Industry - Ai Startups
Unique Go-To-Market Challenges
AI companies face GTM challenges that generic B2B playbooks don't address:
📌 Security Buyers Are the Hardest to Reach
Your buyer isn't a VP Marketing evaluating ad platforms. It's a CTO evaluating inference costs, a Head of ML comparing fine-tuning approaches, or a VP Engineering assessing API reliability. Outbound that leads with "save time" instead of "reduce hallucination rates by 40%" gets deleted. (This mirrors the technical buyer complexity we see with developer tools companies navigating PLG-to-enterprise motions.)
What we build: Outbound sequences segmented by technical role different messaging for the ML engineer champion vs. the VP Engineering economic buyer vs. the CISO security gatekeeper.
📌 Proof-of-Concept Sales Cycles
AI deals rarely close on a demo alone. Buyers need to validate performance on their data, in their environment. Your CRM and pipeline architecture must account for POC stages, technical evaluation periods, and multi-stakeholder sign-off.
What we build: CRM architecture with POC-specific pipeline stages, automated follow-up triggers during evaluation periods, and stakeholder mapping that tracks champion engagement.
📌 The "AI Fatigue" Problem
Buyers are drowning in AI pitches. Every cold email claims to "use AI to transform" something. Breaking through requires specificity concrete benchmarks, named use cases, and proof that you solve a real problem, not a hypothetical one.
What we build: AI-personalized outbound (via Claude API in Clay) that references the prospect's specific technical stack, recent engineering blog posts, or open-source contributions demonstrating genuine relevance.
WHAT WE BUILD
02 / Deep Technical Profiling
⚙️ Enrichment Waterfall for Technical Buyers
Standard enrichment misses technical buyers. We build Clay enrichment waterfalls optimized for the roles that matter in AI purchasing decisions.
What's different: We enrich for GitHub profiles, published papers, conference talks, and technical blog posts not just job titles. This powers personalization that technical buyers actually respond to.
03 / Stakeholder-Aligned Outreach
🧩 Multi-Threaded Outbound Sequences
AI deals are won by threading multiple stakeholders simultaneously. We build sequences that reach the technical champion, the economic buyer, and the procurement gatekeeper each with messaging calibrated to their evaluation criteria.
04 / Structured Deal Management
📊 CRM & Pipeline Architecture
We deploy HubSpot or Salesforce architecture purpose-built for AI sales cycles:
POC tracking: Stage progression from initial demo → data sharing → POC deployment → results review → contract
Technical validation fields: Custom properties for model performance, integration requirements, and security review status
Multi-stakeholder engagement scoring: Track champion engagement alongside economic buyer activity
05 / Market Authority Building
🌐 AI Search Visibility (AEO/GEO)
Your prospects ask ChatGPT and Perplexity "What's the best AI tool for [your category]?" We build entity authority so your brand appears in those answers.
Case Study: AI Content SaaS
The challenge: Series A AI company, $1.2M ARR, every customer from founder relationships. Zero repeatable outbound.
WHAT WE BUILT
Signal-based ICP engine using Clay + LinkedIn Sales Navigator + Factors.ai
32 sending domains with Smartlead for deliverability at scale
AI-personalized outbound referencing each prospect's recent LinkedIn content
Results in 10 weeks:
Qualified meetings/month: Before was 0-1, and after increased to 22.
Cold outbound reply rate: Before was ~50K, and after was scaled to 500K.
Inbox placement rate: Before was ~1%, and after improved 6.2%.
Pipeline generated: Before was $0, and $740K.
Cost per qualified meeting: Before was Not tracked, and after ~$410.
Read the full case study
Measured Against Industry Performance
AI Startup GTM Benchmarks
Based on our engagements with AI-native companies:
Cold outbound reply rate
The metric of Cold outbound reply rate has an Industry Average of 1.5–3%, while the MB Client Average is 5.5–7%.
Cost per qualified meeting
The metric of Cost per qualified meeting has an Industry Average of $800–$2,000, while the MB Client Average is ~$410.
Time to first meetings
The metric of Time to first meetings has an Industry Average of 4–6 months, while the MB Client Average is 10 weeks.
Enrichment data accuracy
The metric of Enrichment data accuracy has an Industry Average of 60–70%, while the MB Client Average is 98%.
Accounts researched/week
The metric of Accounts researched per week has an Industry Average of 10–20 (manual), while the MB Client Average is 150+ (automated).
Flexible Engagement Models
Choose the GTM engagement model that fits your stage—from launching outbound infrastructure to ongoing revenue operations and performance-based growth.
01 / Package
GTM Launchpad
It includes ICP definition, Clay enrichment build, email infrastructure, and first outbound sequences with Project-based Investment.
02 / Package
Growth Ops Retainer
It includes full-stack GTM management, including enrichment, CRM, outbound, AEO, and ongoing optimization with Monthly Retainer Investment.
03 / Package
Performance Partnership
It includes a base fee plus variable compensation for every qualified meeting generated with Hybrid Investment.
Other Industries We Serve
An overview of tailored GTM and sales scaling strategies provided for specialized sectors, including AI Startups, SaaS, and Cybersecurity.
GTM Engineering — How we build outbound infrastructure
Automate Outbound — Replace manual prospecting with systems
Series A Solutions — Stage-specific GTM for post-PMF startups
All Industries — GTM engineering across verticals
All Case Studies — See more client outcomes
FAQ
Frequently
Asked Questions
Have questions? Our FAQ section has you covered with quick answers to the most common inquiries.
What makes GTM for AI startups different from general B2B?
Do you work with pre-revenue AI startups?
How do you source prospects for AI companies?