Target Screening - Wokelo Docs

1. Overview

The Target Screening API identifies and scores potential acquisition targets for a defined acquirer. Given an acquirer and a set of strategic criteria, Wokelo’s AI pipeline searches its coverage universe of 3M+ companies, evaluates each candidate target against the acquirer’s profile, M&A history, and stated thesis, and returns a ranked list with AI-generated deal scores, synergy commentary, and full firmographic and financial profiles. This is an asynchronous API — submitting a request returns a request_id immediately, and you must poll for status and then retrieve results once the job is complete. Read more about the async pattern in How Async APIs work. Each target in the result set is evaluated across five dimensions:

Overall Score — composite strategic fit rating (1–10)
Query Relevance Score — how closely the target matches the stated thesis in detailed_query and keywords
Deal Feasibility Score — likelihood the acquirer can financially execute the acquisition given size, balance sheet, and deal history
Deal Precedent Score — alignment with the acquirer’s historical M&A pattern and deal size
Synergy Potential Score — quantified revenue and cost synergy opportunity between acquirer and target

Each score is accompanied by an AI-written rationale and a Commentary paragraph synthesising the deal thesis — calling out specific product overlaps, customer-base expansion, integration challenges, and valuation considerations. Common use cases:

Corporate development — Generate a scored target pipeline for a strategic acquirer’s M&A team, refreshed quarterly
Buy-side M&A advisory — Build a target universe for a sponsor or strategic client engagement
Private equity bolt-on sourcing — Identify add-on acquisitions for portfolio companies based on their existing profile
Investment banking — Populate target lists for buy-side mandate pitch books or thesis-driven outreach
Strategic planning — Build-vs-buy analyses for new product categories or geographic expansion

This API is asynchronous. You submit a job, receive a request_id, poll until status is "COMPLETED", and then read results from the same response. See How Async APIs work.

2. Quick Start

Step 1 — Submit the job

curl --location 'https://api.wokelo.ai/api/enterprise/target-screening/enrich/' \
  --header 'Authorization: Bearer <YOUR_API_TOKEN>' \
  --header 'Content-Type: application/json' \
  --data '{
    "company": "microsoft",
    "parameters": {
      "detailed_query": "B2B SaaS companies in the cybersecurity space",
      "keywords": ["zero trust", "endpoint security"],
      "sample_companies": ["crowdstrike", "sentinelone"],
      "geography": ["USA"],
      "company_type": "private"
    }
  }'

import requests

response = requests.post(
    "https://api.wokelo.ai/api/enterprise/target-screening/enrich/",
    headers={
        "Authorization": "Bearer <YOUR_API_TOKEN>",
        "Content-Type": "application/json"
    },
    json={
        "company": "microsoft",
        "parameters": {
            "detailed_query": "B2B SaaS companies in the cybersecurity space",
            "keywords": ["zero trust", "endpoint security"],
            "sample_companies": ["crowdstrike", "sentinelone"],
            "geography": ["USA"],
            "company_type": "private"
        }
    }
)
request_id = response.json()["request_id"]
print("Job submitted:", request_id)

Step 2 — Poll for completion

import time

while True:
    status_resp = requests.get(
        f"https://api.wokelo.ai/api/enterprise/request/{request_id}/status/",
        headers={"Authorization": "Bearer <YOUR_API_TOKEN>"}
    )
    status = status_resp.json()["status"]
    if status == "COMPLETED":
        break
    elif status == "FAILED":
        raise Exception("Job failed")
    print(f"Status: {status} — waiting...")
    time.sleep(5)

Step 3 — Read results

result_resp = requests.get(
    f"https://api.wokelo.ai/api/enterprise/request/{request_id}/result/",
    headers={"Authorization": "Bearer <YOUR_API_TOKEN>"}
)
targets = result_resp.json()["result"]
for t in targets:
    print(t["Name"], "— Overall Score:", t.get("Overall Score"))

3. Authentication

All requests must include a Bearer token in the Authorization HTTP header. No other authentication method is supported.

Authorization: Bearer <YOUR_API_TOKEN>

API tokens are issued from your Wokelo account. Navigate to Account Details → API Credentials in the Wokelo dashboard to get your client id and client secret. Contact support@wokelo.ai if you do not yet have API access.

Never expose your token in client-side code, browser requests, or public repositories. A missing or invalid token returns 401 Unauthorized. A valid token without sufficient plan permissions returns 403 Forbidden.

