We extract contact profiles, company firmographics, technographics, funding rounds, and intent signals from Apollo.io. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Contact Profiles objects from apollo.io. All fields typed and schema-versioned.
"contact_id": "c_628a9b1f", "full_name": "Arjun Patel", "job_title": "VP of Engineering", "seniority": "VP", "email": "arjun.p@example.com", "email_status": "verified", "linkedin_url": "linkedin.com/in/arjunpatel", "company_name": "TechCorp India"
| # | contact_id | first_name | last_name | full_name | job_title | seniority |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Company Profiles objects from apollo.io. All fields typed and schema-versioned.
"company_id": "org_918273", "name": "DataFlirt", "domain": "dataflirt.com", "industry": "Information Technology", "employee_range": "51-200", "estimated_revenue": "$10M-$50M", "headquarters": "Bengaluru, Karnataka", "year_founded": 2020
| # | company_id | name | domain | industry | sub_industry | employee_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Technographics objects from apollo.io. All fields typed and schema-versioned.
"domain": "example.com", "technology_name": "Marketo", "technology_category": "Marketing Automation", "vendor": "Adobe", "first_detected": "2023-01-15T00:00:00Z", "last_detected": "2023-10-12T00:00:00Z", "confidence_score": 98
| # | company_id | domain | technology_name | technology_category | vendor | first_detected |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Intent Signals objects from apollo.io. All fields typed and schema-versioned.
"domain": "example.com", "intent_topic": "Data Warehousing", "intent_score": 85, "signal_date": "2023-10-24", "source_category": "Content Consumption", "surge_intensity": "High"
| # | company_id | domain | intent_topic | intent_score | signal_date | source_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Funding & Jobs objects from apollo.io. All fields typed and schema-versioned.
"domain": "example.com", "round_type": "Series B", "funding_amount": 25000000, "funding_currency": "USD", "funding_date": "2023-08-14", "lead_investors": "['Sequoia Capital', 'Accel']", "job_title": "Senior Data Engineer"
| # | company_id | domain | round_type | funding_amount | funding_currency | funding_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Apollo.io scraper navigates strict rate limits, dynamic GraphQL endpoints, and complex pagination to deliver structured firmographics and contact data directly to your systems.
Extract names, verified emails, job titles, seniority levels, and social profiles across target accounts.
Capture employee headcount, revenue estimates, industry classifications, and headquarters locations.
Map the software stack of target companies including CRMs, marketing automation, and infrastructure tools.
Track topic surges and intent scores to identify accounts actively researching specific solutions.
Extract recent investment rounds, funding amounts, and lead investor details to time outreach.
Monitor open roles by department and location to identify company growth areas and technology needs.
Match existing incomplete records against Apollo.io data to fill missing fields and update stale contacts.
Map reporting structures and department sizes to identify decision makers and internal champions.
Monitor specific accounts for job changes, new funding, or intent surges with daily delta deliveries.
Brief in. Clean data out.
Provide target domains, industry filters, or specific buyer personas. We design the extraction schema together.
We configure GraphQL interception, proxy rotation, session management, and rate limit handling for apollo.io.
Schema validation, null-rate checks, and data normalisation before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Apollo.io employs strict rate limiting and complex API structures. Here is how we maintain reliable extraction pipelines.
Apollo.io relies heavily on complex GraphQL queries with dynamic variables. We intercept these internal API calls, map the query structures, and handle cursor-based pagination to extract deep result sets without rendering overhead.
Strict rate limits apply to search queries and profile views. We distribute requests across managed session pools and residential proxies, implementing precise delays to stay under threshold triggers.
Contact and company data is deeply nested within Apollo's responses. Our pipeline flattens and normalises these structures into clean, relational schemas ready for immediate SQL querying.
Accessing Apollo requires passing Cloudflare challenges. We utilise TLS fingerprint spoofing and CapSolver integrations to maintain persistent access without triggering blocks.
For ongoing monitoring, we hash existing records and only extract profiles that show modification timestamps, reducing unnecessary API calls and keeping your database updated efficiently.
RevOps teams feed structured contact lists directly into sequencing tools to scale outbound campaigns.
Marketing operations normalise and complete inbound lead data by appending firmographics and technographics.
Strategy teams extract entire industry segments to calculate TAM and identify market penetration opportunities.
Product teams monitor competitor hiring trends and technology adoption rates across shared accounts.
Demand generation teams use intent signals and funding triggers to launch highly targeted ad campaigns.
Venture capital firms track headcount growth and employee turnover across portfolio companies and prospects.
"Apollo.io holds the most accurate B2B contact graph and technographic data available today, but accessing it beyond manual exports requires a managed extraction pipeline."
Extracting B2B intelligence at scale requires bypassing strict rate limits, handling GraphQL pagination, and managing session rotation. DataFlirt absorbs this infrastructure overhead so your revenue operations team can focus on pipeline generation.
Everything supported by our apollo.io scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
We bypass heavy DOM rendering by targeting internal APIs directly. Scrapy manages request concurrency and pagination logic, drastically reducing latency.
Redis clusters handle distributed session states and rate limit counters across our proxy pools, ensuring we stay within acceptable request thresholds.
Pipelines run on Kubernetes for sustained extraction. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in Postgres.
Data delivered to where your team already works — no new tooling required.
About apollo.io scraping, legality, and pipeline operations.
Ask us directly →Direct dial numbers on Apollo.io are heavily gated and require credit consumption per reveal. We cannot bypass this credit system. We extract all publicly available firmographics and standard contact details that do not require credit usage.
We distribute requests across large pools of residential proxies and manage session rotation via Redis. Our crawlers implement exponential backoff and precise request timing to avoid triggering rate limit blocks.
Yes. You can provide a CSV of domains or company names via S3 or API. Our pipeline will query Apollo for those specific entities and return the enriched firmographic and technographic data.
Intent signals are extracted based on the pipeline schedule you select. For active monitoring, we recommend daily runs to capture topic surges as they happen.
Yes. We normalise Apollo's nested JSON structures into flat CSV or relational database formats, mapping fields directly to standard Salesforce or HubSpot schemas.
Our smallest packages start at a defined list of 5,000 target accounts. For continuous TAM monitoring or custom integration requirements, we price based on volume and delivery frequency. Contact us for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off TAM extraction or a continuous CRM enrichment feed, we scope, build, and operate the pipeline. Tell us what you need.
