This 80-person company builds ML pipeline infrastructure for enterprises still running legacy data science stacks. Average deal size was $80K+, with 6-9 month sales cycles. Their single SDR had produced 3 meetings in 6 months. Conference-generated leads converted at 2%. They needed a warm path to CTOs and VPs of Engineering at enterprises who were evaluating migration from legacy ML frameworks but not responding to vendor outreach.
QUALIFIED PIPELINE
$400K
From 16 enterprise meetings booked in 12 weeks
MEETINGS BOOKED
16
With CTOs and VPs of Engineering at companies with 1000+ employees
DEALS IN EVALUATION
4
Active proof-of-concept evaluations running with engineering teams
What this company was facing
Enterprise CTOs receive dozens of vendor pitches weekly and have trained themselves to ignore them. The company’s SDR hire had produced 3 meetings in 6 months at a fully loaded cost of $8K+ per meeting. Conference attendance generated leads but only 2% converted to a first meeting. The product required technical depth to sell, and the first meeting needed to be with someone who understood ML infrastructure, not a business buyer.
1 SDR producing 3 meetings in 6 months
$0 pipeline from outbound efforts
Conference leads converting at 2%
$8K+ fully loaded cost per meeting from SDR
$400K qualified pipeline from 16 enterprise meetings
4 deals in active technical evaluation
First meeting booked in 14 days
5x more meetings per quarter than the SDR produced in 6 months
WEEKS 1-3
Identified enterprises running legacy ML frameworks (TensorFlow 1.x, custom Spark pipelines, on-premise GPU clusters) through job postings for ML engineers mentioning legacy stack migration, public technical blog posts from engineering teams, and conference talk submissions about infrastructure modernization. Cross-referenced with Fluum’s opted-in network to find warm paths to engineering leadership.
WEEKS 4-8
Each message referenced the prospect’s specific technical stack and migration signals. Outreach positioned around the engineering team’s published challenges, not product features. Messages were reviewed by a technical strategist to ensure credibility with CTO-level readers. Targeting prioritized companies with active ML hiring indicating infrastructure investment.
WEEKS 9-12
Meetings handed to the founding team with pre-loaded technical context, reducing first-call discovery time by 60%. 4 deals entered proof-of-concept evaluation. Signal agents identified a second wave of prospects triggered by cloud cost optimization initiatives at enterprises spending $500K+ annually on ML compute.
“Our SDR spent 6 months trying to reach CTOs through cold outreach. Fluum booked 16 meetings in 12 weeks. The difference was that every introduction came with context about our prospect’s actual tech stack. CTOs responded because the outreach was relevant to their specific migration challenges, not a generic pitch.”
VP Sales, AI infrastructure company, 80 employees
Enterprise CTOs respond to relevance, not persistence. Cold outreach from vendors fails because it lacks technical context. Fluum’s approach identified enterprises with active ML infrastructure migration signals and facilitated introductions that referenced the prospect’s specific technical challenges. The result was $400K in qualified pipeline from 16 meetings, at a fraction of the cost of the SDR-driven approach.
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