4. Request Reference

Endpoint

POST https://api.wokelo.ai/api/enterprise/target-screening/enrich/

The request body is JSON. Only company is required; all parameters fields are optional refinements. The parameters object itself must always be included in the request body — pass empty values ("", [], {}) for any fields you do not want to filter on.

Parameter	Type	Required	Description
`company`	string	Required	Permalink (e.g. `"microsoft"`) or full URL of the acquirer doing the screening. Use the Company Search API to resolve a permalink if needed.
`parameters.detailed_query`	string	Optional	A natural-language description of the ideal target profile, strategic rationale, or deal criteria. The single highest-impact parameter for result quality. Example: `"B2B SaaS companies in endpoint security and zero-trust networking with mid-market and enterprise distribution"`.
`parameters.keywords`	string[]	Optional	Keywords used to identify and filter relevant target companies. Use 3–6 product or category terms. Example: `["zero trust", "endpoint security", "EDR"]`.
`parameters.sample_companies`	string[]	Optional	Permalinks (e.g. `"crowdstrike"`) or full URLs of representative companies to anchor the target search. Strongly recommended for niche or jargon-heavy markets. Use the Company Search API to resolve permalinks.
`parameters.geography`	string[]	Optional	Geographic scope as ISO country codes (e.g. `["USA", "GBR"]`). Defaults to global if omitted.
`parameters.company_type`	string	Optional	Ownership type filter. Accepted values: `"private"`, `"public"`, `"all"`. Defaults to `"all"`.
`parameters.employee_count`	string[]	Optional	Headcount band filter. Accepted values: `"1-10"`, `"11-50"`, `"51-100"`, `"101-250"`, `"251-500"`, `"501-1000"`, `"1001-5000"`, `"5001-10000"`, `"10000+"`. Empty array = all.
`parameters.founded_year`	object	Optional	Filter by founding year. Accepts `{ "from": 2010 }`, `{ "to": 2020 }`, or both.
`parameters.funding_stage`	string[]	Optional	Filter by most-recent funding stage. Accepted values: `"Non-Equity Assistance"`, `"Angel round"`, `"Pre-seed"`, `"Seed"`, `"Series A"` through `"Series J"`, `"Corporate-Funded"`, `"Debt-Funded"`, `"Private equity round"`, `"Others"`. Empty array = all.
`parameters.total_funding`	object	Optional	Filter by total funding raised in USD. Accepts `{ "from": 1000000 }`, `{ "to": 50000000 }`, or both.
`parameters.revenue`	object	Optional	Filter by annual revenue in USD. Accepts `from` and/or `to` values. Most useful when combined with `company_type: "public"`.

Full request example:

curl --location 'https://api.wokelo.ai/api/enterprise/target-screening/enrich/' \
  --header 'Authorization: Bearer <YOUR_API_TOKEN>' \
  --header 'Content-Type: application/json' \
  --data '{
    "company": "microsoft",
    "parameters": {
      "detailed_query": "B2B SaaS companies in endpoint security and zero-trust networking",
      "keywords": ["zero trust", "endpoint security", "EDR"],
      "sample_companies": ["crowdstrike", "sentinelone"],
      "geography": ["USA"],
      "company_type": "private",
      "employee_count": ["101-250", "251-500"],
      "founded_year": { "from": 2015 },
      "funding_stage": ["Series B", "Series C"],
      "total_funding": { "from": 20000000 }
    }
  }'

import requests

response = requests.post(
    "https://api.wokelo.ai/api/enterprise/target-screening/enrich/",
    headers={
        "Authorization": "Bearer <YOUR_API_TOKEN>",
        "Content-Type": "application/json"
    },
    json={
        "company": "microsoft",
        "parameters": {
            "detailed_query": "B2B SaaS companies in endpoint security and zero-trust networking",
            "keywords": ["zero trust", "endpoint security", "EDR"],
            "sample_companies": ["crowdstrike", "sentinelone"],
            "geography": ["USA"],
            "company_type": "private",
            "employee_count": ["101-250", "251-500"],
            "founded_year": {"from": 2015},
            "funding_stage": ["Series B", "Series C"],
            "total_funding": {"from": 20000000}
        }
    }
)
print(response.json())

5. Response

Job submission response

When you submit the job, you receive a response immediately with a request_id and an initial status.

