1
Discover
We scan thousands of public sources across the web for signals that indicate buying intent.
Process
A pipeline your team can audit.
Every stage is designed to make the output more relevant, more explainable, and easier to route downstream.
2
Detect Signals
Our AI identifies relevant events like hiring, funding, expansion, new products and more.
3
Enrich & Verify
We find the right contacts and enrich them with verified data and supporting evidence.
4
Score & Explain
Every lead is scored and explained so you know why it is a good opportunity.
5
Deliver
Export your lead pack to CSV or XLSX and hand it to your team to start conversations.
Scoring Model
Ranked for action, not just enrichment.
ICP Fit
We rank based on the shape of the company, not a generic category label.
- Segment fit
- Geo coverage
- Commercial relevance
Buying Intent
Signals are weighted by what they imply commercially right now.
- Hiring motion
- Budget events
- Launch pressure
Contactability
We prefer lead packs that can be actioned instead of researched again by sales.
- Role match
- Verified contact
- Reachability
Evidence Quality
Every row is easier to trust when the source trail is visible.
- Why-now note
- Source link
- Freshness date
Delivery
Clean handoff without losing context.
Exports are built for real workflows, whether the next step is analyst review, founder outreach, or CRM import.
1
Brief the run
Set the ICP, target roles, triggers, exclusions, and export expectations before sourcing starts.
2
Review evidence
Check ranked accounts, why-now notes, and source references before final handoff or import.
3
Export and route
Send the pack to sales, RevOps, or a CRM import queue with enough context to preserve trust.
FAQ
Common workflow questions.
Does every run include contact verification?
The default lead pack structure expects verified buyer data and source-backed evidence fields, so the output is usable the moment it lands with the team.
Can RevOps review before import?
Yes. Many teams use a reviewed queue before CRM creation. The workflow is designed to keep evidence fields intact in that review stage.
Do you support founder-led and SDR-led motions?
Yes. The same research engine works for both. The difference is usually brief quality, routing logic, and cadence rather than the data model itself.