ICP matching
Reads your ideal-customer profile and finds companies and people that fit: by industry, size, geography, tech stack, and signals like hiring or funding.
Give it your ideal-customer profile. It finds matching companies and people, enriches them with firmographic and contact data, scores them for fit, and hands a clean, ready-to-work list to your sales team, or straight to first-touch outreach.
Builds, enriches and scores lists on its own. Nothing is sent to a prospect without a person or an approved outreach agent. How autonomy works
A lead generation agent turns an ideal-customer profile (ICP) into a working pipeline. It searches public sources and data providers for companies and contacts that match your criteria, enriches each record (size, industry, tech stack, role, verified email), scores them for fit, deduplicates against your CRM, and writes the result to your CRM or a sheet. Optionally it hands the best-fit leads to a sales outreach agent for personalised first-touch. Operational tier, running on Workers AI in your own Cloudflare account. Typical output: 200–1,000 qualified, enriched leads per week with verified contact data.
theagency47 · Updated June 2026| Job-to-be-done | Turn an ICP definition into a deduplicated, enriched, scored list of target accounts and contacts |
| Tier | Operational (departmental flow) |
| Underlying model | Workers AI models in your own Cloudflare account, routed per task |
| Trigger | Scheduled weekly run + on-demand for a new campaign or segment |
| Inputs | ICP definition, exclusion list, existing CRM (for dedup), data-provider credentials |
| Tools available | Web search, data providers (Apollo / Clearbit / ZoomInfo), email verification, CRM read+write |
| Autonomous decisions | Fit scoring, dedup matching, enrichment source selection, list segmentation |
| Escalation rules | Ambiguous fit → flag for human review · Low data confidence → marked, not sent · Provider rate-limit → pause & notify |
| KPIs measured | Leads sourced, enrichment coverage %, email-verification rate, fit-score distribution, cost per qualified lead |
| Eval suite | 20 test cases (fit scoring, dedup accuracy, enrichment correctness). Must pass 90%+ of our internal eval before go-live. |
Reads your ideal-customer profile and finds companies and people that fit: by industry, size, geography, tech stack, and signals like hiring or funding.
Fills each record with firmographics, role, seniority, and a verified business email. No more half-empty rows.
Scores every lead against your criteria so reps work the best-fit accounts first instead of top-of-list.
Checks every lead against your CRM so you never pay to re-source or re-contact someone already in the pipeline.
Writes clean records straight to HubSpot, Salesforce, or a sheet: segmented and tagged by campaign.
Best-fit leads can flow straight to a sales outreach agent for personalised first-touch, closing the gap between list and pipeline.
Bought lists are static, stale, and undeduplicated. The agent sources continuously against your live ICP, verifies emails at source, scores for fit, and removes anyone already in your CRM, so reps work fresh, relevant accounts, not a decaying spreadsheet.
Yes. It sources only business contact data with a legitimate-interest basis under GDPR Article 6(1)(f), respects do-not-contact lists, and a DPA is executed before deployment. It does not scrape personal/consumer data.
Yes, it writes to your CRM or sequencing platform, or hands best-fit leads directly to a sales outreach agent for personalised first-touch.
Two cost lines: data-provider fees (your existing Apollo/Clearbit plan) and model API usage (typically €40–€200/month at this volume). Build is delivered as a Spark engagement.
Related: Meet Nova (outreach) →
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