In 60% of CRM records, key fields are missing: job title, company size, technology stack. Managers spend hours manually searching LinkedIn and Google—and the data becomes outdated within a month. We build AI pipelines that supplement a client profile from a dozen open sources in seconds: LinkedIn, Crunchbase, GitHub, news feeds, registries. Result: a contact with 20+ fields instead of 3.
In a typical CRM with 50,000 contacts, manual entry consumes up to 20 person-hours per week. Automation cuts this time by 80% and simultaneously improves sales funnel forecast quality by 25%. We have already implemented such solutions for companies with CRMs ranging from 10,000 to 2,000,000 records—and in every case, payback occurred within the first 3 months. The time savings allow the team to focus on lead qualification rather than routine searching.
Why AI is faster and more accurate than manual entry?
An AI pipeline processes queries in parallel: in 2–5 seconds, it queries LinkedIn via ProxyCurl, Crunchbase, Clearbit, GitHub, and registries. Manual search takes 3–5 minutes per contact and yields 2–3 fields. AI delivers 20+ fields with a confidence score above 0.85. Thanks to AI-based lead scoring, the sales team focuses on the most promising contacts.
Problems we solve
Incomplete profiles, outdated data, disparate sources. Manual entry is costly both in time and resources. Our pipeline via ProxyCurl finds LinkedIn profiles by email, extracts experience, skills, and certifications. Data is never older than 30 days—automatic updates. The reconciliation engine resolves conflicts by priority (registries → Clearbit → web).
How the pipeline works?
We use asynchronous asyncio and httpx for parallel requests. This processes requests 5 times faster than sequential traversal. The pipeline consists of independent enrichers—one source does not block others. The pipeline works in 5 steps:
- Get contact from CRM (email, company).
- Query all sources in parallel.
- Merge data by priority (registries > Clearbit > LinkedIn > web).
- Validate and compute confidence score.
- Write result back to CRM.
LinkedIn enrichment via ProxyCurl
import httpx class LinkedInEnricher: def __init__(self, api_key: str): self.api_key = api_key self.base_url = "https://nubela.co/proxycurl/api" async def enrich(self, email: str, company: str) -> dict: async with httpx.AsyncClient() as client: # Search profile by email response = await client.get( f"{self.base_url}/linkedin/profile/resolve/email", params={"email": email}, headers={"Authorization": f"Bearer {self.api_key}"} ) if response.status_code != 200: return {} profile_url = response.json().get('linkedin_profile_url') if not profile_url: return {} # Get full profile profile_response = await client.get( f"{self.base_url}/v2/linkedin", params={"url": profile_url, "skills": "include"}, headers={"Authorization": f"Bearer {self.api_key}"} ) return profile_response.json() AI-powered tech stack extraction
class TechStackExtractor: def __init__(self): self.llm = Anthropic() async def extract_from_website(self, domain: str) -> list[str]: """Extract tech stack from company website via AI""" # Collect content from website job_postings = await self._scrape_job_postings(domain) about_page = await self._scrape_page(f"https://{domain}/about") combined_text = ' '.join([about_page] + job_postings[:5]) response = self.llm.messages.create( model="claude-3-5-sonnet", max_tokens=300, messages=[{ "role": "user", "content": f"""Extract technology stack from this company information. Return JSON array of technology names (programming languages, frameworks, cloud platforms, databases). Only include clearly mentioned technologies. Text: {combined_text[:3000]}""" }] ) return json.loads(response.content[0].text) How we ensure the accuracy of enriched data?
Different sources provide contradictory data: Clearbit shows 50 employees, LinkedIn shows 120. Our priority logic resolves conflicts. We use confidence score (no less than 0.85) and cross-validation of fields. Each source has a priority: official registries > Clearbit > LinkedIn > web scraping. As a result, accuracy for basic fields (position, industry, company size) reaches 85–90%.
Data conflicts are resolved via priority reconciliation: official registries > Clearbit > LinkedIn > web scraping. Each field undergoes validation and gets a confidence score. If the score is below 0.85, the data is discarded.
def reconcile_company_info(sources: list[dict]) -> dict: """Merge company info from multiple sources""" reconciled = {} # Source priority: official registries > Clearbit > Web scraping priority_order = ['company_registry', 'clearbit', 'linkedin', 'web_scraping'] for field in ['employee_count', 'founded_year', 'industry', 'headquarters']: for source_name in priority_order: source = next((s for s in sources if s.get('source') == source_name), None) if source and field in source: reconciled[field] = source[field] break return reconciled Typical result: enrichment of 80–90% of CRM contacts within 2–5 seconds per record.
Process and timelines
The project includes the following stages:
- Audit of current CRM: identify missing fields and duplicates.
- Pipeline design: select sources, configure API keys, agree on data format.
- Implementation from scratch or integration with existing infrastructure (Python, FastAPI, asyncio).
- Testing on 1000+ records: check accuracy and latency.
- Deployment on your servers or in the cloud (AWS/GCP).
- API and integration documentation.
- Team training on dashboard usage.
- 3 months of support with quality guarantee.
| Stage | Duration | Result |
|---|---|---|
| Analysis and agreement | 3–5 days | Technical specification with sources and metrics |
| Pipeline prototype | 5–10 days | MVP with 2 sources |
| Full integration | 10–20 days | Pipeline with 5+ sources |
| Testing and refinement | 5–7 days | Accuracy report |
| Deployment and documentation | 3–5 days | Working endpoint + Confluence |
Total timeline—from 4 to 8 weeks depending on the number of sources and reconciliation complexity. Cost is calculated individually after the audit.
What is included in the work
- Architectural documentation of the pipeline.
- Pipeline code with integration of 5+ sources.
- Ready-made connectors for HubSpot, Salesforce, Bitrix24, AmoCRM.
- REST API for batch enrichment.
- Monitoring dashboard (latency, accuracy, coverage).
- Team training (2 sessions of 2 hours).
- 3 months of support with SLA response time.
Typical enrichment mistakes and how we avoid them
- Dependency on a single source—we use a fallback chain and timeouts.
- Outdated API tokens—we monitor quotas and proxy requests through key rotation.
- Incorrect deduplication—we apply fuzzy matching on company names and emails.
- Data leakage—all data is transmitted via TLS, tokens are stored in Vault.
Conclusion
We have been operating for over 5 years—more than 50 projects in data enrichment for fintech, retail, and SaaS. We use stacks: PyTorch, LangChain, PostgreSQL, Redis. We support Python 3.11+. In practice, our solution saves clients up to 80% of time by reducing manual entry. Order an audit of your CRM—we will analyze the current state and propose a turnkey architecture. Get a consultation from an implementation engineer.