Many Roads
Use case
Editorial photograph for Outbound prospecting

Outbound prospecting

B2B sales teams have always had to choose between volume and quality of outbound. A bigger list means more sends, less time on each, lower conversion per send. A smaller list is the opposite. AI changes the math: the list can stay big, every account gets researched, and the openings that go out are personalized to the businesses that actually fit.

Engagement
From a cold list to qualified outbound

We built a sourcing pipeline for a B2B engagement that needed to find specific kinds of businesses at scale. The flow: a large dump of candidate sites becomes the input; AI filters down to the ones matching the criteria; crawlers visit each survivor to verify what AI inferred; AI re-qualifies the verified set with the richer evidence; personalized openings go to the survivors. The volume-of-leads game becomes the right-leads game.

What we can build here

Capabilities

AI filter on a large candidate list

Start with everything: every site, every domain, every business that could conceivably fit. AI applies your criteria across the whole list at once: industry, size, stack, public signals (are they hiring for the right roles? are they running the right tools?). What survives is a list that's still large but no longer noisy.

Crawler verification

AI's filter is fast but inferential; it reads what's said about a site, not the site itself. Crawlers then visit each survivor, check that the business is live and active, confirm the signals AI saw, and discard the false positives. The list shrinks; what's left is verified.

AI re-qualification with full context

Crawlers bring back richer evidence than the first pass had access to: actual site content, team page, product line, customer signals. AI re-runs qualification with this evidence (does this account actually fit, enterprise opening or startup opening?) and assigns a fit score plus a recommended approach.

Personalized openings at scale

The qualified set gets opening messages personalized to each account's actual context: what they do, what their stack looks like, what their recent signals suggest. Not “Dear {firstname}”, but a paragraph that references something specific about *this* business. Personalization that used to cost a researcher's hour per account, in seconds.

Questions

FAQ

Will the prospect spot that this is AI-generated?
Increasingly likely, and that's fine if the message is genuinely relevant. The reason hand-written outbound used to win was relevance; AI gets you relevance at volume. The reason mass outbound used to lose was generic content; AI doesn't have to be generic. We optimize for relevance, not for hiding AI's role.
Does this work with our existing CRM?
Yes. Qualified accounts and contact details land in your CRM (Salesforce, HubSpot, whatever you use) with the AI's reasoning attached: which criteria they passed, what the personalized opening was, what the recommended next step is. Reps work in their existing tools; the pipeline is plumbing.
What about GDPR, CAN-SPAM, and other compliance rules?
We design with the compliance regime your team is targeting in mind: opt-out handling, data retention, jurisdiction-specific consent. The AI's job is to find the right accounts and draft the right openings; compliance is engineered into the pipeline, not bolted on.
Also relevant in
Process automation