Most AI in products is theater, not value.
A 4-week engagement for product teams ready to build AI that solves the right problems
Why most launches fall flat
Adding AI to a product isn’t a technology decision. It’s a product strategy decision. And strategy has to come before the build.
An executive saw a competitor’s chatbot. Someone asked why you aren’t doing the same thing. A third-party tool got integrated, a feature got shipped, and now it sits there — technically present, rarely used, not solving anything specific for the customer who encounters it.
The problem isn’t AI. The problem is the order of operations. Most teams choose the tool before they define the problem. They build what’s available rather than what’s needed. And they launch without a narrative that explains to customers, to sales, or to the market why the AI in their product actually matters.
Built for product teams who want AI that earns its place in the product.
Professional Services Platforms
B2B SaaS & Tech
If your organization has shipped AI features that aren’t landing, has AI on the roadmap without a customer-problem-first strategy behind it, or is preparing to launch a product where AI is part of the value proposition — and you need a foundation built on real evidence, not internal assumptions — this engagement was designed for you.
Consumer Products
One sprint. Four weeks. An AI strategy built to survive contact with customers.
Discovery & Scoping
Customer & Product Diagnosis
Collaborative AI Opportunity Design
GTM Playbook & Handoff
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60-minute discovery call to assess fit and define the AI context — what’s been built, what’s planned, and what the team is trying to achieve
Client intake questionnaire covering current AI features, product positioning, and existing customer research
Statement of work and engagement welcome packet
Scoping fee: $500–750, credited toward the sprint
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Structured interviews with product, customer success, and sales to surface what customers are actually experiencing — where they struggle, where they succeed, and where AI has or hasn’t helped
AI feature audit: review of what’s been built or planned, how it was scoped, whether it maps to a real customer problem, and where adoption has or hasn’t followed
Competitive AI scan: how peers and alternatives are using AI in their products, how they position it to customers, and where genuine differentiation exists
Customer research synthesis: existing interviews, support tickets, churn data, and NPS comments reviewed through a pain-point-first lens
Observe a customer call, a demo, or a CS handoff to hear how AI features are described and received in practice
Mid-point check-in to validate findings before moving to design
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A structured half-day working session with your product, CS, and sales team — using the diagnostic findings as the foundation, not a blank whiteboard
Customer pain-to-AI opportunity mapping: match real friction points to realistic AI interventions, both customer-facing and backend, evaluated for business impact and technical feasibility
Build vs. buy vs. partner evaluation framework: for each prioritized opportunity, define the right approach, relevant vendors or APIs, and the tradeoffs the team needs to own
Prioritization into three tiers: High Conviction (build now), Worth Watching (build next), and Not Yet (deprioritize with rationale)
The team leaves the session aligned on what to build, why, and in what order — before anyone writes a spec or evaluates a vendor
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GTM-ready AI narrative: how to talk about your AI to customers, to prospects, and to the market — grounded in the customer problems it actually solves, not the features it contains
Sequenced implementation roadmap: what to build first, what to build next, and what success looks like at each stage — with enough detail for your engineering team to plan against it
Sales and CS enablement starter: updated language for demos and discovery calls, objection handling for AI skeptics, and talking points for customer conversations about your AI direction
Live 60-minute readout with Q&A for leadership, product, sales, and CS — designed to close alignment gaps across teams, not just present findings to one audience
Complete deliverable package delivered within 48 hours of readout
Deliverables
Executive Summary
2 pages, board-ready
Customer Pain-to-AI Opportunity Map
An understanding of your customer’s current pain points and AI opportunities
AI Feature Audit with Gap Analysis
Where your AI features meet customer expectations and where they fall short.
Build vs. Buy vs. Partner Evaluation by Opportunity
Guidance on when to build in-house vs. seek external solutions
GTM-Ready AI Narrative: Positioning & Proof Points
How to talk about AI features with your buyer in their language to gain trust and increase adoption
Sequenced Implementation Roadmap with Success Metrics
A plan to build out your AI feature roadmap and make course correction decisions.
KEEP THE MOMENTUM
The sprint gives you the strategy. The retainer turns it into results.
Build Track
What we do together:
Ongoing vendor and API evaluation as options emerge or change
Feature scoping and product requirements review from a customer-outcome-first lens
Build vs. buy decision support as engineering constraints surface
Weekly or biweekly synchronous sessions to unblock decisions in real time
Best when:
You’ve got a prioritized roadmap and are about to enter active development. You want expert judgment available throughout the build — not just at the start.
Launch Track
What we do together:
AI messaging QA — review of how the narrative holds up in real sales and marketing materials
Sales and CS coaching on the AI value proposition through launch
Early customer feedback synthesis and positioning refinement
Weekly synchronous sessions through launch window; biweekly thereafter
Best when:
A launch is coming in 30–90 days and you want the AI narrative stress-tested through the whole go-to-market motion — not just written once and handed off.
All advisory tracks: $6,000–$8,000 per month. Includes regular synchronous sessions with me directly — not account management, not a team handoff. Minimum 3-month commitment. Sprint clients receive preferred scheduling and onboarding within one week of sprint completion.
Growth Track
What we do together:
Usage data review and AI feature adoption analysis
Roadmap refinement based on what customers are actually doing post-launch
Competitive monitoring and differentiation refreshes as the market moves
Biweekly synchronous sessions with async support between
Best when:
You’ve launched AI features and have real usage data. You want a strategic partner to help interpret it, adjust the roadmap, and stay ahead of a market that is moving fast.
Transparent pricing. Fixed fee. No surprises.
Start-Ups & SMB
$7,500-$10,000
Pre-Series A or 20-75 person companies
Fewer stakeholders, tighter roadmap scope
50% due at signing · 50% due on delivery · Scoping fee ($500–750) credited toward your Sprint
Most Common
Growth Stage
$10,000-$15,000
75-200 person companies.
Multiple product lines, broader competitive context
Mid-Market
$18,000-$25,000
200+ person companies.
More stakeholders, board-level AI narrative required