How ATAPIC actually works under the hood
This is the part of the product most companies hide behind buzzwords. We're going to be specific about what Tapi does, how he does it, and the choices we're making that we think other lead-intelligence tools get wrong.
The angle
Most "AI for sales" tools give you a list of names and a confidence score with no explanation. You don't know why someone scored an 87, you don't know what data backed it up, and you don't know whether to trust the email next to them. That's the gap we're building against.
The product principle is simple: every match comes with a reason. Tapi doesn't return a person without telling you why he picked them — role fit, company fit, the specific signal that put them on your list — and every contact value he hands you carries a confidence score and a "last checked" stamp so you know what to send to and what to double-check. If a search would return zero, we cascade across multiple sources rather than show you an empty page. If a result is too thin to act on, Tapi asks back instead of guessing.
That sounds obvious. It isn't, in this category. The honest version of what most tools ship is "scrape, score, ship a CSV." We're trying to ship a teammate.
How the lead intelligence stack actually works
Multi-source, with fallback
Tapi pulls candidates from several lead sources in parallel rather than betting on one. Each source has its own strengths (some surface hiring signals, some surface content/intent, some surface verified contact details). When one path comes back thin, we cascade to the next so a real result lands instead of a "no matches" dead end.
Same person, same record. Every time.
If the same person shows up in three different places, you should see them once, not three times. Tapi standardises LinkedIn URLs, emails, and company domains before matching them up, so a person sourced from three different paths lands as a single record with everything those sources knew about them combined. The Chrome extension uses the same matching logic, so when you save a profile from LinkedIn it merges into the record you already have instead of creating a fourth.
Rules first, AI on the edge cases
Grading runs in two passes. A simple rule check on role, title, and company handles the obvious cases — clearly a fit, clearly not, with the reason written next to the verdict. The ambiguous middle ground is the part that actually needs judgement, so that's where Tapi calls the AI with the full context to verify or reject, and the reason gets written back to the record so you can see why. You get speed where speed makes sense and judgement where judgement matters.
Per-field confidence + freshness
Every value Tapi enriches — email, phone, title, company, headline — carries its own confidence score and a "last checked" stamp on the field, not on the whole record. You can sort by what's verified vs inferred and decide what's safe to send to. No more "the record is 80% confident" which actually means nothing.
Outcomes loop back into the model
The leads you accept, reject, and close train Tapi's intent model on what "good" looks like for your team specifically. We boost what your team has historically chosen and penalise what gets bounced. Future searches get sharper without you tuning anything — the system learns from your real outcomes, not from a one-time persona setup.
Hard budgets on every long-running task
Every user-facing task — search, deep dive, enrichment — has a wall-clock cap and a cost cap. Nothing runs forever. If a search hits its budget before completing, you get the partial results with what's known so far rather than a forever-loading screen. We'd rather ship an honest "here's what we found in 90 seconds" than a fake "still working...".
What we don't claim
We don't claim to predict who will buy. We surface signals (hiring activity, role changes, public posts about specific topics) and give you the reasoning. The decision to reach out is still yours.
We don't claim 100% contact-fill rates. Email and phone enrichment uses multiple strategies and sometimes returns empty or low-confidence values. When that happens, you see it — Tapi doesn't fabricate a value to make a number look better.
We don't claim guaranteed inbox placement. Tapi tracks opens, clicks, and replies, but final delivery depends on your sending domain, ISP reputation, and provider setup. We help you do your part. The rest is your infrastructure.
We don't claim live, instant LinkedIn monitoring. Deep dives and signal lookups run on demand or on a schedule. If we ever build "real-time" anything, it'll say so. For now: periodic and honest about it.
Responsible by default
Tapi works with public and business-relevant information. We respect the platforms we read from, we respect the people Tapi surfaces, and we ship the controls — opt-out tokens, unsubscribe footers, per-category notification preferences, contact deletion — that make this a tool teams can use without becoming a problem for the recipients on the other end.
Outbound only works long-term if it's done well. We're trying to build for the operators who already know that and want a tool that makes "done well" the easy path.
Want to put Tapi to work on your pipeline?
Your first search is on us. Open a chat, describe who you want, and Tapi will turn that into a graded shortlist with reasons.