Use cases

Sales prospecting: build an ideal customer profile (ICP) list

Build a target account list for outbound sales — private, US/UK-based SaaS companies in the growth stage with an API, enriched with firmographic and revenue data so reps can prioritize outreach. Use the following filters:

firmographic.company_type — restrict to private companies (public companies aren’t sales targets).
location.hq.country — scope to USA and GBR.
business_model.offering_type — restrict to software.
financial_estimate.revenue_estimate — mid-market revenue bands (10m_25m, 25m_50m, 50m_100m).
technology.api_detail.has_api — true, since the product is API-first.
sections — firmographic, location, financial_estimate for context reps need before a call.

curl --location 'https://api.akta.pro/v1/company/list/generate' \
  --header 'x-api-key: <YOUR_API_KEY>' \
  --header 'Content-Type: application/json' \
  --data '{
    "filters": {
      "firmographic.company_type": ["private"],
      "location.hq.country": ["USA", "GBR"],
      "business_model.offering_type": ["software"],
      "financial_estimate.revenue_estimate": ["10m_25m", "25m_50m", "50m_100m"],
      "technology.api_detail.has_api": true
    },
    "sections": ["firmographic", "location", "financial_estimate"],
    "sort_by": "revenue_estimate",
    "sort_order": "desc",
    "limit": 50
  }'

import requests

url = "https://api.akta.pro/v1/company/list/generate"

payload = {
    "filters": {
        "firmographic.company_type": ["private"],
        "location.hq.country": ["USA", "GBR"],
        "business_model.offering_type": ["software"],
        "financial_estimate.revenue_estimate": ["10m_25m", "25m_50m", "50m_100m"],
        "technology.api_detail.has_api": True
    },
    "sections": ["firmographic", "location", "financial_estimate"],
    "sort_by": "revenue_estimate",
    "sort_order": "desc",
    "limit": 50
}
headers = {"x-api-key": "YOUR_API_KEY", "Content-Type": "application/json"}

response = requests.post(url, headers=headers, json=payload)
data = response.json()

for company in data["data"]:
    print(company["name"], company["website"], company.get("financial_estimate"))

Sample response:

{
  "data": [
    {
      "uuid": "0000a1b",
      "name": "ExampleCo",
      "website": "https://exampleco.com",
      "firmographic": {
        "company_type": "private",
        "founded_year": 2017,
        "headcount_range": "51-200"
      },
      "location": {
        "hq": {"country": "USA", "region": "North America"}
      },
      "financial_estimate": {
        "revenue_estimate": "25m_50m"
      }
    }
  ],
  "count": 1,
  "total_count": 34,
  "credits_consumed": 6.0
}

Market mapping: size a market segment

Enumerate every company in a market segment — for example, API-first fintech companies globally — to understand market size, identify competitors, and spot white space. Use the following filters:

industry.industry — use the Industry Search API to resolve “fintech” or a sub-segment to Akta industry codes first.
business_model.gtm_motion — restrict to api_first to capture companies building API-native products.
location.market_served.is_global — optionally restrict to companies serving a global market, if you’re mapping the global segment rather than a single geography.
Omit sections for a fast, low-cost count-only pass first (total_count tells you market size), then re-run with sections for the companies you want to study in detail.

curl --location 'https://api.akta.pro/v1/company/list/generate' \
  --header 'x-api-key: <YOUR_API_KEY>' \
  --header 'Content-Type: application/json' \
  --data '{
    "filters": {
      "industry.industry": ["FINTECH_CODE"],
      "business_model.gtm_motion": ["api_first"]
    },
    "limit": 1
  }'

import requests

# Step 1: resolve "fintech" to an industry code
industry_code = requests.get(
    "https://api.akta.pro/api/v1/industry/search",
    headers={"x-api-key": "YOUR_API_KEY"},
    params={"query": "fintech"}
).json()["data"][0]["code"]

# Step 2: get total market size with a minimal-cost call (no sections)
size_check = requests.post(
    "https://api.akta.pro/v1/company/list/generate",
    headers={"x-api-key": "YOUR_API_KEY", "Content-Type": "application/json"},
    json={
        "filters": {
            "industry.industry": [industry_code],
            "business_model.gtm_motion": ["api_first"]
        },
        "limit": 1
    }
).json()

print(f"Market size: {size_check['total_count']} companies")

# Step 3: pull the full enriched list once you know the size is manageable
full_list = requests.post(
    "https://api.akta.pro/v1/company/list/generate",
    headers={"x-api-key": "YOUR_API_KEY", "Content-Type": "application/json"},
    json={
        "filters": {
            "industry.industry": [industry_code],
            "business_model.gtm_motion": ["api_first"]
        },
        "sections": ["firmographic", "location", "financial_estimate"],
        "limit": 500
    }
).json()

Requesting limit: 1 with no sections is a near-zero-cost way to check total_count before committing to a full enrichment pull — useful when you’re not yet sure how large a segment is.

Investor sourcing: find companies matching a deal thesis

Surface companies that match an investment thesis — for example, Series A or Series B companies that raised in the last 6 months with a lead investor from a specific set of firms. Use the following filters:

funding_detail.funding_rounds.round — series_a, series_b.
funding_detail.funding_rounds.date — a recent date range using gte.
funding_detail.funding_rounds.investors.lead_investor — true, to only match rounds where a lead investor is identified.
funding_detail.funding_rounds.investors.uuid — optionally scope to specific investor UUIDs if you’re tracking co-investment patterns with particular firms.
sections — funding_detail, firmographic, financial_estimate for deal context.

