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November 2, 2026

# Best web data APIs for AI-powered sales tools

AI sales tools are limited by the data layer beneath them, not by the CRM or sequencer on top. This guide evaluates web data APIs as sales infrastructure: which API types serve which workflows, how eight providers compare across search, extraction, and deep research, and how to wire them into an existing sales stack.

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Author: By Parallel
Tags:Guides

Most "best AI sales tools" guides evaluate end-user platforms: which CRM has the slickest UI, which outreach tool writes the catchiest emails. They skip the layer underneath: the APIs that decide whether your agents surface current signals or regurgitate stale database records. Parallel, which publishes this guide, provides web search, extraction, decision, and deep research APIs built for AI agents.

## Why AI sales tools need web data APIs

Traditional sales data providers built their businesses on static databases. Apollo, ZoomInfo, and Clearbit (now part of HubSpot as Breeze Intelligence) aggregate contact information, company firmographics, and technographic data into pre-packaged datasets. Every customer gets the same records. When you query "Series B fintech companies in San Francisco," you're searching against data that might be weeks or months old.

AI sales tools need real-time signals: a prospect just announced a funding round on TechCrunch, their VP of Engineering left according to LinkedIn, they adopted a competitor's product per BuiltWith[BuiltWith], they filed an 8-K with SEC EDGAR[SEC EDGAR]. These signals decay fast: a funding announcement is actionable for 48 hours, and after that every sales team in your market has seen it.

Web data APIs close this gap by providing live, structured data from the open web. Instead of querying a static database, you query the web itself. The API crawls, indexes, extracts, and structures the results.

Web data APIs for sales intelligence fall into four categories:

  • - **Search APIs** help you discover relevant pages, companies, and signals across the web
  • - **Extraction APIs** pull clean, structured content from specific URLs you've identified
  • - **Deep research APIs** combine search, extraction, and AI reasoning to produce comprehensive profiles from multiple sources
  • - **Decision APIs** answer a closed question about a company, such as yes or no, one option from a list, or a score, from current web evidence

Each category serves a different stage in your sales data enrichment[data enrichment] pipeline.

## Types of web data APIs for sales

### Search APIs

Search APIs[Search APIs] return ranked results and excerpts for queries you submit. You ask a question; the API returns pages that answer it. The best search APIs for AI workloads accept natural-language _objectives_ rather than keyword strings.

**Sales use case:** Prospect discovery, competitive intelligence, market research. Find companies that match specific criteria, surface news about target accounts, identify market trends.

**Key differentiator:** Semantic search[Semantic search] with objective-driven queries. An objective-driven search for "fintech companies that raised Series B funding in the last 90 days" understands intent and returns relevant results even when pages don't use your exact phrasing.

**Example:** You need to identify prospects who recently expanded their engineering teams. A semantic search API lets you query: "B2B SaaS companies that announced significant engineering hiring in Q1 2026." The API searches TechCrunch, company blogs, LinkedIn posts, and press releases, then returns ranked results with excerpts.

### Extraction APIs

Extraction APIs convert any URL into clean, structured data. You provide a page; the API returns markdown, JSON, or structured fields. The best extraction APIs accept an _objective_ that focuses the extraction on what you need.

**Sales use case:** Pull detailed information from target account websites, job postings, press releases, and SEC filings. Build prospect profiles from primary sources rather than aggregated databases.

**Key differentiator:** Objective-driven extraction versus CSS selector web scraping[web scraping]. Traditional scraping requires you to specify exactly which HTML elements to extract. Objective-driven extraction lets you state what you want ("extract the leadership team and their titles") and the API figures out where that information lives on the page.

**Example:** You've identified a target account. You want their leadership team, recent product announcements, and open job postings. An extraction API pulls all three from their website, careers page, and press section in one call, returning structured JSON ready for your CRM.

### Deep research APIs

Deep research APIs[Deep research APIs] combine search, extraction, and AI reasoning into multi-step research workflows. You define what you want to know; the API orchestrates the entire research process across multiple web sources.

**Sales use case:** Build comprehensive prospect profiles, run custom enrichment workflows with any data schema, generate account intelligence reports that synthesize information from Crunchbase[Crunchbase], TechCrunch, BuiltWith, and SEC EDGAR.

