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Documentation Index

Fetch the complete documentation index at: https://docs.sixtyfour.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Use case

Research and enrich company data with additional firmographic information and find associated people. Use it for sales outreach, CRM enrichment, lead qualification, or investigative diligence.

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Endpoint

POST https://api.sixtyfour.ai/company-intelligence

API Reference

See the full request/response schema and parameters in the API Reference.

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Pricing

See Credits & Pricing Guide for credit costs by tier.

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Errors

For error responses (400, 403, 422, etc.), see Handling Errors.

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Tiers

The tier parameter controls research depth and cost.
TierDescriptionAccess
low (default)Baseline tier — fast and cheap. Good for high-volume firmographic enrichment.All orgs
mediumDeeper research — more fields filled, more sources.All orgs
highOSINT-grade investigation — deeply recursive crawling across the open web, dark web, directories, proprietary sources. Designed for high-stakes accounts, sensitive diligence, and investigative workflows.Exclusive — access is granted case-by-case by our team. Contact sales to request.
If tier is omitted, low is used. Requests with tier: "high" on an org without high-tier access return 403 — see Handling Errors.

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Using the struct field

The struct field defines exactly what data you want back. Each key becomes a field in structured_data, and its value tells the agent what to find. You can pass either a plain-English description or an object with description and type: 
{
  "struct": {
    "instagram_url": "Instagram url for the photography company",
    "num_employees": {"description": "Approximate employee count", "type": "int"}
  }
}
The agent uses these descriptions to guide its research. Be specific — "company's primary Instagram handle" returns better results than "social". For supported types, type resolution priority, and casting examples, see Struct & Type Casting.

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find_people vs full_org_chart

  • find_people — Finds specific people associated with the company. Pair with people_focus_prompt to filter by role or seniority.
  • full_org_chart — Returns a compact view of employees grouped by department (capped per department). It’s broad coverage of who works where, not a literal reporting tree or an exhaustive employee dump. When true, the response includes a top-level org_chart field.
Use find_people for targeted lead discovery and full_org_chart for department-level visibility.

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research_plan vs people_focus_prompt

  • research_plan — Methodology. Tells the agent where to look (e.g., "Check the 'About Us' page and LinkedIn Company People tab").
  • people_focus_prompt — Criteria. Tells the agent who to find (e.g., "Find the VP of Marketing and the CTO").

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Understanding scores

FieldMeaningRange
confidence_scoreGlobal quality score — overall quality, consistency, and correctness of the returned data.0–10
scoreRelevance score — likelihood that a returned person matches the prompt or is helpful. Returned per lead.0–10
The findings field on the response is deprecated. It currently returns an empty list and will be removed in a future update.

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Async pattern

For production workflows, use /company-intelligence-async to submit a job and poll for results. This avoids long-lived HTTP connections during deep research runs. The flow is:
  1. Submit — POST /company-intelligence-async with the same body as the sync endpoint. Response includes a task_id.
  2. Poll — GET /job-status/{task_id} until status is completed, failed, or cancelled.
  3. Read result — When completed, the full enrichment is in the result field.
The async start endpoint returns uppercase RUNNING. Subsequent /job-status/{task_id} calls return lowercase statuses. charge_amount is returned in cents, not credits.

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Polling example

import requests
import time

response = requests.post(
    "https://api.sixtyfour.ai/company-intelligence-async",
    headers={"x-api-key": "YOUR_API_KEY", "Content-Type": "application/json"},
    json={
        "target_company": {
            "company_name": "Pacific View Studios",
            "website": "https://pacificview.studio"
        },
        "struct": {
            "instagram_url": "Instagram url for the photography company",
            "num_employees": "How many employees work there"
        },
        "find_people": True
    }
)
response.raise_for_status()
task_id = response.json()["task_id"]

while True:
    status = requests.get(
        f"https://api.sixtyfour.ai/job-status/{task_id}",
        headers={"x-api-key": "YOUR_API_KEY"}
    ).json()

    if status["status"] == "completed":
        results = status["result"]
        break
    if status["status"] in ("failed", "cancelled"):
        raise RuntimeError(f"Job {status['status']}: {status.get('error', 'Unknown error')}")

    time.sleep(10)