Context API

Overview

The Context API is our most powerful endpoint. Send us unstructured data about a person, and we’ll:

Enrich the profile with data from our websets
Structure the data into our standard schema
Generate custom fields using LLMs based on your requirements

Coming Soon: The Context API is currently in development. Documentation is available now, but the endpoint is not yet live.

Endpoint

POST /api/v1/context

Request Parameters

person

string | object

required

Unstructured data about a person. Can be a string or an object with any fields.Examples:

String: "Justin Connell works at PeopleContext — his email is [email protected]"
Object: {"name": "Justin", "company": "PeopleContext", "linkedin": "https://linkedin.com/in/justinconnell"}

schema

object

Custom fields you want generated for this profile. Each field should have a type and description.Example:

{
  "fit_score": {
    "type": "number",
    "description": "How well this person matches the job: Senior Python Engineer, SF-based"
  },
  "email_copy": {
    "type": "string",
    "description": "A personalized email inviting them to apply"
  }
}

confidence

number

default:"0.9"

Minimum confidence threshold for returned data (0.0 - 1.0). Higher values return more accurate data but may exclude some information.

webhook_url

string

Optional webhook URL to receive the enriched profile asynchronously.

response_timeout_sec

number

default:"30"

Maximum time to wait for enrichment before sending to webhook.

Response Schema

The response includes:

All standard profile fields (from our unified schema)
A custom object with your requested custom fields
Confidence scores for generated fields

{
  "first_name": "Justin",
  "last_name": "Connell",
  "full_name": "Justin Connell",
  "personal_email": "[email protected]",
  "github": {
    "url": "https://github.com/justinconnell",
    "username": "justinconnell",
    "id": 123456
  },
  "linkedin": {
    "url": "https://www.linkedin.com/in/justinconnell",
    "username": "justinconnell",
    "headline": "Founder at PeopleContext"
  },
  "current_position": {
    "summary": "Founder & CEO",
    "start": "2025-02-01",
    "company": {
      "name": "PeopleContext",
      "linkedin_url": "https://www.linkedin.com/company/peoplecontextai"
    }
  },
  "custom": {
    "fit_score": 0.85,
    "email_copy": "Hi Justin,\n\nI came across your profile and was impressed by your work at PeopleContext..."
  }
}

Code Examples

Basic Enrichment

import requests

API_KEY = "your_api_key_here"
BASE_URL = "https://api.peoplecontext.com"

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

# Simple string input
payload = {
    "person": "Justin Connell works at PeopleContext — his email is [email protected]"
}

response = requests.post(
    f"{BASE_URL}/api/v1/context",
    headers=headers,
    json=payload
)

profile = response.json()
print(f"Name: {profile['full_name']}")
print(f"Email: {profile['personal_email']}")

With Custom Schema

# Enrich with custom fields
payload = {
    "person": {
        "name": "Justin Connell",
        "company": "PeopleContext",
        "linkedin": "https://linkedin.com/in/justinconnell"
    },
    "schema": {
        "fit_score": {
            "type": "number",
            "description": "How well this person matches: Senior Python Engineer, 5+ years, SF Bay Area. Score 0.0-1.0"
        },
        "email_copy": {
            "type": "string",
            "description": "A personalized 2-3 sentence email inviting them to apply for the Senior Python Engineer role"
        },
        "key_strengths": {
            "type": "array",
            "description": "List of 3-5 key technical strengths based on their experience"
        }
    },
    "confidence": 0.9
}

response = requests.post(
    f"{BASE_URL}/api/v1/context",
    headers=headers,
    json=payload
)

profile = response.json()

# Access custom fields
print(f"Fit Score: {profile['custom']['fit_score']}")
print(f"Email:\n{profile['custom']['email_copy']}")
print(f"Strengths: {', '.join(profile['custom']['key_strengths'])}")

Async with Webhooks

Python

# For long-running enrichment, use webhooks
payload = {
    "person": "Justin Connell, CEO at PeopleContext",
    "schema": {
        "fit_score": {
            "type": "number",
            "description": "Fit for executive role"
        }
    },
    "webhook_url": "https://yourapp.com/webhooks/context",
    "response_timeout_sec": 10
}

# Request returns immediately if processing takes longer than 10 seconds
response = requests.post(
    f"{BASE_URL}/api/v1/context",
    headers=headers,
    json=payload
)

# Result will be POSTed to your webhook URL when ready

Use Cases

Recruiting - Candidate Scoring

Score candidates automatically based on job requirements:

job_description = """
Senior Python Engineer
Requirements:
- 5+ years Python experience
- Experience with FastAPI, Django, or Flask
- Cloud deployment (AWS/GCP)
- SF Bay Area based or remote
"""

def score_candidate(person_data):
    payload = {
        "person": person_data,
        "schema": {
            "fit_score": {
                "type": "number",
                "description": f"How well this person matches this role (0.0-1.0):\n{job_description}"
            },
            "match_reasons": {
                "type": "array",
                "description": "3-5 specific reasons why they match or don't match"
            },
            "red_flags": {
                "type": "array",
                "description": "Any concerns or missing qualifications"
            }
        }
    }

    response = requests.post(
        f"{BASE_URL}/api/v1/context",
        headers=headers,
        json=payload
    )

    return response.json()