**Key differentiator:** Custom schema definition versus fixed data fields. Traditional data providers return the same 15 fields for every company. Deep research APIs let you define exactly what fields you need, including industry-specific or workflow-specific attributes no vendor has pre-built.

**Example:** You need to enrich 500 new leads with a custom schema: funding history, tech stack, recent news mentions, executive changes, and whether they use a specific competitor product. A deep research API call might look like this:

### Python
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from parallel import Parallel

client = Parallel()

# Define custom enrichment schema for sales prospects
fields = {
    "funding_history": "List of funding rounds with dates and amounts",
    "tech_stack": "Key technologies and tools they use",
    "recent_news": "Notable announcements in the last 90 days",
    "leadership_changes": "Any C-suite or VP changes in the last year",
    "competitor_usage": "Whether they use Salesforce, HubSpot, or Outreach"
}

task_run = client.task_run.create(
    input={"company_name": "Acme Corp", "domain": "acme.com"},
    task_spec={
        "output_schema": {
            "type": "json",
            "json_schema": {
                "type": "object",
                "properties": {
                    name: {"type": "string", "description": desc}
                    for name, desc in fields.items()
                },
                "required": list(fields),
                "additionalProperties": False
            }
        }
    },
    processor="core"
)

result = client.task_run.result(task_run.run_id, api_timeout=3600)
print(result.output.content)``` 
from parallel import Parallel
 
client = Parallel()
 
# Define custom enrichment schema for sales prospects
fields = {
    "funding_history": "List of funding rounds with dates and amounts",
    "tech_stack": "Key technologies and tools they use",
    "recent_news": "Notable announcements in the last 90 days",
    "leadership_changes": "Any C-suite or VP changes in the last year",
    "competitor_usage": "Whether they use Salesforce, HubSpot, or Outreach"
}
 
task_run = client.task_run.create(
    input={"company_name": "Acme Corp", "domain": "acme.com"},
    task_spec={
        "output_schema": {
            "type": "json",
            "json_schema": {
                "type": "object",
                "properties": {
                    name: {"type": "string", "description": desc}
                    for name, desc in fields.items()
                },
                "required": list(fields),
                "additionalProperties": False
            }
        }
    },
    processor="core"
)
 