Funding Detail filters and the funding_detail enrichment section are available on Enterprise plans only.

curl --location 'https://api.akta.pro/v1/company/list/generate' \
  --header 'x-api-key: <YOUR_API_KEY>' \
  --header 'Content-Type: application/json' \
  --data '{
    "filters": {
      "funding_detail.funding_rounds": {
        "round": ["series_a", "series_b"],
        "date": {"gte": "2026-01-01"},
        "investors.lead_investor": true
      }
    },
    "sections": ["funding_detail", "firmographic", "financial_estimate"],
    "sort_by": "founded_year",
    "sort_order": "desc",
    "limit": 100
  }'

import requests

payload = {
    "filters": {
        "funding_detail.funding_rounds": {
            "round": ["series_a", "series_b"],
            "date": {"gte": "2026-01-01"},
            "investors.lead_investor": True
        }
    },
    "sections": ["funding_detail", "firmographic", "financial_estimate"],
    "sort_by": "founded_year",
    "sort_order": "desc",
    "limit": 100
}

response = requests.post(
    "https://api.akta.pro/v1/company/list/generate",
    headers={"x-api-key": "YOUR_API_KEY", "Content-Type": "application/json"},
    json=payload
)
data = response.json()
for company in data["data"]:
    print(company["name"], company.get("funding_detail"))

Lookalike targeting: find companies similar to your best customers

Once you know the profile of your best customers (e.g. product-led, AI-native, mid-market SaaS), use that profile as a filter template to find similar companies you haven’t yet reached. Use the following filters:

business_model.gtm_motion — product_led_growth, matching your best customers’ go-to-market motion.
technology.ai_maturity.scale — ai_native or ai_differentiated, matching your customers’ AI sophistication.
firmographic.founded_year — a range matching your customers’ typical company age/maturity.
financial_estimate.revenue_estimate — the revenue band where your product has the best fit.

curl --location 'https://api.akta.pro/v1/company/list/generate' \
  --header 'x-api-key: <YOUR_API_KEY>' \
  --header 'Content-Type: application/json' \
  --data '{
    "filters": {
      "business_model.gtm_motion": ["product_led_growth"],
      "technology.ai_maturity.scale": ["ai_native", "ai_differentiated"],
      "firmographic.founded_year": {"gte": 2018, "lte": 2023},
      "financial_estimate.revenue_estimate": ["10m_25m", "25m_50m"]
    },
    "sections": ["firmographic", "technology", "financial_estimate"],
    "limit": 50
  }'

import requests

payload = {
    "filters": {
        "business_model.gtm_motion": ["product_led_growth"],
        "technology.ai_maturity.scale": ["ai_native", "ai_differentiated"],
        "firmographic.founded_year": {"gte": 2018, "lte": 2023},
        "financial_estimate.revenue_estimate": ["10m_25m", "25m_50m"]
    },
    "sections": ["firmographic", "technology", "financial_estimate"],
    "limit": 50
}

response = requests.post(
    "https://api.akta.pro/v1/company/list/generate",
    headers={"x-api-key": "YOUR_API_KEY", "Content-Type": "application/json"},
    json=payload
)
data = response.json()
for company in data["data"]:
    print(company["name"], company.get("technology"))

Natural language exploration: quick prospecting without enumerating filters

When you’re exploring a segment for the first time and don’t yet know which exact filter values apply, a natural language query gets you a relevant starting list without resolving industry codes or enumerating enum values up front. Use the following:

query — a plain-English description of the target companies (e.g. "API-first fintech companies in Europe with Series B funding").
Do not pass filters in the same request — query and filters are mutually exclusive.

curl --location 'https://api.akta.pro/v1/company/list/generate' \
  --header 'x-api-key: <YOUR_API_KEY>' \
  --header 'Content-Type: application/json' \
  --data '{
    "query": "API-first fintech companies in Europe with Series B funding",
    "limit": 20
  }'

import requests

response = requests.post(
    "https://api.akta.pro/v1/company/list/generate",
    headers={"x-api-key": "YOUR_API_KEY", "Content-Type": "application/json"},
    json={
        "query": "API-first fintech companies in Europe with Series B funding",
        "limit": 20
    }
)
data = response.json()
for company in data["data"]:
    print(company["name"], company["website"])

Once a natural language query returns a promising set of results, switch to structured filters for repeatable, cacheable, and auditable queries — natural language interpretation can vary slightly between calls, while structured filters always resolve deterministically.

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Sales prospecting: build an ideal customer profile (ICP) list

Market mapping: size a market segment

Investor sourcing: find companies matching a deal thesis

Lookalike targeting: find companies similar to your best customers

Natural language exploration: quick prospecting without enumerating filters

​Sales prospecting: build an ideal customer profile (ICP) list

​Market mapping: size a market segment

​Investor sourcing: find companies matching a deal thesis

​Lookalike targeting: find companies similar to your best customers

​Natural language exploration: quick prospecting without enumerating filters

Sales prospecting: build an ideal customer profile (ICP) list

Market mapping: size a market segment

Investor sourcing: find companies matching a deal thesis

Lookalike targeting: find companies similar to your best customers

Natural language exploration: quick prospecting without enumerating filters