "is_verified": true}
Type Inference from Values: 
{
  "struct": {
    "score": 95.5,    // Inferred as float
    "active": false,  // Inferred as bool
    "count": 42       // Inferred as int
  }
}