{
  "request_id": "8583f59e-0412-4f1e-81e5-13cc70349cb6",
  "status": "PENDING"
}

Field	Type	Description
`request_id`	string	Unique identifier for this job. Use it to poll status and retrieve results.
`status`	string	Initial job state. One of `"PENDING"`, `"PROCESSING"`, `"COMPLETED"`, `"FAILED"`.

Completed result response

Once status is "COMPLETED", the result contains a result array of target objects.

{
  "request_id": "8583f59e-0412-4f1e-81e5-13cc70349cb6",
  "status": "COMPLETED",
  "result": [ ...target objects... ]
}

Target object fields

Each object in the result array contains the following fields: Identity & firmographics

Field	Type	Description
`Permalink`	string	Wokelo company identifier for the target (e.g. `"postscript"`, `"attentive"`). Use this with other Wokelo APIs.
`Name`	string	Display name of the target company.
`Website`	string	Target’s primary domain (e.g. `"postscript.io"`).
`HQ City`	string	Headquarters city.
`HQ Country`	string	Headquarters country.
`HQ Continent`	string	Headquarters continent.
`Founded`	string	Year the company was founded.
`Type`	string	Ownership type: `"Public"` or `"Private"`.
`Operating Status`	string	Current operational status (e.g. `"Operating"`, `"Acquired"`, `"IPO"`).

Product & business

Field	Type	Description
`Product Category`	string	High-level category describing the target’s primary product (e.g. `"SMS Marketing Platform"`).
`Core Offering`	string	AI-generated paragraph describing the target’s core business and product.
`Product Catalog`	string	Comma-separated list of key products and services offered.

People & funding

Field	Type	Description
`Employees (Crunchbase)`	string	Employee count band from Crunchbase (e.g. `"251-500"`).
`Employees (LinkedIn)`	integer or empty	Employee count from LinkedIn. May be empty.
`Funding Stage`	string	Most recent funding stage (e.g. `"Series C"`). May be empty for public or bootstrapped companies.
`Total Funding`	float or empty	Total disclosed funding raised in USD.
`Last Funding Date`	string	Date of the most recent funding round (`YYYY-MM-DD`).
`Key Investors`	string[]	Array of notable investors. May be empty.

Financials (primarily for public companies)

Field	Type	Description
`Revenue`	float or empty	Annual revenue in USD.
`Ticker`	string	Exchange and ticker symbol (e.g. `"NASDAQ:CRWD"`).
`EBITDA`	float or empty	Earnings before interest, taxes, depreciation, and amortisation.
`Net Income`	float or empty	Net income in USD.
`EBITDA Margin (%)`	float or empty	EBITDA as a percentage of revenue.
`Net Income Margin (%)`	float or empty	Net income as a percentage of revenue.
`EPS Diluted`	float or empty	Diluted earnings per share.
`ROA (%)`	float or empty	Return on assets.
`Unlevered FCF`	float or empty	Unlevered free cash flow in USD.
`D/E`	float or empty	Debt-to-equity ratio.
`Market Cap`	float or empty	Market capitalisation in USD.
`Enterprise Value`	float or empty	Enterprise value in USD.
`EV/Revenue (LTM)`	float or empty	Enterprise value to last-twelve-months revenue multiple.
`EV/EBITDA (LTM)`	float or empty	Enterprise value to LTM EBITDA multiple.
`P/E (LTM)`	float or empty	Price-to-earnings ratio on LTM basis.

M&A history

Field	Type	Description
`Acquisitions`	string	Comma-separated list of past acquisitions made by the target with year (e.g. `"Tone (2021), Privy (2021)"`).
`Investments`	string	Notable investments made by the target. May be empty.

AI scoring

Field	Type	Description
`Overall Score`	float	Composite strategic fit score (1–10) for the target as an acquisition for the acquirer. Higher is a stronger candidate.
`Overall Score Rationale`	string	One-sentence explanation of the Overall Score.
`Commentary`	string	Full AI-written deal thesis paragraph evaluating strategic rationale, synergies, integration risks, and revenue impact.
`Query Relevance Score`	float	Score (1–10) measuring how closely the target matches the stated thesis in `detailed_query` and `keywords`.
`Query Relevance Rationale`	string	One-sentence rationale for the Query Relevance Score.
`Deal Feasibility Score`	float	Score (1–10) assessing whether the acquirer can financially execute the acquisition given its size, balance sheet, and deal history.
`Deal Feasibility Rationale`	string	One-sentence rationale for the Deal Feasibility Score.
`Deal Precedent Score`	float	Score (1–10) measuring how closely this deal aligns with the acquirer’s historical M&A pattern and deal size.
`Deal Precedent Rationale`	string	One-sentence rationale for the Deal Precedent Score.
`Synergy Potential Score`	float	Score (1–10) estimating the revenue and cost synergies achievable post-acquisition.
`Synergy Potential Rationale`	string	One-sentence rationale for the Synergy Potential Score.

