We build targeted prospect lists and ICPs by pooling 20+ data providers and our own scrapers, then research, enrich and double-verify every record so your outreach reaches the right people with the right context.
Every record is double-verified before it reaches a campaign, and weak-fit accounts are filtered out. Where a single provider finds 100 contacts, our pooled, verified approach typically surfaces 150–170.
Yes. We map your ideal customer profile — including pain points and buying signals — and research accounts against it, so the list reflects who actually fits, not just who matches a firmographic filter.
GTM motion · The list under everything
We build the accounts and people behind your pipeline, then research, enrich and double verify every record, so your outreach reaches the right person with the right context. Clean inputs in, fewer wasted touches out.
Why it matters
The list decides who you reach. One wrong title, one stale email or one missed job change burns a send, a sequence and a rep's hour, and you never see it happen. Most teams blame the copy. The problem started earlier, in the data.
Data quality is invisible when it is good and devastating when it is not. You only notice a bad list after you have sent to it: in the bounce rate, in the reply rate, in the spam complaints that quietly clip your sender score. Prevention is cheaper than recovery.
Clean inputs in. Fewer wasted touches out.
How to work with us
The engine underneath
We don't rely on one provider. We pool 20+ data sources and our own scrapers, cross-reference them, then double verify what's left. You get more real contacts, not more bounces.
See how the methodology worksSame input. 50 to 70% more real contacts, double verified.
Pooled, not single-sourced
We pay for the tools so you don't have to stack them. Your list is the best of all of them, cross-checked and verified.
Nine of the 20+ sources we pool. Plus our own scrapers, cross-referenced and double verified.
The process
Every batch follows the same sequence. The order is not optional: each step protects the ones that follow it.
The biggest failure mode in B2B data is starting from step two. Teams jump straight to pulling records from a provider without defining the account universe first. The result is a list of vaguely relevant companies, no signal on any of them, and no shared understanding across the team of who is actually in-scope. We fix the foundation before we build the list.
We start with your ICP, not a keyword in Apollo. We map firmographic fit (size, sector, tech stack, geography), buying-trigger signals, and the competitor set. The output is a documented account definition your whole team agrees on.
We query every major provider in parallel: Apollo, Clay, LinkedIn Sales Navigator, ZoomInfo, Clearbit, and our own scrapers. Each source has blind spots. Pooling them means the contacts a single tool misses still end up in your list.
Each record gets cross-checked across sources. Duplicate contacts are merged, not doubled. The result is a clean, flat list with the best available data for each person, not four half-complete rows for the same contact.
For every record we capture up to 7 variables: verified email, direct dial where available, LinkedIn URL, job title, seniority, company size, and a personalisation signal (recent news, job posting, funding, or tech change). That signal is what makes a cold message feel like a warm one.
We run every address through two independent verification layers before delivery. Anything that returns a risky or invalid result is removed. You get a list where every email either sends clean or stays off the list.
What you receive
Most data services hand you a CSV with three columns. We deliver enough context to open a personalised conversation without manual research.
The seven variables are not arbitrary. They map to the specific decisions your SDRs or founders make before sending: which title to address, what context to open with, which CRM field to populate, and whether this contact is the right person or the wrong one at the right company.
Source provenance is something most lists skip entirely. We include it because it tells your team how confident to be in the data and what to do if a contact bounces. If the address came from a single source with known decay rates, your sequence handles it differently than a cross-confirmed triple-verified record.
The signal advantage
The fundamental problem with most B2B contact lists is that they are identical to everyone else's. Your competitor bought the same Apollo export, applied the same title filter, and sent the same message to the same 500 people last Tuesday.
The only way out of that dynamic is a signal: a real, current reason to reach out to this person, at this company, right now. Not a manufactured icebreaker. Not a compliment on their LinkedIn post. An actual business context that makes the timing of your outreach logical rather than arbitrary.
We treat signal capture as a core step in the research process, not an optional enrichment layer. For every record we build, we look for one of five triggers in priority order. If we find one, it goes on the record. If we do not, we document why so your SDR knows exactly what context is missing before they send.
A raise or a deal in the last 90 days is the clearest buying signal in B2B. New money means new problems to solve and a mandate to spend it. We track funding rounds, acqui-hires, and PE portfolio moves.
A new head of revenue, marketing, or operations is typically evaluating the existing stack within 90 days of starting. This is the highest-intent signal for any GTM or operations-adjacent service.
A company hiring for the role your product assists is signalling the problem openly. A job post for a "sales operations analyst" tells you they are scaling a revenue motion. That context belongs in the opening line.
Product launches, award wins, regulatory changes, or published commentary give you a topical opener that is specific to them, not a template your tool generated. We pull and tag the relevant headline.
A newly added CRM, MAP, or infrastructure tool signals an active build-out. If you integrate with or displace a tool they just adopted, the timing matters. We detect stack additions from job posts and public signals.
Where teams go wrong
Most teams blame copy when a campaign underperforms. These are the upstream causes worth fixing first.
Apollo, ZoomInfo, Lusha: each has genuine coverage in certain sectors and near-zero coverage in others. If your ICP happens to land in the gap, your list is thin and you never know it. We found this matters most in UK mid-market and manufacturing, where US-built databases are notoriously sparse.
A list of names and emails is a cold-call list. A list with a current trigger (a funding round, a new VP hire, a live job post) is the raw material for outreach that opens with something real. Without it, your SDRs open with "I hope this finds you well" and so does everyone else.
Unverified lists run at 8 to 15% bounce rates. One campaign at that rate damages your sending domain and triggers spam filters for everything that follows. The cost of verification is negligible. The cost of skipping it compounds every week.
Contacts move jobs every 18 to 24 months on average. A list you built a year ago has meaningful decay. You need automated re-enrichment on your CRM records, not just a clean list at the start. This is why our CRM enrichment offer runs on a schedule, not as a one-time clean-up.
Your ICP is the company. Your persona is the person inside it. Most teams get this backwards and end up with the right person at the wrong company, or the right company with no idea who to contact. We define both before we pull a single record.
Who it is for
Data and research is not a universal need. Most teams have one of three specific problems. If yours is in here, fixing the list upstream is the highest-leverage move before touching copy, sequencing, or anything else.
You are sending your own emails. You have a clear ICP but no time to research 400 contacts manually. We build the list, you send it, and you stop spending hours in Apollo that you could spend on calls. Most founders who engage us on data alone are sending 200 to 600 contacts per month. We scope the universe first so you know exactly how long that runway lasts before you start.
Your reps are bouncing off wrong titles, stale emails, and zero context. They blame copy. The problem is upstream. We give them a cleaner list with a signal on every row so they can open with something real, not a generic opener. In our experience, reps with a signal-enriched list spend less time on manual pre-call research and more time on actual conversations.
Thousands of contacts, most of them thin or stale. You cannot report on pipeline cleanly because you cannot trust the underlying data. We run the clean-up once, then set up automated re-enrichment on a monthly cadence so the CRM does not drift again. The typical outcome: fewer bounced contacts, cleaner reporting, and sequences that actually reach the right person.
Ownership, your call
Both paths produce the same output: a verified, enriched, CRM-ready list your team can action without manual research. The difference is who runs the engine.
Most teams start with managed and move to owned as their GTM motion matures. A few come in wanting full ownership from day one. Neither is wrong. What matters is that the infrastructure exists and compounds, whether we are running it or you are.
Book a 15-minute call. Tell us your ICP and we will show you how many real, verified contacts are actually out there. We will also tell you if your ICP definition is too narrow to support the volume you are planning, before you waste a campaign on it.