Case Study

How LaviaAI Built a Production-Grade GTM Machine in 90 Days

LaviaAI had fragmented tooling and unstable inbox placement. We rebuilt their outbound infrastructure so they could scale safely, protect reputation, and own the system end-to-end.

Company

LaviaAI

Marketing • Boston

Timeline

14-28 Days

From kickoff to first production systems live

Revenue Impact

4x Growth

$2M → $8M ARR with better margins

The Challenge

LaviaAI had the outbound fundamentals but was stuck in fragmentation. They were using 7+ different tools to manage:

  • × Data quality issues: Duplicates, bad enrichment, inconsistent ICP rules across systems
  • × Deliverability problems: 65% inbox placement, sender reputation suffering, no real-time monitoring
  • × Automation friction: No unified routing, manual CRM work, SDRs spending 20+ hours/week on ops
  • × Zero visibility: Couldn't track which campaigns were actually working, bottlenecks everywhere
  • × Vendor lock-in anxiety: Renting everything, no fallback if a tool broke

The Real Cost: They were losing ~$150K/month to bad data, undelivered emails, and SDR busywork. Revenue had plateaued. They needed to scale, but the infrastructure couldn't support it.

Our Approach

Phase 1: Assessment & Architecture (Week 1-2)

We did a 360° audit: data quality assessment, deliverability diagnostics, automation workflow mapping, visibility gaps.

  • Found 35% duplicate rate in their list and 40K bad emails
  • Identified sender reputation issues: 3 of 5 domains were in spam traps
  • Mapped manual workflows: 150+ hours/month on data hygiene and routing
  • Built custom implementation roadmap aligned with their growth goals

Phase 2: Infrastructure Build (Week 3-8)

We architected 5 interconnected layers:

Clean Data Foundation
  • • Built ICP qualification engine
  • • Set up real-time deduplication
  • • Automated enrichment with 3rd-party APIs
Delivery Infrastructure
  • • 5-domain rotation with warmup
  • • DKIM/SPF/DMARC setup
  • • Real-time inbox monitoring
Smart Automation
  • • Dynamic sequence triggers
  • • AI-powered personalization
  • • Automatic CRM syncing
Monitoring & Control
  • • Real-time dashboards
  • • Performance alerts
  • • Custom analytics

Phase 3: Testing & Handoff (Week 8-12)

Load testing, failover testing, team training, and full knowledge transfer.

  • SDRs trained on new workflows (2-hour sessions, then hands-on support)
  • Ops team given full admin access and documentation
  • Ran beta campaigns with 10K emails to validate deliverability
  • All systems live and processing 50K+ emails/day by Go-Live

The Results

Data Quality

65% → 98%

List accuracy. Duplicates eliminated, bad emails removed, real-time validation in place.

Email Deliverability

65% → 95%

Inbox placement rate. Multi-domain rotation, reputation management, real-time monitoring.

Operational Efficiency

150h → 25h

Manual hours per month. Automation eliminated routine work, SDRs focused on selling.

Revenue Growth

$2M → $8M

ARR expansion. Better list quality, higher conversion, lower CAC, predictable growth.

Secondary Wins

  • Team morale: SDRs aren't drowning in busywork. They're selling again.
  • Visibility: For the first time, they can see what's actually working. Data-driven decisions, not guesses.
  • Vendor freedom: They own their infrastructure. Can swap tools without losing data or functionality.
  • Cost predictability: No more per-email surprises. Costs scale linearly with revenue.

Key Learnings

1. Data Quality is the Foundation

Every dollar spent fixing list quality returned 10x in deliverability and conversion gains. Bad data costs way more than good data.

2. Ownership Prevents Bottlenecks

When your GTM stack is fully owned, you can optimize fast. No vendor waiting. No feature requests. Just improvements that ship.

3. Deliverability Is a System, Not a Setting

Inbox placement depends on domain hygiene, sending discipline, list quality, and monitoring. When these work together, deliverability becomes predictable.

Before vs. After

Before

  • ×7+ disconnected tools
  • ×65% inbox placement
  • ×150+ hours/month manual ops
  • ×No end-to-end visibility

After

  • Unified owned architecture
  • 95% inbox placement
  • Automated workflows + alerts
  • Dashboards for every stage

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