result = client.task_run.result(task_run.run_id, api_timeout=3600)
print(result.output.content)
```

This Task API run orchestrates searches across multiple sources, extracts relevant information, and returns structured JSON with citations for each field.

## Best web data APIs for AI sales tools

The right API depends on your workflow: some excel at high-volume search, others at deep enrichment. The table below summarizes the leading options as of November 2026, followed by detailed profiles.

APICategorySales use casePricing modelKey differentiator
ParallelSearch + Extract + Decisions + Deep ResearchFull-stack sales intelligence, lead qualification and routingPer-request (search from $0.001, extract $0.001, decisions $0.001)Integrated suite with objective-driven queries and per-field citations
Bright DataExtract + ProxyHigh-volume scraping and data collectionUsage-based (varies by product)Massive proxy network and pre-built datasets
ScrapingBeeExtractSingle-page extraction with JS renderingMonthly credit plans (from $19/month)Simple API, handles CAPTCHAs and headless browsing
People Data LabsContact/Company DataContact enrichment and prospectingPer-record credits (from $98/month)Large B2B contact database with API access
CrustdataCompany IntelligenceFirmographic and growth signalsSubscription + usageGrowth metrics, headcount tracking, technographics
CoresignalCompany IntelligenceEmployee data and company insightsSubscription + usageDeep LinkedIn-derived company intelligence
ScrapeGraphAIExtract (AI-native)LLM-powered web extractionOpen source, or managed API from $17/monthAI-native extraction using natural language
You.comSearchWeb search with AI summarizationPer-request ($5 per 1,000 calls)News API and RAG-optimized search results

### Parallel

Parallel is a single API suite built for AI agents[AI agents]. The Search API accepts natural-language objectives and returns ranked results with token-dense excerpts optimized for LLM context windows. The Extract API converts any URL to clean markdown. The Decisions API answers yes-or-no, multiple-choice, and score questions about a company from live web evidence, with citations. The Task API handles multi-step research workflows with custom schemas.

**Sales-specific strengths:** You can discover prospects with Search, pull detailed profiles with Extract, qualify and route leads with Decisions, and run batch enrichment with Task, all through one platform. The Basis framework provides per-field citations, reasoning traces, and calibrated confidence scores, so your sales team can verify any data point before acting on it.

**Data sources:** Parallel's proprietary web index covers billions of pages with millions added daily. Searches surface information from Crunchbase, LinkedIn (public profiles), TechCrunch, BuiltWith technographic data, SEC EDGAR filings, company blogs, and press releases.

**Pricing[Pricing]:** Search API from $1 per 1,000 requests with Turbo or Fast mode (~200ms and ~700ms latency; Fast is the recommended default), $5 per 1,000 for Basic and Advanced (10 results included), Extract API at $0.001 per URL, Decisions API at $1 per 1,000 requests with up to 100 questions per request, Task API from $5 to $2,400 per 1,000 runs depending on processor.

**Best for:** Teams building custom AI sales tools who want an integrated data layer without stitching together multiple vendors.

**Example: Objective-driven prospect search**

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from datetime import date, timedelta
from parallel import Parallel

client = Parallel()

# Find prospects matching specific criteria
results = client.search(
    objective="B2B SaaS companies that raised Series A or B funding in 2026, "
              "have between 50-200 employees, and are hiring for sales roles",
    search_queries=[
        "B2B SaaS Series B funding 2026",
        "SaaS startup hiring sales team",
        "Series A SaaS raises funding"
    ],
    mode="fast",
    advanced_settings={
        "max_results": 25,
        "source_policy": {"after_date": (date.today() - timedelta(days=30)).isoformat()}
    }
)

for result in results.results:
    print(f"{result.title}: {result.url}")
    for excerpt in result.excerpts:
        print(f"Excerpt: {excerpt[:200]}\n")``` 
from datetime import date, timedelta
from parallel import Parallel
 
client = Parallel()
 
# Find prospects matching specific criteria
results = client.search(
    objective="B2B SaaS companies that raised Series A or B funding in 2026, "
              "have between 50-200 employees, and are hiring for sales roles",
    search_queries=[
        "B2B SaaS Series B funding 2026",
        "SaaS startup hiring sales team",
        "Series A SaaS raises funding"
    ],
    mode="fast",
    advanced_settings={
        "max_results": 25,
        "source_policy": {"after_date": (date.today() - timedelta(days=30)).isoformat()}
    }
)
 
for result in results.results:
    print(f"{result.title}: {result.url}")
    for excerpt in result.excerpts:
        print(f"Excerpt: {excerpt[:200]}\n")
```

### Bright Data

Bright Data[Bright Data] operates one of the largest proxy networks globally. Its Scraper Studio builds custom scrapers, and its Dataset Marketplace sells pre-built datasets.

**Sales-specific strengths:** High-volume data collection at scale. Pre-built datasets for company information, job postings, and e-commerce data. Handles geo-restrictions and CAPTCHAs.

**Data sources:** Any publicly accessible website. Pre-built datasets cover LinkedIn company pages, Glassdoor, Crunchbase, and job boards.

**Pricing:** Usage-based model varying by product and volume.

**Best for:** Teams with high-volume scraping needs and engineering resources.

### ScrapingBee

ScrapingBee[ScrapingBee] provides simple web scraping through a clean API. You submit a URL; they return rendered HTML or extracted content, handling proxies and CAPTCHA solving.

**Sales-specific strengths:** Easy integration for single-page extraction. JavaScript rendering for dynamic sites.

**Data sources:** Any URL you provide. No pre-built datasets.

**Pricing:** Monthly credit plans, starting at $19/month for 75,000 API credits.

**Best for:** Teams that need simple extraction from known URLs.

### People Data Labs

People Data Labs[People Data Labs] provides B2B contact and company data through API access to their aggregated database.

**Sales-specific strengths:** Large contact database with emails, phone numbers, job history, and company associations. Good for enriching existing lead lists.