The Commentary field is the richest AI output in the response — a full paragraph synthesising why the target does or does not fit the acquirer’s thesis. Read this carefully before relying on the numeric scores alone.

6. Examples

Buy-side sourcing for corporate development

Find acquisition targets for Klaviyo — Series A and B SMS marketing and push notification infrastructure companies in the United States — anchored on Attentive and Postscript. Filter to high-fit candidates and sort by feasibility.

curl --location 'https://api.wokelo.ai/api/enterprise/target-screening/enrich/' \
  --header 'Authorization: Bearer <YOUR_API_TOKEN>' \
  --header 'Content-Type: application/json' \
  --data '{
    "company": "klaviyo",
    "parameters": {
      "detailed_query": "SMS marketing and push notification infrastructure companies",
      "keywords": ["SMS marketing", "push notification"],
      "sample_companies": ["attentive", "postscript"],
      "geography": ["USA"],
      "company_type": "private",
      "funding_stage": ["Series A", "Series B"]
    }
  }'

import requests, time

HEADERS = {
    "Authorization": "Bearer <YOUR_API_TOKEN>",
    "Content-Type": "application/json"
}

# Submit
job = requests.post(
    "https://api.wokelo.ai/api/enterprise/target-screening/enrich/",
    headers=HEADERS,
    json={
        "company": "klaviyo",
        "parameters": {
            "detailed_query": "SMS marketing and push notification infrastructure companies",
            "keywords": ["SMS marketing", "push notification"],
            "sample_companies": ["attentive", "postscript"],
            "geography": ["USA"],
            "company_type": "private",
            "funding_stage": ["Series A", "Series B"]
        }
    }
).json()

request_id = job["request_id"]

# Poll
while True:
    s = requests.get(
        f"https://api.wokelo.ai/api/enterprise/request/{request_id}/status/",
        headers=HEADERS
    ).json()["status"]
    if s == "COMPLETED": break
    time.sleep(5)

# Retrieve and shortlist actionable targets
targets = requests.get(
    f"https://api.wokelo.ai/api/enterprise/request/{request_id}/result/",
    headers=HEADERS
).json()["result"]

actionable = sorted(
    [t for t in targets
     if t.get("Overall Score", 0) >= 7
     and t.get("Deal Feasibility Score", 0) >= 6],
    key=lambda x: x["Overall Score"],
    reverse=True
)

for t in actionable:
    print(f"{t['Name']} ({t['HQ City']}) — Overall: {t['Overall Score']} | "
          f"Feasibility: {t['Deal Feasibility Score']} | "
          f"Relevance: {t['Query Relevance Score']}")

Sample response (excerpt):

{
  "request_id": "f525ae70-b6aa-4b6e-b0af-c40efaf9681c",
  "status": "COMPLETED",
  "result": [
    {
      "Permalink": "postscript",
      "Name": "Postscript",
      "Website": "postscript.io",
      "HQ City": "Scottsdale",
      "HQ Country": "United States",
      "Founded": "2018",
      "Type": "Private",
      "Product Category": "SMS Marketing Platform",
      "Core Offering": "Postscript provides SMS marketing solutions aimed at ecommerce brands...",
      "Employees (Crunchbase)": "251-500",
      "Funding Stage": "Series C",
      "Total Funding": 106200000.0,
      "Key Investors": ["Twilio", "Y Combinator", "Accel", "OpenView", "Greylock"],
      "Overall Score": 7.0,
      "Overall Score Rationale": "Strong strategic fit and product synergies offset by significant financial stretch and integration complexity for this scale of acquisition.",
      "Commentary": "Postscript acquisition would eliminate Klaviyo's primary SMS competitor while adding superior AI-powered automation and deliverability infrastructure. The combined platform would dominate ecommerce SMS with unified customer data and expanded automation flows. Revenue synergies from cross-selling existing customers could generate $200M+ incremental ARR within 24 months. Integration challenges and premium valuation require careful due diligence on customer overlap and retention metrics.",
      "Query Relevance Score": 9.0,
      "Deal Feasibility Score": 3.0,
      "Deal Precedent Score": 6.0,
      "Synergy Potential Score": 8.0
    },
    {
      "Permalink": "shoptext",
      "Name": "ShopText",
      "Website": "shoptext.com",
      "HQ City": "Norwalk",
      "HQ Country": "United States",
      "Founded": "2006",
      "Type": "Private",
      "Product Category": "Mobile marketing platform",
      "Employees (Crunchbase)": "11-50",
      "Funding Stage": "Series A",
      "Total Funding": 5700000.0,
      "Overall Score": 9.0,
      "Overall Score Rationale": "Strong strategic fit with Klaviyo's SMS marketing focus, clear synergies in mobile engagement technology, and highly feasible acquisition size.",
      "Commentary": "ShopText's ScanTxt technology would enable Klaviyo clients to capture consumer intent directly from physical advertisements through text responses...",
      "Query Relevance Score": 9.0,
      "Deal Feasibility Score": 10.0,
      "Deal Precedent Score": 7.0,
      "Synergy Potential Score": 8.0
    }
  ]
}

