Client Overview
- Type of AI company: ML infrastructure platform offering modular observability and deployment tooling for AI/LLM pipelines
- Stage: Seed
- Buyers: CTOs, platform leads, ML ops teams inside mid-market to enterprise tech companies
- Revenue motion: Founder-led with hybrid PLG/sales intent, but unclear execution
Role & Duration
Role: Fractional GTM Architect + Revenue System Designer
Duration: 8 weeks (initial scope), retained through rollout
The challenge
The founders built what most infra buyers say they want — deep observability, flexible deployment, easy integration with major LLM stacks.
But two years in, the company was trapped in pilot purgatory.
Demos were technical masterclasses, but deals rarely moved beyond “interesting.”
- They had dozens of high-quality conversations — but no velocity.
- Product spoke to engineers; buyers with budget couldn’t see the win.
- Sales assets? A Notion doc, a Figma slide, and a 45-minute founder demo.
- Positioning was stuck: too technical for innovation teams, too vague for infra buyers.
They weren’t short on demand.
They were short on clarity — and a growth system that matched how AI infra actually gets bought.
Strategic & operational approach
1. Rebuilt positioning from “what it is” to “why it wins”
The original narrative was feature-first: latency numbers, connectors, and “runs anywhere.”
We rebuilt around pain and proof:
- Spoke directly to infra leads managing runaway inference costs
- Positioned product as a control plane for LLM ops — not another observability tool
- Created a buyer map: differentiated messaging for CTOs (control + roadmap risk), MLOps (workflow fit), and product execs (risk vs. spend ROI)
Outcome: we moved from demoing features to selling a before/after state — built trust with real operators, not just curious peers.
2. Designed a modular offer that reduces GTM drag
To exit the demo loop, we engineered an offer strategy that removed buyer friction:
- Entry-level “Infra Audit” — scoped engagements with clear ROI and zero risk
- Custom deployment workshops to drive urgency without needing a dedicated sales team
- Tiered rollouts with usage-based pricing that mapped to real infra triggers (e.g. volume spikes, model shifts)
Outcome: faster closes, less ghosting, and a buying journey that let prospects self-segment into “test,” “expand,” or “commit.”
3. Built the trust-led sales layer (without a sales team)
We crafted executive-ready sales assets that didn’t just tell — they closed:
- 1-page outcome briefs per vertical (fintech, healthtech, devtools)
- Performance ROI calculator with customizable assumptions
- Objection-killer docs: security proof points, integration maps, model performance impact
And rebuilt the demo flow into a three-part buyer journey:
- Qualify with pre-call primer
- Anchor with narrative-backed use case
- Close with tailored rollout path and clear next step
Outcome: conversion moved from 8% to 27%. And founders spent half the time selling.
4. Shipped outbound + nurture system tailored to technical buyers
No fluff. No “warm-up sequences.” Just signal-rich touchpoints:
- Cold outbound targeting ML leads based on model velocity, hiring patterns, and tool stack signals
- Dedicated landing layers with buyer-specific copy and asset bundles
- Email nurtures that deconstructed buyer risks, not just product features
We ran a 10-sequence outbound test. Result? 42% reply rate, 19% booked, 3 converted in 45 days — without SDRs.
5. Instrumented the ops stack for signal, not vanity
We didn’t “track leads.” We tracked revenue triggers:
- Which buyers engaged with performance calculators → prioritized for follow-up
- Which demo variants had the highest conversion rate by role
- Which accounts showed model instability → triggered audit pitch
Every touchpoint fed into a minimal, no-bloat GTM dashboard: live pipeline view + deal-stage velocity.
Outcome: the system became founder-extendable — not founder-dependent.
Results & business impact
- Closed first 4 enterprise accounts in 8 weeks
- Increased demo-to-close rate from 8% → 27%
- Reduced founder time spent on selling by 60%
- Built a $480K pipeline with zero SDRs, zero paid channels
- First 5-figure deal closed 18 days post-initial outbound touch
The system is now running in-market — feeding learnings back into product, informing roadmap, and compounding trust with every deal.
This wasn’t growth theatre.
It was revenue architecture that performs.
Why working with me was the advantage
AI infra isn’t sold with landing pages and lead magnets.
It’s sold through narrative precision, buyer calibration, and systems thinking that reflects the complexity of what you’ve built.
Here’s what most growth “playbooks” miss:
- Your product doesn’t need mass adoption — it needs the right 50 accounts
- Your buyer isn’t confused — they’re unconvinced
- Your problem isn’t awareness — it’s translation
I don’t do tactics.
I build growth infrastructure that matches the maturity, depth, and ambition of what you’ve actually built — and where you need it to go.
If you’re an AI founder or C-level operator who’s tired of chasing traction with duct-taped GTM — I’ll build the system that flips the switch from demo → revenue.
And I don’t stop until it performs.