Enrich every contact in your CRM automatically
The problem
Walk into any sales org's CRM and you will find the same picture: 20-40% of records have a name and an email and nothing else. Salespeople either spend 30 minutes per lead doing the research themselves, or — more commonly — they skip the leads where the research is too thin and revenue quietly disappears.
Enrichment vendors exist (Apollo, ZoomInfo, Lusha) but each has gaps — particularly for Israeli companies, where the major US-centric providers have weak coverage. You typically need to chain 2-3 providers to get usable density, and that chaining gets expensive at volume.
What is genuinely new is the ability to chain providers + LLM reasoning + web research dynamically, per record, only filling the gaps each tool actually has. The cost per fully enriched record drops sharply and the data is good enough to drive routing decisions automatically.
How Link AI solves it
Link's enrichment pipeline runs on every new CRM record (and re-runs on stale ones, on a schedule). For each record it identifies the missing fields, chooses the cheapest reliable source, and fills them. Sources are routed intelligently — for an Israeli company, the pipeline knows to skip ZoomInfo and lean on Apollo + targeted web research.
Beyond firmographics, we enrich decision-maker contacts (with intent signals where available), tech stack, recent funding or hiring news, and a one-paragraph 'why this company matters now' brief written by an LLM.
Output writes back to your CRM as standard fields, so existing automations (routing, scoring, sequence enrollment) pick up the enriched data immediately. No new tool to learn.
Operational impact
Cost per fully enriched record drops 60–80%
Compared to running a single premium enrichment vendor on every record, the chained-provider + LLM approach delivers the same field density at a fraction of the price — and significantly better coverage for the Israeli market.
What this looks like in practice
A growth-stage Israeli SaaS company
Was paying $1,800/month for an enrichment tool with poor Israel coverage, then paying SDRs to manually research the 40% of leads the tool missed. After switching to the chained pipeline, manual research time dropped to near zero and the monthly tool spend was roughly halved.
A B2B services firm
Used the pipeline to enrich a 30,000-contact CRM that had been accumulating partial records for years. Routing and scoring rules — which had been effectively useless on the underfilled data — became reliable enough to drive automated AE assignment.
A recruiting agency
Pointed the pipeline at every new candidate record to pull current company, tenure, recent role moves, and LinkedIn. Recruiter time spent on initial research dropped from 15+ minutes per candidate to under 2 minutes.
Frequently asked
- Which CRMs do you write back to?
- HubSpot, Salesforce, Pipedrive, Monday, Zoho. For others we can write back via API or to a Google Sheet that feeds your existing automations.
- How fresh is the data?
- Firmographic data is refreshed on a schedule you choose (typically every 90 days). Decision-maker contacts are re-checked when the next sequence enrollment fires, so you do not call a number that has changed.
- What about GDPR / Israeli privacy law?
- We use only legally collected sources and respect deletion requests. We can also operate in a 'minimal data' mode that pulls only the fields you have a legitimate-interest basis for.
- Can we add custom fields?
- Yes. Any structured field you can describe (e.g. 'do they have a security team?', 'what e-commerce platform do they use?') the LLM enrichment can attempt to fill, with a confidence score per field.
- Does this work for B2C?
- Partially — the firmographic side is B2B-only. For B2C we can enrich behavioral data from your own sources (orders, sessions) but not personal demographic data from external sources.
Related
Written by
Ori Tabachnik
Founder, Link AI
Ori is the founder of Link AI. He works hands-on with Israeli SMBs deploying Hebrew AI voice agents and cold-outreach systems, and writes about what actually moves operational metrics in production.