Automated prospecting that finds your next customers while you sleep

The manual prospecting tax nobody accounts for

Every SDR quietly loses hours a week to work that is not selling: pulling exports, tabbing between LinkedIn and a data provider, guessing whether a company is the right size, and copying names into a spreadsheet. It is repetitive, it is error-prone, and it is the first thing that gets skipped when a quota is looming. The result is a target list that is half guesswork and already out of date.

The deeper problem is timing. The best moment to reach a prospect is right after something changes: they raise a round, hire a VP of the function you sell to, open a new office, or start using a tool that pairs with yours. A human checking a static list once a month catches almost none of those moments. By the time anyone notices, three competitors have already emailed.

Automated prospecting removes the tax and fixes the timing. Instead of a person periodically hunting, a system watches your market every day, applies your definition of a good fit consistently, and surfaces accounts the moment they match. Your reps stop building lists and start working the ones that already sit in front of them.

What we build, and how it identifies a fit

We start by turning your fuzzy sense of a good customer into a precise, testable ICP: firmographics like industry, headcount, and geography; technographics like the tools they run; and the trigger events that mean now is the time. That definition becomes the ruleset the system enforces on every candidate, so a good fit means the same thing on Monday as it did last quarter.

From there the engine runs a loop. It pulls candidate companies and contacts from the sources that actually hold them, then an LLM reads each company's site, recent news, and public signals the way a sharp SDR would, and writes a short, structured verdict: does this fit, why, and what is the hook. Every account gets a fit score and a one-line reason, so your team sees not just who to contact but why they surfaced now. This is the research layer that feeds directly into our list building, enrichment, and personalized outreach work, so the same account flows from discovery to a sent message without anyone re-keying it.

Because it reads and reasons rather than just filters on checkboxes, the system catches fits a keyword search misses, a company that never lists an industry tag but clearly serves your market, and rejects false positives a filter would wave through, like a holding company or a competitor.

• Firmographic match: industry, size, revenue band, location, and structure
• Technographic signals: the software and infrastructure a company already runs
• Trigger events: funding rounds, key hires, expansion, product launches, and leadership changes
• Intent and activity signals: hiring patterns, public posts, and site changes that hint at a live need
• LLM fit verdict: a scored, reasoned yes or no with the specific hook for each account
• Deduplication against your CRM so you never surface an account a rep already owns

A day in the life of the prospecting engine

Picture the loop running on a Tuesday. Overnight, the system pulls newly funded companies in your target industries, cross-checks them against your ICP rules, and drops the ones that are too small or clearly out of market. For the survivors, it researches each company, identifies the two or three people who actually make the buying decision, and checks them against your CRM so nothing already in a rep's pipeline resurfaces.

By the time your team logs in, a fresh, ranked queue is waiting: named accounts, the right contacts, a fit score, and a plain-English reason each one is worth a message today. There is no export, no spreadsheet cleanup, no debate about whether a company is the right size. The judgment calls that used to eat an afternoon were made overnight, consistently, against your rules.

We build this on the same stack we use across every lead machine: n8n or Make for orchestration, an LLM for the research and scoring, verified data providers for the contacts, and your CRM as the system of record. It runs on your tools, so you own it. If we ever part ways, the workflows and the logic stay with you.

What it takes to build, and what you keep

A first prospecting engine is usually live in two to three weeks. The first few days go into sharpening your ICP and choosing the signals that predict a real deal, because a system pointed at a vague target just produces bad accounts faster. Then we build the loop, run it against your real market, and tune it: you review the first batches, tell us which accounts are right and which are noise, and we adjust the rules and scoring until the queue is one your reps trust.

You keep everything. The account list, the research, and the scores land in your CRM under your ownership, not rented from us behind a login you lose access to later. There is no per-lead markup; data providers are billed to you at cost. Because we are an outcome-first studio, a meaningful part of our fee sits behind results, so we are on the hook for whether the queue actually turns into pipeline, not just whether it fills.

Prospecting is the top of the funnel, so it is the natural first build. Once accounts flow in reliably, the same records move straight into enrichment and outreach, which is where a researched list turns into booked meetings.

• Week one: define the ICP, pick predictive signals, and connect your data sources and CRM
• Week two to three: build the research loop, run it live, and tune scoring on your feedback
• Ongoing: the engine runs daily and we adjust rules as your market and results shift
• You own the accounts, research, scores, and workflows, all inside your own CRM
• Data provider costs pass through at cost, with no per-lead markup

Results, ROI, and when not to automate this

The clearest win is time: a working prospecting engine hands each rep back the ten or more hours a week they were spending on list building, and those hours go to conversations instead. The second win is coverage, the system watches your whole market continuously, so trigger-based accounts reach you while the signal is still fresh instead of a month late. The third is consistency, every account is judged against the same rules, so your pipeline stops depending on which rep built the list and how motivated they were that afternoon.

It is not right for every business, and we will say so before you spend a euro. If your total addressable market is a few dozen named accounts, you do not need a research engine; a rep who knows every one of them by heart will do better by hand. If your ICP is genuinely unclear or your offer has not closed a few deals yet, automation just finds the wrong companies more efficiently, so fix the targeting and the offer first. Prospecting multiplies a clear definition of a good customer; it cannot invent one.

Where it fits, though, it changes the shape of the day. Instead of reps rationing their energy between hunting and selling, the hunting runs itself and they spend their hours on the part only a human can do: the conversation.