**Data sources:** Aggregated from public web sources and professional networks. Updated periodically.

**Pricing:** Credit-based, one credit per enriched record; self-serve plans start at $98/month for 350 credits, with volume discounts and custom enterprise pricing.

**Best for:** Teams focused on contact enrichment who need structured B2B data.

### Crustdata

Crustdata specializes in company intelligence with a focus on growth signals. They track headcount changes, funding events, technographic shifts, and web traffic patterns.

**Sales-specific strengths:** Growth metrics that traditional databases miss: headcount velocity, web traffic trends, technology adoption patterns. Good for identifying companies in growth phases.

**Data sources:** LinkedIn-derived headcount data, technographic crawling via BuiltWith and similar, funding data from Crunchbase, web traffic estimates.

**Pricing:** Subscription model with usage-based components.

**Best for:** Teams prioritizing growth signals and timing-based prospecting.

### Coresignal

Coresignal provides company and employee data derived from professional network profiles.

**Sales-specific strengths:** Deep employee-level intelligence: seniority distribution, tenure patterns, hiring trends. Useful for identifying decision-makers.

**Data sources:** LinkedIn-derived company and employee data, job postings, firmographic attributes.

**Pricing:** Subscription with usage-based pricing.

**Best for:** Teams building account-based marketing workflows that require employee-level intelligence.

### ScrapeGraphAI

ScrapeGraphAI is an open-source library that uses LLMs to extract structured data from web pages using natural language descriptions. The company also runs a managed API built on the library.

**Sales-specific strengths:** AI-native extraction without CSS selectors or XPath. Good for prototyping.

**Data sources:** Any URL you provide.

**Pricing:** The open-source library is free; you pay for LLM API calls and hosting. The managed API uses monthly credit plans starting at $17/month for 10,000 credits.

**Best for:** Technical teams prototyping extraction workflows who want full infrastructure control.

### You.com

You.com offers web search APIs including a News API and RAG-optimized search results.

**Sales-specific strengths:** News API surfaces recent coverage about companies and industries. Results work well as LLM context.

**Data sources:** Web search index and news publications.

**Pricing:** Per-request, $5 per 1,000 calls for the Web Search API, with the news endpoint included.

**Best for:** Teams building AI assistants that need web-grounded answers with news coverage.

## How to choose the right API for your sales stack

Evaluate web data APIs for AI sales workflows on five criteria.

**1. Data freshness**

How often does the API update its index? For deal signals like funding announcements, leadership changes, or product launches, you need data updated within hours. Ask whether the API offers live web fetching versus cached index queries. A "fresh" result from a week-old cache is already stale for sales intelligence.

**2. Output format**

Does the API return LLM-ready outputs (clean markdown, structured JSON) or raw HTML requiring post-processing? Post-processing raw HTML burns tokens your model could spend on reasoning. APIs built for AI workloads optimize for _token density_, the ratio of useful information to total tokens.

**3. Custom schema support**

Can you define your own enrichment fields, or are you limited to the vendor's predetermined data points? Sales workflows vary by industry, deal size, and sales motion. An API that only returns "company size" and "industry" won't help when you need "SOC 2 certification status" or "current marketing automation platform."

**4. Accuracy and sourcing**

Does the API provide citations, confidence scores, or source URLs for each data point? Reps act on this information, so a wrong funding amount or outdated headcount wastes their time. Look for APIs that surface _where_ each piece of data came from and _how confident_ the system is in its accuracy.

**5. Compliance**

Does the vendor hold SOC 2 Type II certification? What's their approach to GDPR, CCPA, and data subject requests? Scraping publicly available business data generally falls under legitimate interest[legitimate interest], but you need vendors who handle data responsibly and can support your compliance requirements.

**Decision framework:**

  • - **Need discovery?** Prioritize search APIs with semantic query support
  • - **Need extraction from known URLs?** Evaluate extraction APIs on JS rendering, output format, and rate limits
  • - **Need comprehensive profiles from multiple sources?** Deep research APIs with custom schemas save integration work
  • - **Need to qualify or route records?** A decision API returns a typed answer for each record from current web evidence, at a cost low enough to run on every lead
  • - **Need continuous monitoring?** Look for APIs with webhook-based alerting for new results

## Connecting web data APIs to your AI sales workflow

A typical integration into your existing sales infrastructure has three layers: the web data API retrieves information, an AI processing layer reasons over it, and your sales platform (CRM, outreach tool) acts on the results.

