July 12, 2025

No Contacts? No Problem. How to Build a Lead Generation Pipeline from Nothing

#
Media about us
Media about us
Want to see us in action?
Schedule a 30-minute demo
Let's talk

Introduction

B2B sales teams often dream of a pipeline that magically feeds them qualified leads while they focus on closing deals. In reality, building such a pipeline takes strategy and the smart use of tools. Imagine finding potential clients online, pulling in their info automatically, enriching those details with a bit of detective work, and then reaching out at scale – all with minimal manual grunt work. This article breaks down that journey from scraping raw data to automated outreach with AI, in four key stages. Let’s dive into how a modern sales pipeline can turn cold data into warm conversations.

//1. Scraping Potential Client Data from the Web//

The first step is building a list of potential customers. Scraping means extracting publicly available data – like names, emails, phone numbers, company info – from online sources. Think of it as mining the web for leads. Instead of manually copying contacts from websites or social networks, you can use tools and scripts to gather this data in bulk. This isn’t about hacking databases; it’s about pulling info that’s already out there on pages like directories, social profiles, or business listings.

Where to find leads

The key to successful scraping is identifying the right source of data for your target audience. This depends on your niche. Start by asking: Where do my potential customers already show up online? For example, if you're targeting car dealerships you need a reliable, structured source that already lists them. A site like AutoScout24 is a great example. It's a leading vehicle marketplace, and each listing typically includes the dealership’s name, address, website, phone number, and sometimes even a direct contact person. You can filter by country, car brand, or dealership size to narrow your targeting. Once you identify such a source, you can extract data systematically and start building a highly relevant, pre-qualified lead list—no cold guessing, just focused discovery.

When it comes to scraping, you have two main approaches: Build vs. Buy.

  1. Buy (Using Web Scraper APIs): For many users, buying a web scraper API product is a quick and easy way to get started without needing to write code. These products allow you to extract data from websites by simply providing them with the URLs you want to scrape, and they take care of the heavy lifting. These APIs can automate tasks like handling IP rotations, managing rate limits, and parsing data into structured formats. This option is ideal if you're looking for a fast, efficient solution without the complexity of building your own scraping infrastructure. Some popular examples of web scraper APIs include ScraperAPI and DataMiner. They offer user-friendly interfaces and can integrate directly into your existing systems.
  2. Build (Custom Scraping Platform): If you need to scrape data at scale or want full control over your scraping system, building your own scraping platform is the way to go. With this approach, you can fine-tune your scraping logic to extract exactly the data you need and even handle complex tasks like multi-step scraping or filtering out irrelevant content. Building your own platform also means you’re free from any usage restrictions or service limitations that might come with third-party APIs. Curious what that looks like in action? Check out this success story showcasing an enterprise-grade parsing engine in action.
Keep it ethical

Not every site allows scraping, and you should respect privacy guidelines. Focus on publicly available, business-relevant information and avoid anything too personal or protected. The goal is to create an initial prospect list, not to violate trust.

By the end of this stage, you have a raw list of leads – say a few hundred names with contact info. It’s a starting point, but it’s just data. Next, we make that data richer and more useful.

//2. Enriching Data with OSINT and Public Information//

Raw contact data alone only tells you so much. This is where Open-Source Intelligence (OSINT) comes in to enrich your leads with more context. In plain terms, enrichment means adding more useful information about each prospect from publicly available sources. It’s like doing detective work on your leads: the more you know, the better you can approach them.

  • What to enrich: Key details might include the person’s job title, their company size and industry, social media links, recent news about their company, or even if they’ve mentioned needing a solution like yours. For example, for a list of car dealership contacts, you could visit each dealer’s website to find the owner’s name, see how many locations they have, and note what brands they sell.
  • Automating enrichment: Manual research takes time—but most of it can be automated. Instead of googling each lead, you can build scripts or pipelines that scan public sources like company websites, social media, or news mentions. These systems extract job titles, team info, brand affiliations, and other useful signals—turning basic contact data into actionable lead profiles. The goal is to create a structured, enriched dataset at scale, without lifting a finger for each search.

Enriched data transforms your lead list from a cold spreadsheet into something more like a CRM profile. It sets you up to personalize your outreach and prioritize the hottest prospects (e.g. maybe you focus first on those at bigger companies or those who fit your ideal customer profile best). With enriched leads in hand, it’s time to feed them into an outreach system.

//3. Importing and Organizing Leads in Your Outreach Platform//

Now that you have a list of promising leads with rich information, you need a home for that data – usually a CRM (Customer Relationship Management system) or a specialized outreach platform. This stage is about getting your leads into a tool where you can track and manage engagement. In other words, we’re moving from research mode to action mode.But where should this data live? The answer depends on your goals—and choosing between a CRM and a sales engagement (outreach) platform makes all the difference.

