$2M+
New Revenue
18 Mo
Timeframe
0
SDRs Hired
The Challenge: The Signal vs. Noise Problem
A specialized IT Services provider focused on Cloud Migration and Kubernetes infrastructure faced a common B2B challenge: they knew exactly who they could help—CTOs of mid-sized Fintech companies scaling their infrastructure—but reaching them was nearly impossible.
The "spray and pray" approach of cold emailing 1,000 prospects a week yielded less than a 1% open rate. These technical leaders were inundated with generic sales pitches.
The Roadblock
The company considered hiring a team of 3-4 Sales Development Representatives (SDRs) to manually research and personalize outreach.
- High Cost: Estimated $350k/year in salaries.
- Slow Ramp-up: 3-6 months training time.
- Manual Error: Inconsistent data entry and research.
The Solution: An Automated Prospecting Architecture
Unizol rethought the entire prospecting funnel. Instead of humans doing the research, we built a Clay + n8n engine that acted as a tireless, 24/7 digital investigator. The goal wasn't just to send email; it was to send relevant context.
1. Deep Signal Intelligence (Clay)
We configured Clay to continuously scan alternative data sources for buying intent signals, rather than just demographics.
- Job Postings: Identifying companies hiring for "Kubernetes Engineers" or "AWS Architects".
- Tech Stack Verification: Payer APIs verified if companies were running legacy infrastructure.
- Funding News: Triggering outreach sequences immediately after a Series B/C announcement.
"It's not cold outreach anymore. It feels like a warm introduction because the agent knows exactly what they are working on. Open rates jumped from 1% to 65% in the first month."
Hyper-Personalized Generation (LLMs)
Once a prospect was flagged, the AI didn't just merge a first name. It drafted entire paragraphs referencing the specific context found.
// Generated Email Snippet
"I saw you just hired a DevOps Lead last week—scaling your K8s clusters can often be a headache during that new integration..."
This level of specificity made the outreach feel like a warm introduction from a peer, rather than a cold sales pitch.
Orchestration & Safety Guidelines
We built complex workflow logic in n8n to ensure zero embarrassment. If a prospect replied on LinkedIn, the email sequence paused automatically to prevent "crossed wires." If a prospect was marked as a customer in Salesforce, they were automatically excluded from all prospecting pools.
Data Quality Waterfall
Data quality is the fuel for any AI engine. We implemented a "waterfall" enrichment strategy. If Source A didn't have the CTO's direct mobile number, the system automatically queried Source B, then Source C. This redundancy ensured 95%+ data coverage for their "Golden List" of accounts.
The Outcome: $2M Revenue
Over an 18-month period, this automated engine generated over 400 qualified discovery calls. With a focused sales team consuming specifically vetted, high-intent leads, the conversion rate skyrocketed.
The company booked over $2 Million in new contract value directly attributed to leads sourced and warmed up by the automated engine.
They effectively scaled their revenue without scaling their headcount, achieving the "non-linear growth" that defines modern AI-first enterprises.
Scale your revenue, not your headcount.
Let us build your automated GTM engine today.
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