Bolt-on screening for a PE portfolio company

Identify bolt-on acquisitions for a portfolio company. Use a narrower thesis in detailed_query, restrict by geography, and filter to a target size that the portfolio company can realistically absorb.

import requests

response = requests.post(
    "https://api.wokelo.ai/api/enterprise/target-screening/enrich/",
    headers={
        "Authorization": "Bearer <YOUR_API_TOKEN>",
        "Content-Type": "application/json"
    },
    json={
        "company": "acme-vertical-saas",
        "parameters": {
            "detailed_query": "Bolt-on field-service software targeting commercial HVAC and plumbing contractors in North America with $1-10M ARR",
            "keywords": ["field service management", "HVAC", "dispatch"],
            "geography": ["USA", "CAN"],
            "company_type": "private",
            "employee_count": ["11-50", "51-100"],
            "total_funding": {"from": 1000000, "to": 25000000}
        }
    }
)
print(response.json()["request_id"])

Public-company target screen

For larger strategic acquirers evaluating public-to-public deals, restrict to public targets and use revenue filters to focus on scaled assets.

import requests

response = requests.post(
    "https://api.wokelo.ai/api/enterprise/target-screening/enrich/",
    headers={
        "Authorization": "Bearer <YOUR_API_TOKEN>",
        "Content-Type": "application/json"
    },
    json={
        "company": "salesforce",
        "parameters": {
            "detailed_query": "Public AI-native enterprise SaaS companies in customer data, analytics, and revenue intelligence",
            "keywords": ["customer data platform", "revenue intelligence", "AI analytics"],
            "company_type": "public",
            "geography": ["USA"],
            "revenue": {"from": 100000000}
        }
    }
)
print(response.json()["request_id"])

7. Error Handling

The API uses standard HTTP status codes. All error responses include a JSON body with a detail or message field.

Status	Meaning	Cause & Resolution
`200 OK` / `202 Accepted`	Success	Job submitted (`202`) or result retrieved (`200`) successfully.
`400 Bad Request`	Invalid parameters	A required field is missing or a parameter value is invalid — e.g. unrecognised `funding_stage`, missing `parameters` object, or invalid ISO country code. Check the `detail` field.
`401 Unauthorized`	Auth failed	The `Authorization` header is missing, malformed, or contains an invalid token. Verify your key in Settings → API Keys.
`403 Forbidden`	Insufficient access	Your plan does not include access to this endpoint. Contact support@wokelo.ai to review your plan.
`404 Not Found`	Resource not found	The acquirer `company` permalink could not be resolved, a `sample_companies` permalink is invalid, or the `request_id` does not exist. Use the Company Search API to verify permalinks.
`429 Too Many Requests`	Rate limit exceeded	Implement exponential back-off. The response includes a `Retry-After` header.
`500 Internal Server Error`	Server error	Retry after a brief delay. If the issue persists, contact support@wokelo.ai with your `request_id`.