CRM systems

If you already have a CRM like Salesforce, that’s the natural place to import your new contacts. Many sales teams use their CRM to organize leads into campaigns or sequences. They’re ideal for storing detailed contact histories, pipeline stages, and integrating with other tools like support or billing systems.

Outreach platforms

Outreach platforms like Outreach.io or Salesloft on the other hand, are built specifically for scalable outbound communication. Their strength lies in automating email sequences, tracking open/reply rates, managing follow-ups, and triggering call tasks. If your goal is to book meetings and qualify leads at scale, an outreach platform is your operational front line.

In practice, many teams use both: CRM for tracking and reporting, outreach platforms for execution. Some tools integrate directly, syncing contact data, engagement history, and status updates between systems automatically.

By the end of this step, your leads are loaded, segmented, and ready to go. Whether you're managing a high-touch deal cycle in a CRM or running automated cadences in an outreach tool, your pipeline is now primed for action.

//4. Outreach at Scale – Emails, AI Call Bots, and More //

Initial outreach is where the magic (and the hard work of the previous steps) starts paying off. The goal here is to contact your leads in a personalized yet automated manner. This typically involves a combination of cold emails and sometimes cold calls or voicemails, increasingly with a boost from AI. The challenge is to do it at scale without sounding like a robot, ironically even when a robot might be helping you.

  • Email automation with AI personalization: Email remains the backbone of B2B outreach—but sending one message at a time doesn’t scale. That’s where automation comes in. Modern outreach platforms let you build sequences—pre-written emails that go out over several days, automatically triggered based on lead behavior. This lets you stay top-of-mind without manual effort. But automation doesn’t mean generic. With enriched lead data, you can personalize each message: referencing a recent product launch, calling out their job title, or tying your value prop to their industry pain points. And now, AI is making that personalization easier. Many platforms integrate GPT-powered features that generate custom intros, suggest subject lines, or even recommend the best send times—turning what used to be hours of manual drafting into minutes.
  • Calls and AI call bots: Phone calls add a human touch, but your human team only has so many hours. This is where AI call bots are emerging as a game-changer. Imagine an AI-driven phone agent that can call leads for you, deliver a short pitch or ask a few qualifying questions, and even book meetings if the prospect is interested – all while sounding nearly human. It’s not sci-fi: startups Bland.ai offer AI sales assistants that handle initial outreach. For instance, an AI call bot might ring up a contact and say, “Hi, I’m calling from XYZ Corp to quickly tell you about [product]. Is now a bad time?” and intelligently handle the responses. These bots use advanced text-to-speech and natural language processing to carry a basic conversation.

Wondering if anyone actually uses it in real life? Here are the results of marketing research based on real customer reviews: Companies experienced time savings of 30–40% in sailing and support processes due to the automation provided by AI solutions.

Want more insights, or even to run your own marketing research at the click of a button? Check out LeadMeld AI, AI-powered tool that helps B2B marketers and sales teams find high-intent leads and insights for ABM — using real client reviews as the data source.

By leveraging email sequences and AI call agents, one salesperson can initiate conversations with dozens or hundreds of prospects a day, something impossible to do manually. This kind of scale is how small teams punch above their weight. However, quantity should not replace quality – the best results come when automation boosts your capacity while your strategy keeps things targeted and meaningful for the recipient.

Looking for data mining?

Let us prepare a FREE technical proposal for you in just 2 days!
Uladzislau Kuzmich
Software Architect | Co-Founder
Want to see us in action?
Schedule a 30-minute demo
Let's talk

More Publications

Automotive
E-commerce
Marketing
How to Choose a Modern Data Scraping Solution
Businesses rely on data scraping for all kinds of insights, whether it’s e-commerce teams watching rival prices, financial firms tracking market swings, retailers checking stock levels, or marketing groups gauging customer sentiment.
July 23, 2025
10

Success Stories

Data Scraping
Data Integration
AI Web Applications
Multi-Channel Auto Aggregator Scraping Platform
More

1M

Scrapping requests 
per day

1M

Active offers

200k

Daily users

Data Science
Data Integration
AI Web Applications
AI-Powered Fine-Grained Image Classification
More

70%

faster onboarding of new car listing sources

30%

boost in analytics precision

40%

reduction in model training time

Data Science
MVP Development for Startups
Payment Gateway Platform
More

2M

transactions per month

98%

fraud detection accuracy

x3

faster payment provider integration

Data Scraping
Data Integration
AI Web Applications
Money Transfer System
More

150k

transactions per month

25%

manual work decrease

97%

fraud detection accuracy

Got a project
in mind?

Let's talk!
Uploading...
fileuploaded.jpg
Upload failed. Max size for files is 10 MB.
Send
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.