Breaking into a new B2B market used to feel ambitious and mildly terrifying — weeks of research, Excel gymnastics, and a lot of coffee. Today? Not so dramatic. AI and automation take care of the grunt work, leaving you time for what actually matters: strategy and human-to-human outreach.

Below: a step-by-step playbook (with short, real-feeling examples) that shows how the process changed — and how you can apply it right away.


1. Using AI to Map Niches and Match Ideal Buyers

Old way: Google lists, trade show participant spreadsheets, Yellow Pages, LinkedIn — the kind of manual digging that eats a week and your patience.

Now: prompt Perplexity for market maps and micro-niches, pull the linked sources, and export lists into Google Sheets via simple automation (Google Sheets with app script, N8N, python...?). The same intel — in hours, not days.

Short case: Ukrainian soybean grower → Europe. Using Perplexity we mapped soy end-products by country (Germany: soy milk & meat analogues; France: tofu & yogurt; Netherlands: animal feed; Italy: vegan cheese), then collected manufacturers/buyers per micro-niche. Matching included soy specs (protein content, non-GMO) and geographic proximity to cut logistics costs. Result: a segmented buyer list in a day instead of a week.

2. Relevancy scoring — cut the noise

Finding companies is one thing. Finding the right companies is another.

  • Old: Python scrapers looking for keywords like Java or HIPAA.
  • Now: AI microservices scan sites, reports, news, job boards and other public pages, outputting a relevancy score (e.g. 9/10 — legacy .NET, very relevant) and a short explanation.
Short case: An outsourcing provider in the US used AI scoring to drop ~60% of irrelevant leads. The AI didn’t just say yes/no — it explained why, so sales focused where it actually mattered.

3. Finding decision-makers — less digging, more connecting

Companies are fine — but who signs the check?

  • Old: LinkedIn Sales Navigator + CrunchBase + paid databases (and a growing subscription bill).
  • Now: microservices + aggregator APIs (Apify, targeted agents) that pull likely decision maker profiles from public sources; if gaps remain, selectively use pay-as-you-go services.
Short case: for an equipment manufacturer entering the Norwegian market, we used the Apify + N8N agent to collect contacts for selected companies. Clay was only used in situations where the agent could not find contacts.

4. Purchase readiness — signals that actually mean something

“This company fits” is not the same as “this company is buying now.” Look for signals:

  • Job postings (they are hiring employees to solve a problem that you have already learned to solve for dozens of clients).
  • News (investment rounds = budget, layoffs = pause).
  • Tender portals, regional announcements, industry forums and even Reddit threads.
Short case: The client wanted to sell his online store (cannabis seeds, a legal business, since the seeds do not contain opiates). He couldn't find a buyer. On Reddit, we contacted an American entrepreneur who was actively looking for just such a business. It was a perfect match.
Or here's another example: Company Y writes about layoffs in the news — immediately to the “deferred” pile. As a result, managers only work with those who have a real need.

5. Outreach & communications — human first, automated second

With the right list, you can stop blasting and start starting conversations.

  • Old: emails to segments with spintax like {Hello|Hi|Hey} or I wanted to {reach out|connect|get in touch} about… .
  • Now: AI crafts personalized outreach using the dossier you’ve built — tailored messages for each role/context, then automation sends them from warmed email inboxes and active LinkedIn accounts.
Short case: An engineering consultancy in DACH moved from templates to a soft-touch funnel: like → comment → tailored message. The specific "hook" was different every time: maybe a shout-out to a great conference paper, maybe some killer stats from a company news story, or even a line pulled directly from a hiring post. The AI did the heavy lifting, formulating the copy to sound totally natural, but a human was still there to polish the final rough spots. Result: >30% of contacts replied and half became qualified leads.
Quick note: personalization isn’t inserting a name — it’s demonstrating context awareness (they raised funds, they’re opening a new hub, they mentioned a challenge in an article). That’s what warms a lead.

From manual research to a LEGO-like system

Market entry isn’t a minefield anymore — it’s a LEGO set: pick the microservices, plug in AI and automation, and build a repeatable system.

  • Research time drops from days to hours.
  • Irrelevant companies get filtered automatically.
  • Decision makers surface faster.
  • Purchase readiness is verified by real signals.
  • Communications become personalized and alive.

Heads up: two years ago we were still doing the digging for clients by hand. Today we do that digging and we set up the tools — then hand those tools to the client.

No subscriptions. No vendor lock-in. We generate MQLs for you while building the system that stays with you.

Interested? We also maintain a set of free microservices you can try.