# Use it
candidate = score_candidate("Jane Smith, Senior Backend Engineer at Stripe")
print(f"Fit: {candidate['custom']['fit_score']}")
print(f"Reasons: {candidate['custom']['match_reasons']}")

Sales - Lead Qualification

Qualify and score leads automatically:

def qualify_lead(person_info, company_criteria):
    payload = {
        "person": person_info,
        "schema": {
            "is_decision_maker": {
                "type": "boolean",
                "description": "Is this person a decision-maker for enterprise software purchases?"
            },
            "company_size_match": {
                "type": "boolean",
                "description": f"Does their company match our ICP: {company_criteria}"
            },
            "outreach_angle": {
                "type": "string",
                "description": "Best angle for outreach based on their background and current role"
            },
            "priority_score": {
                "type": "number",
                "description": "Priority score 1-10 based on fit and likelihood to convert"
            }
        }
    }

    response = requests.post(
        f"{BASE_URL}/api/v1/context",
        headers=headers,
        json=payload
    )

    return response.json()

Content Personalization

Generate personalized content at scale:

def generate_personalized_email(recipient_info, campaign_goal):
    payload = {
        "person": recipient_info,
        "schema": {
            "subject_line": {
                "type": "string",
                "description": "Personalized email subject line that references their background"
            },
            "email_body": {
                "type": "string",
                "description": f"3-paragraph personalized email with goal: {campaign_goal}"
            },
            "call_to_action": {
                "type": "string",
                "description": "Specific CTA based on their seniority and role"
            }
        }
    }

    response = requests.post(
        f"{BASE_URL}/api/v1/context",
        headers=headers,
        json=payload
    )

    return response.json()

# Use it
email = generate_personalized_email(
    "Sarah Chen, VP Engineering at Airbnb",
    "Invite to our AI Engineering Summit"
)

print(f"Subject: {email['custom']['subject_line']}")
print(f"Body:\n{email['custom']['email_body']}")

Best Practices

Write Clear Schema Descriptions

The quality of custom fields depends on clear, detailed descriptions:Good:

{
  "fit_score": {
    "type": "number",
    "description": "How well this person matches: Senior Python Engineer, 5+ years experience, SF-based, startup experience preferred. Score 0.0-1.0 where 1.0 is perfect match."
  }
}

Bad:

{
  "fit_score": {
    "type": "number",
    "description": "Score the person"
  }
}

Adjust Confidence Levels

Use different confidence levels based on your use case:

High confidence (0.95+): Critical decisions, compliance
Medium confidence (0.85-0.95): General enrichment, CRM data
Lower confidence (0.75-0.85): Exploratory analysis, lead generation

Use Webhooks for Batch Processing

For processing large volumes, use webhooks to avoid timeouts:

# Process 1000 leads
for lead in leads:
    requests.post(
        f"{BASE_URL}/api/v1/context",
        headers=headers,
        json={
            "person": lead,
            "schema": scoring_schema,
            "webhook_url": "https://yourapp.com/webhooks/lead-scored",
            "response_timeout_sec": 5
        }
    )

# Results arrive at your webhook as they complete

Pricing

Context API pricing is based on:

Base enrichment: Standard per-request fee
Custom field generation: Additional cost per custom field
Data sources accessed: May incur additional costs for premium data

Contact sales for detailed pricing.

FAQ

When will the Context API be available?

The Context API is currently in development. Contact [email protected] to express interest and be notified when it launches.

What LLM powers custom field generation?

We use state-of-the-art LLMs (GPT-4, Claude, etc.) optimized for accuracy and cost-efficiency.

Can I use my own LLM for custom fields?

Enterprise customers can integrate their own LLM endpoints. Contact sales for details.

How accurate are generated custom fields?

Accuracy depends on:

Input data quality
Schema description clarity
Confidence threshold
Complexity of the field

We recommend testing with your specific use case and adjusting the confidence threshold accordingly.

Get Started

Guides

Legal

Overview

Endpoint

Request Parameters

Response Schema

Code Examples

Basic Enrichment

With Custom Schema

Async with Webhooks

Use Cases

Recruiting - Candidate Scoring

Sales - Lead Qualification

Content Personalization

Best Practices

Pricing

FAQ

Get Started

Guides

Legal

​Overview

​Endpoint

​Request Parameters

​Response Schema

​Code Examples

​Basic Enrichment

​With Custom Schema

​Async with Webhooks

​Use Cases

​Recruiting - Candidate Scoring

​Sales - Lead Qualification

​Content Personalization

​Best Practices

​Pricing

​FAQ

Overview

Endpoint

Request Parameters

Response Schema

Code Examples

Basic Enrichment

With Custom Schema

Async with Webhooks

Use Cases

Recruiting - Candidate Scoring

Sales - Lead Qualification

Content Personalization

Best Practices

Pricing

FAQ