Error response example:

{
  "status": "error",
  "detail": "Company permalink 'unknown-co' could not be resolved."
}

Job failure handling:

import requests, time

def poll_until_done(request_id, headers, interval=5, timeout=600):
    elapsed = 0
    while elapsed < timeout:
        resp = requests.get(
            f"https://api.wokelo.ai/api/enterprise/request/{request_id}/status/",
            headers=headers
        ).json()
        status = resp["status"]
        if status == "COMPLETED":
            return True
        elif status == "FAILED":
            raise Exception(f"Job {request_id} failed: {resp.get('detail', 'unknown error')}")
        time.sleep(interval)
        elapsed += interval
    raise TimeoutError(f"Job {request_id} did not complete within {timeout}s")

8. Best Practices

Write a specific detailed_query — this is the highest-leverage parameter The detailed_query is by far the most impactful way to sharpen the target universe. A vague query like "cybersecurity targets" returns a broad, noisy result set. A specific query naming the product category, customer segment, deal size, and strategic rationale returns a tight, actionable list. For example:

“B2B SaaS companies in endpoint security and zero-trust networking with mid-market and enterprise distribution, $20M-$ 100M ARR, and a complementary go-to-market motion”

is far stronger than:

“cybersecurity companies”

Anchor niche or jargon-heavy markets with sample_companies For specialised verticals where the AI cannot fully infer the thesis from a topic and keywords alone, supply 2–4 representative permalinks in sample_companies. This dramatically improves precision. Use the Company Search API to resolve company names to permalinks. Always include the full parameters object Even when you don’t want to filter on a field, the API expects the parameters object with every field present. Pass empty values ("", [], {}) for unused filters. Omitting parameters entirely will return a 400 Bad Request. Use both Overall Score and Query Relevance Score for triage These two scores measure different things. Overall Score is the composite strategic fit including feasibility, precedent, and synergies. Query Relevance Score measures only how closely the target matches the stated thesis. A target with high Query Relevance but low Overall Score is an on-thesis fit that’s hard to execute (e.g. too expensive, off-precedent); a target with high Overall Score but moderate Query Relevance is an adjacent opportunity worth exploring even if it wasn’t the original ask. Filter on Deal Feasibility Score to drop financially out-of-reach targets A high Overall Score paired with a low Deal Feasibility Score (≤4) usually means the target is strategically attractive but priced beyond what the acquirer can realistically execute. For most pipelines, drop these unless the acquirer is open to merger-of-equals or stock-heavy structures. Cross-check Deal Precedent Score for stylistic fit The Deal Precedent Score measures whether the proposed deal looks like the acquirer’s prior M&A. A low score doesn’t disqualify the target, but flags that the deal would be a departure — useful context for senior-stakeholder conversations. Read the Commentary for the full deal narrative The Commentary field is the richest AI output — a full paragraph synthesising the strategic rationale, synergy thesis, integration considerations, and key risks. Use it to draft the strategic-rationale section of a board memo or buy-side pitch, and to brief deal teams before outreach. Iterate the query — treat the first run as a diagnostic Review the top 20 targets and their commentaries from the first run, then refine detailed_query, swap sample_companies, or tighten filters and re-run. Two or three iterations typically produce a substantially better target universe than a single pass. Store the request_id for auditability Target screens are associated with your account and a specific point in time. Store the request_id alongside the acquirer, thesis, and run date so you can re-retrieve the result set later, track how the universe evolves across quarterly refreshes, and audit which target list was used for a given mandate or board update.

Buyer Screening

Identify and score potential acquirers for a target company — the inverse of Target Screening.

Market Map

Discover and map all companies competing in a specific market or product category.

Competitor List

Generate a structured list of direct and indirect competitors for any company.

Company Deep Intelligence

Generate deep AI intelligence on any target — business model, financials, strategy, and M&A history.

Company Instant Enrichment

Synchronously enrich firmographic and financial data for any target in the screened list.

Industry Deep Intelligence

Generate a deep intelligence report on the industry behind the screen for thesis development.

​1. Overview

​2. Quick Start

​3. Authentication

​4. Request Reference

​5. Response

​Job submission response

​Completed result response

​Target object fields

​6. Examples

​Buy-side sourcing for corporate development

​Bolt-on screening for a PE portfolio company

​Public-company target screen

​7. Error Handling

​8. Best Practices

​9. Related APIs

Buyer Screening

Market Map

Competitor List

Company Deep Intelligence

Company Instant Enrichment

Industry Deep Intelligence

1. Overview

2. Quick Start

3. Authentication

4. Request Reference

5. Response

Job submission response

Completed result response

Target object fields

6. Examples

Buy-side sourcing for corporate development

Bolt-on screening for a PE portfolio company

Public-company target screen

7. Error Handling

8. Best Practices

9. Related APIs