How Barie generates a list of 50 mid-market SaaS companies that recently raised Series B — with security fit scoring — and exports them directly to HubSpot

Use case walkthrough | Sales & Lead Generation | 6 min read

Barie queries live funding databases, tech stack intelligence, and company profile sources simultaneously. It identifies mid-market SaaS companies that raised Series B in the last 90 days, scores each for security product fit based on team size and technology signals, and exports the structured prospect records directly into HubSpot via Connector — no CSV, no copy-paste, no tab-switching.

Why building Series B prospect lists manually consumes an entire SDR week for 50 mediocre records

An SDR at a cybersecurity SaaS company spends four hours on Crunchbase filtering for US-based SaaS companies that raised Series B in the last 90 days. She gets 200 results. She then spends another three hours manually visiting each company website to assess company size, tech stack, and whether security is a plausible pain point. By the time she has 50 qualified records built out in HubSpot with employee count, funding amount, recent news, and a fit score, she has spent the better part of two working days on research that should have been a 20-minute prompt.

The manual version also produces a list frozen at the moment of research. The Series B announcement Barie finds today reflects the actual current funding status. The employee count pulled from LinkedIn via Apollo.io reflects this week’s headcount, not a figure from a 6-month-old dataset. The tech stack signal from Firecrawl reflects what the company’s job listings and website are advertising right now, not last quarter’s enrichment run.

💡

Barie exports the 50-record prospect list directly into HubSpot via Connector — no intermediate file, no manual import: Each company becomes a HubSpot Company record with funding amount, employee count, funding date, tech stack signals, fit score, and source URL fields populated. The records are tagged with the campaign source and assigned to the correct sequence queue. The SDR opens HubSpot to 50 enriched, scored records ready for outreach.

Your prompt

Task prompt

“Generate a list of 50 mid-market SaaS companies in the US that recently raised Series B — likely to need our security product. Export to HubSpot.”

Connectors Activated

Step 1: Connectors activated — each sourcing the specific intelligence type this task requires

Barie Research Stack · Live Intelligence

Parallel execution

📈 Crunchbase

Queries Crunchbase for US-based SaaS companies that completed Series B funding rounds in the last 90 days. Filters by company size range (50 to 500 employees) and US headquarters. Returns company name, funding amount, funding date, investor list, and company category. Also retrieves the company’s most recent funding history to distinguish first-time Series B from follow-on rounds.

Live funding data · 90 days

🕷️ Firecrawl

Crawls each identified company’s website and careers page for technology stack signals — job listings mentioning AWS, Azure, GCP, Kubernetes, Terraform, or SOC 2 requirements indicate cloud-native infrastructure and potential security product fit. Also extracts current hiring volume from the careers page as a growth velocity signal. High current hiring + recent Series B + cloud-native stack = highest fit score.

Tech stack · hiring signals

👤 Apollo.io

Retrieves current employee count and verified company LinkedIn profiles for each identified company. Also surfaces the top security, IT, and engineering decision-maker titles at each company — CISO, VP Engineering, Head of IT — with their names and LinkedIn URLs for outreach sequencing. Employee count is used in the fit score calculation as a proxy for security infrastructure complexity.

Employee count · decision-makers

🌐 Web Research

Retrieves recent press coverage for each company to add context: product launches since the funding round, executive hires, customer announcements, and any security incidents or compliance announcements. Recent news is used to personalize outreach and validate that the company is in an active growth phase where security infrastructure investment typically follows.

Step 2: The structured output — every finding sourced, every record actionable

Prospect companies identified
and scored

Day funding window applied

Fit signals scored per company

HubSpot records created,
enriched, and tagged

Company and funding

Raised

Employees

Fit

Stack signals

Velosync AI – SaaS automation

Series B – March 2026 – Austin, TX

$44M

280

High

AWS, Terraform, SOC 2 req in 3 job listings

Clarifio Analytics – RevOps SaaS

Series B – February 2026 – NYC

$28M

190

High

Azure, Kubernetes, ISO 27001 mentioned

Proxim Platform – HR Tech SaaS

Series B – January 2026 – Chicago, IL

$35M

210

Med

GCP, hiring security engineer now

🎯

The fit score formula uses three live signals: company size (Series B mid-market: 100 to 500 employees), cloud-native tech stack signals from Firecrawl job listing analysis, and security-related keyword presence on the company website or in job descriptions. High fit = all three signals present. Each company’s fit score is visible as a custom field in the HubSpot record alongside the source URL for verification.

Delivered to Your Tools

Step 3: Output delivered directly to your sales and GTM workflow tools

🎯 HubSpot

50 Company records created with funding amount, employee count, fit score, stack signals, and decision-maker name fields populated.

📋 Apollo.io

Contact records created for the top security and engineering decision-maker at each company, linked to the company record.

📊 Google Sheets

Prospect spreadsheet with all 50 companies, full data, and sortable fit score column for SDR prioritization.

📓 Notion

Prospect research brief with the screening criteria, fit scoring logic, and top 10 high-priority accounts highlighted.

💬 Slack

SDR team notification with the top 10 highest-fit prospects, their funding details, and HubSpot record links.

📧 Gmail

Sales leadership summary email with the prospect list attached and a breakdown of the fit score distribution.

🗂️ Asana

Outreach tasks created for each of the top 20 high-fit companies, with target decision-maker and suggested sequence assigned.

📄 CSV Export

Full 50-company dataset as a structured CSV with all fields for import into any other CRM or outreach tool.

The Verdict

An SDR who spends two days manually building a Series B prospect list delivers 50 records frozen at the time of research, with no consistent enrichment, and then spends another four hours importing them into HubSpot. Barie retrieves live funding data, tech stack signals, and decision-maker names simultaneously, scores each company for product fit using three signal dimensions, and exports all 50 enriched records directly into HubSpot as Company objects with correct field mapping. The SDR opens HubSpot to a scored, enriched list ready for sequence enrollment. The two days became one prompt.

Barie features used in this task

Feature

ChatGPT

Clay

Barie

Live Crunchbase Funding Query — Series B announcements from the last 90 days retrieved at query time, not from a static list or cached dataset

✗

✓

Three-Signal Fit Scoring — Company size, cloud-native stack signals, and security keyword presence scored per company from live source data

✗

✓

Direct HubSpot Export via Connector — 50 Company records created in HubSpot with full field mapping — no CSV, no manual import, no tab-switching

✗

✓

Decision-Maker Surfacing — Apollo.io retrieves current CISO, VP Engineering, and Head of IT names and LinkedIn URLs for each company

✗

✓