**Practical integration pattern:**

  1. **Discovery:** Use a search API to find companies matching your ideal customer profile
  2. **Qualification:** Use a decision API to check fit and route each company before spending on deeper research
  3. **Deep dive:** Pass promising results to an extraction API for detailed profile information
  4. **Enrichment:** Run a deep research API to build structured profiles with your custom schema
  5. **Activation:** Push enriched data to Salesforce, HubSpot, or your outreach platform via their APIs

This pattern works for both batch workflows (enrich 1,000 new sign-ups weekly) and real-time triggers (alert when a target account announces funding).

**Continuous intelligence with monitoring APIs:**

Enrichment doesn't have to be a one-time job. A monitoring API can track queries like "funding announcements from companies in my pipeline" or "leadership changes at target accounts" and deliver alerts via webhooks to Slack or your CRM.

For a deeper look at how AI web enrichment for sales[AI web enrichment for sales] works in practice, see our guide on building enrichment pipelines.

**Example: Batch prospect enrichment**

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import time
from parallel import Parallel
from parallel.types import TaskSpecParam, JsonSchemaParam
from parallel.types.run_input_param import RunInputParam
from parallel.types.task_run_event import TaskRunEvent

client = Parallel()

# Batch enrich prospects with custom schema
prospects = [
    {"name": "Acme Corp", "domain": "acme.com"},
    {"name": "TechStart Inc", "domain": "techstart.io"},
    # ... more prospects
]

enrichment_schema = {
    "funding_stage": "Most recent funding round (Seed, Series A, B, etc.)",
    "total_raised": "Total funding raised in USD",
    "employee_count": "Approximate number of employees",
    "tech_stack": "Key technologies: CRM, marketing automation, data warehouse",
    "recent_news": "Most significant news in the past 90 days",
    "key_decision_makers": "VP+ titles in sales, marketing, or operations"
}

task_spec = TaskSpecParam(
    output_schema=JsonSchemaParam(json_schema={
        "type": "object",
        "properties": {
            name: {"type": "string", "description": desc}
            for name, desc in enrichment_schema.items()
        },
        "required": list(enrichment_schema),
        "additionalProperties": False
    })
)

# Create a task group for batch processing
task_group = client.task_group.create()
client.task_group.add_runs(
    task_group.task_group_id,
    inputs=[RunInputParam(input=p, processor="core") for p in prospects],  # Balance speed and depth
    default_task_spec=task_spec
)

# Wait for the group to finish
while client.task_group.retrieve(task_group.task_group_id).status.is_active:
    time.sleep(10)

# Results include citations for each field
runs = client.task_group.get_runs(
    task_group.task_group_id, include_input=True, include_output=True
)
for event in runs:
    if isinstance(event, TaskRunEvent) and event.output:
        output = event.output.content
        basis = {b.field: b for b in event.output.basis}
        print(f"Company: {event.input.input['name']}")
        print(f"Funding: {output['funding_stage']} - {output['total_raised']}")
        print(f"Source: {basis['funding_stage'].citations[0].url}\n")``` 
import time
from parallel import Parallel
from parallel.types import TaskSpecParam, JsonSchemaParam
from parallel.types.run_input_param import RunInputParam
from parallel.types.task_run_event import TaskRunEvent
 
client = Parallel()
 
# Batch enrich prospects with custom schema
prospects = [
    {"name": "Acme Corp", "domain": "acme.com"},
    {"name": "TechStart Inc", "domain": "techstart.io"},
    # ... more prospects
]
 
enrichment_schema = {
    "funding_stage": "Most recent funding round (Seed, Series A, B, etc.)",
    "total_raised": "Total funding raised in USD",
    "employee_count": "Approximate number of employees",
    "tech_stack": "Key technologies: CRM, marketing automation, data warehouse",
    "recent_news": "Most significant news in the past 90 days",
    "key_decision_makers": "VP+ titles in sales, marketing, or operations"
}
 
task_spec = TaskSpecParam(
    output_schema=JsonSchemaParam(json_schema={
        "type": "object",
        "properties": {
            name: {"type": "string", "description": desc}
            for name, desc in enrichment_schema.items()
        },
        "required": list(enrichment_schema),
        "additionalProperties": False
    })
)
 
