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AI Consulting for GTM

AI strategy that ships — not another slide deck

You've evaluated 20 AI tools. You're running 6. None are moving measurable pipeline. We help digitally enabled companies leaders turn AI from pilot purgatory into production infrastructure — strategy, governance, and embedded implementation, all tied to revenue.

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The Problem

Where AI adoption breaks

Every GTM team we talk to has an AI problem that sounds the same: a top rep built a lead scoring prompt in Claude that works brilliantly — for them. Marketing runs Apollo + Clay. Ops is piloting 11x. Legal is nervous about data going into ChatGPT. Leadership wants a strategy. No one can point to the pipeline AI has moved.

The gap isn't enthusiasm. It's operational clarity — a framework for which pilots to scale, which to kill, and how to run AI as revenue infrastructure, not a sidecar.

We close that gap. Working AI integrated into your CRM, your sales stack, and your team's daily workflow. Governance that legal signs off on. Measurement that ties to pipeline.

And because we ship what we design — our own stack runs on a custom HubSpot MCP, Claude, and Clay — you get operators who've lived the architecture decisions, not slideware consultants.

Framework

Our AI-for-GTM framework

Adoption roadmap

Pilot-to-production scorecard. Which 6 tools stay, which 14 go. Investment tied to revenue targets, not vendor hype cycles.

Guardrails & policy

Data handling rules, vendor evaluation rubric, prompt injection defenses. Legal and security stop panicking.

Embedded workflows

Working integrations across HubSpot, Clay, Apollo, 11x, Claude, and custom MCPs. Not pilots in isolated sandboxes.

Team capability

Role-specific training. Prompt libraries your reps actually use. Capability transfers, not dependency on us.

What We Deliver

From diagnostic to embedded implementation

AI Readiness Assessment

2-week diagnostic: current tool footprint, spend audit, governance gaps, opportunity map. Output: prioritized 90-day roadmap with ROI estimates.

AI GTM Roadmap

Which pilots scale, which die, which get rebuilt. Budget allocation, ownership, metrics. Quarterly revisits baked in.

Governance Framework

Data policies, vendor evaluation rubric, security standards, approval workflow. Ready for legal sign-off in regulated industries (fintech, healthtech).

Pilot-to-Production Playbook

Structured pilot methodology with kill criteria and scale paths. End zombie pilots.

AI-Native Workflow Build

Working integrations in your stack: Clay enrichment orchestration, Claude-powered lead scoring, HubSpot MCP for sales workflows, 11x for outbound, Apollo signal-driven plays.

Team Enablement

Role-specific training for SDRs, AEs, ops, and leadership. Prompt libraries for your ICP, messaging, and systems. Office hours for the first 90 days.

Our Process

From audit to production

1
Discovery

Audit current AI footprint, tool spend, governance posture, team capability.

2
Design

Roadmap, governance framework, measurement architecture.

3
Build

Implement prioritized workflows in your production stack.

4
Launch

Team training, rollout, measurement baseline, kill criteria for legacy pilots.

5
Optimize

Ongoing refinement, new tool evaluation, governance reviews.

Tools We Operate

We're tool-agnostic. But we have opinions.

We've shipped production AI workflows on every category below. When we recommend a stack, it's because we've run it — not because we read a Gartner report.

Click any category to see the tools
Our internal stack

Checkpoint's own GTM runs on coco ai, Lemlist, HeyReach, Clay, HubSpot, and a whole lot of Claude.

We don't sell theoretical architectures.

We use it.

Mid-engagement: live workshop with a Checkpoint GTM client
Mid-engagement. Real client. Real workflow.
Who This Is For

If AI is in your GTM stack — or you're trying to add it without breaking what works — this is for you.

We work with seed startups running their first Claude workflow, all the way through Series C+ teams operationalizing 20+ AI tools across the funnel. Founder-led, ops-led, marketing-led, sales-led — the role doesn't change the work. The pattern is the same every time: get clarity, get measurement, get shipping.

Any stage — from pre-seed teams piloting their first AI workflow to Series C+ teams operationalizing 20+ tools
Any team shape — founder-led, RevOps-led, marketing-led, sales-led, or CS-led GTM motions
Any AI maturity — you've never shipped an agent, or you're already running 15 of them and can't tell which ones earn their seat
Any industry — including regulated ones (fintech, healthtech, EU companies) where governance has to come before scale
One throughline: you want AI to compound inside your GTM — not fragment it
Frequently Asked Questions

AI consulting questions, answered

What's the difference between your AI Consulting and your AI Automation service?

AI Consulting answers “how should we think about AI across our revenue motion?” — strategy, governance, roadmap. AI Automation is the tactical build (“please automate our lead enrichment routing”). Most engagements start strategic and fund tactical builds downstream. See our AI & Automation page for pure build work.

How much does an AI consulting engagement cost?

AI Readiness Assessments start at €15K for a 2-week diagnostic. Full roadmap + governance + implementation engagements typically run €35K–€90K depending on scope. Ongoing retainers range €8K–€25K/month. We scope every engagement before quoting.

How do you measure ROI on AI initiatives?

We baseline current spend and output before we touch anything, set measurement standards per workflow (pipeline generated, hours saved, data quality lift), and review quarterly against kill criteria. Zombie pilots don't survive us.

Do you work with AI-native startups or only traditional SaaS?

Both. AI-native startups hire us to tighten their internal GTM operations (they're so focused on building AI for their customers that their own sales motion is a mess). Traditional SaaS hires us to adopt AI operationally without the whiplash.

My team is already using 10 AI tools. Do we really need more?

Probably not. Most engagements kill 30–50% of the existing stack. We audit what's actually moving pipeline and consolidate. Tools we can't justify get cut.

How do you handle data privacy and governance?

We design policies before touching production data. Vendor evaluation rubrics cover SOC 2, GDPR, data retention, prompt logging, and sub-processor chains. For regulated industries (fintech, healthtech, EU companies) this is where we start.

Do you build custom AI systems or only implement off-the-shelf tools?

Both. We've built production MCPs (custom HubSpot integration, Salesforce admin tooling), Hermes-based agent architectures, and Clay + Claude orchestration systems. Build vs. buy is a decision we make per workflow, based on your control needs and economics — not a dogma.

Can you integrate with our existing HubSpot setup?

Yes — we're a HubSpot Platinum Partner and have built our own HubSpot MCP layer. Most of our AI work lands directly in HubSpot via workflows, custom properties, and bidirectional data flow with Clay, Apollo, and Cargo. See HubSpot Implementation.

Your GTM team's AI strategy, sorted.

30-minute call. We'll map your current AI footprint, identify your three highest-ROI workflows to scale, and tell you which pilots to kill. No deck, no pitch.

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