# Create a task group for batch processing
task_group = client.task_group.create()
client.task_group.add_runs(
    task_group.task_group_id,
    inputs=[RunInputParam(input=p, processor="core") for p in prospects],  # Balance speed and depth
    default_task_spec=task_spec
)
 
# Wait for the group to finish
while client.task_group.retrieve(task_group.task_group_id).status.is_active:
    time.sleep(10)
 
# Results include citations for each field
runs = client.task_group.get_runs(
    task_group.task_group_id, include_input=True, include_output=True
)
for event in runs:
    if isinstance(event, TaskRunEvent) and event.output:
        output = event.output.content
        basis = {b.field: b for b in event.output.basis}
        print(f"Company: {event.input.input['name']}")
        print(f"Funding: {output['funding_stage']} - {output['total_raised']}")
        print(f"Source: {basis['funding_stage'].citations[0].url}\n")
```

This workflow enriches a batch of prospects with custom fields, returns structured JSON ready for CRM import, and provides citations so your team can verify any data point.

**Integration tips:**

  • - **Start with one API, then expand.** Get value from search before adding extraction and deep research
  • - **Cache aggressively.** Company profiles don't change hourly. Cache results and refresh on a schedule
  • - **Handle rate limits gracefully.** Build queuing into your pipeline for batch operations
  • - **Log everything.** Track which sources informed which data points for compliance and debugging

## Qualify and route leads with the Decisions API

Most inbound leads need a routing decision before they need a full profile: which segment, which rep, or whether to work them at all. Those decisions depend on facts that change, such as funding stage, headcount, and what the company sells, so a CRM field from last quarter can send a lead to the wrong place. A decision API[decision API] answers closed questions like these and returns a typed answer with probabilities. Parallel’s Decisions API also retrieves current web evidence for each request and returns the sources it used.

In a launch demo[launch demo], Parallel ran 500 inbound leads through the Decisions API in under nine seconds for about $0.50. Of the leads it routed without a person, 97% landed in the right segment. That is Parallel’s own result from one demo run, so test on a sample of your own hand-routed leads before you rely on it.

Ask every question about a lead in one request. The questions share one set of retrieved evidence, so the whole batch costs one call: $1 per 1,000 requests, with up to 100 questions per request.

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import os

import requests

ROUTING_QUESTIONS = {
    "segment": {
        "type": "choice",
        "instructions": "Which sales segment fits this company by employee count? "
                        "Select unknown when evidence is insufficient.",
        "criteria": {
            "smb": "Fewer than 100 employees.",
            "mid_market": "100 to 999 employees.",
            "enterprise": "1,000 or more employees.",
            "unknown": "The evidence does not establish the company size.",
        },
    },
    "builds_ai_products": {
        "type": "noul",
        "instructions": "Does this company build or sell AI-powered products or agents?",
    },
    "funding_stage": {
        "type": "choice",
        "instructions": "What is the company's most recent funding stage?",
        "criteria": {
            "none_disclosed": "Bootstrapped, or no funding has been disclosed.",
            "seed": "Pre-seed or seed.",
            "series_a_b": "Series A or Series B.",
            "growth_or_public": "Series C or later, or publicly traded.",
        },
    },
}


def route_lead(company: str, domain: str, threshold: float = 0.8) -> dict:
    response = requests.post(
        "https://api.parallel.ai/v1/decisions",
        headers={"x-api-key": os.environ["PARALLEL_API_KEY"]},
        json={
            "state": {"company": company, "website": f"https://{domain}"},
            "questions": ROUTING_QUESTIONS,
        },
        timeout=30,
    )
    response.raise_for_status()
    decision = response.json()
    segment = decision["answers"]["segment"]

    # Route automatically only when the segment answer is confident
    confident = (
        segment["type"] == "choice"
        and segment["choice"] != "unknown"
        and segment["probabilities"][segment["choice"]] >= threshold
    )

    return {
        "queue": segment["choice"] if confident else "manual_review",
        "answers": decision["answers"],
        "sources": [c["url"] for c in decision["citations"]],
    }


print(route_lead("Acme Corp", "acme.com"))``` 
import os
 
import requests
 
ROUTING_QUESTIONS = {
    "segment": {
        "type": "choice",
        "instructions": "Which sales segment fits this company by employee count? "
                        "Select unknown when evidence is insufficient.",
        "criteria": {
            "smb": "Fewer than 100 employees.",
            "mid_market": "100 to 999 employees.",
            "enterprise": "1,000 or more employees.",
            "unknown": "The evidence does not establish the company size.",
        },
    },
    "builds_ai_products": {
        "type": "noul",
        "instructions": "Does this company build or sell AI-powered products or agents?",
    },
    "funding_stage": {
        "type": "choice",
        "instructions": "What is the company's most recent funding stage?",
        "criteria": {
            "none_disclosed": "Bootstrapped, or no funding has been disclosed.",
            "seed": "Pre-seed or seed.",
            "series_a_b": "Series A or Series B.",
            "growth_or_public": "Series C or later, or publicly traded.",
        },
    },
}
 
 
def route_lead(company: str, domain: str, threshold: float = 0.8) -> dict:
    response = requests.post(
        "https://api.parallel.ai/v1/decisions",
        headers={"x-api-key": os.environ["PARALLEL_API_KEY"]},
        json={
            "state": {"company": company, "website": f"https://{domain}"},
            "questions": ROUTING_QUESTIONS,
        },
        timeout=30,
    )
    response.raise_for_status()
    decision = response.json()
    segment = decision["answers"]["segment"]
 
    # Route automatically only when the segment answer is confident
    confident = (
        segment["type"] == "choice"
        and segment["choice"] != "unknown"
        and segment["probabilities"][segment["choice"]] >= threshold
    )
 
    return {
        "queue": segment["choice"] if confident else "manual_review",
        "answers": decision["answers"],
        "sources": [c["url"] for c in decision["citations"]],
    }
 
 
print(route_lead("Acme Corp", "acme.com"))
```

Write the queue, the answers, and the source URLs back to the lead record. The citations cover the whole call rather than each answer, so store them with the routing decision and a rep can check why a lead landed where it did. Set the threshold from a sample of leads your team has already routed by hand; a probability of 0.8 from one model isn’t calibrated the same way as 0.8 from another.

Once a lead clears routing and needs a full profile, pass it to the Task API with an enrichment schema like the one above. The Decisions API answers closed questions; the Task API fills open fields such as recent news or key decision makers.

## FAQs

**What are web data APIs and why do AI sales tools need them?**

Web data APIs retrieve live, structured information from the open web. AI sales tools need them because static databases can't provide the real-time signals (funding rounds, leadership changes, competitive moves) that make prospect intelligence actionable.

**What's the difference between search, extraction, decision, and deep research APIs?**

Search APIs find relevant pages from across the web. Extraction APIs pull structured content from specific URLs you provide. Decision APIs return a typed answer to a closed question, such as whether a lead fits your ICP or which segment it belongs in. Deep research APIs orchestrate multi-step research across many sources and return comprehensive profiles.

**How much do web data APIs for sales cost?**

Pricing varies: per-request ($0.001 to $0.005 for search and extraction calls, more for deep research), per-record (about $0.10 to $0.28 on entry-level self-serve plans), or subscription tiers. Most vendors offer free trials. Budget $500 to $2,000/month for production sales workflows handling thousands of queries.

**Can web data APIs replace Apollo or ZoomInfo?**

They serve different needs. Traditional providers offer pre-built contact databases optimized for quick lookup. Web data APIs let you build custom intelligence pipelines that capture signals those databases don't track. Many teams use both.

**Are web data APIs for sales GDPR compliant?**

Compliance depends on the provider and your use case. Scraping publicly available business information generally qualifies as legitimate interest. Look for SOC 2 Type II certified vendors with clear data processing agreements. Consult legal counsel for your specific jurisdiction and use case.

**Ready to power your AI sales stack with accurate, real-time web data?**

Parallel's API suite gives you search, extraction, decisions, and deep research in one platform, built for AI agents.

Start Building[Start Building]

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