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Sharpen your product decisions.

Your product leaders have always made calls on too little signal. AI is the first technology that lifts that constraint, by reading what they never had time to read.

Two weeks. Cohorts up to six. Each participant leaves making sharper product calls, with an AI that synthesizes the evidence behind them.

Your engineering team got the AI step change last year. Your product team has not. This is how they catch up.

Or send a note: craig@maxgradient.ai

The bet behind every epic

Surface the signal. Prototype the bet. Make the call.

For twenty years, the product job has been to make the call on incomplete evidence. The data was always there: sales calls, customer interviews, support tickets, competitive moves. No one ever had the time to read all of it, so the call got made on a slice.

AI does not make the decision. It reads the slice you never had time for and tells you what is in it.

Surface the signal. Prototype the bet. Make the call.

What's showing up

Two patterns across most product teams right now.

What changes

Same product leaders. Sharper calls in every meeting.

Before

A director walks into a prioritization meeting with three feature requests, a gut read on which one matters, and an hour of meeting time. The outcome depends on who argues hardest, or longest.

After

A director walks into the same meeting with patterns pulled from 50 sales call transcripts, customer signals tagged by theme, the strongest counter-argument to their own recommendation already on paper, and confidence flagged on each item. The outcome is the right call, not the loudest call. What used to take a week of context-switching now happens before the meeting starts.

Visibility into what your customers are telling you. Continuously.

Think of it as an exoskeleton for product decision-making. The director still does the deciding. The system carries the synthesis weight nobody has time to carry alone.

How it works

Two weeks, nine artifacts, one capstone.

Each participant will complete the 2-week engagement with a working system delivering value and the confidence to extend it going forward.

Week before Days 1–9: Core engagement - 1 hour/day, 1:1 Day 10

The week before kickoff sets the frame: a sponsor call to scope the engagement, discuss possible measurable outcomes, and a cohort introduction that walks the team through what is ahead.

Days 1–9 run on daily 1:1s, anchored to a real use case each participant brings to the engagement. By day seven, the system is being applied to a strategic question relevant to the participant. Day 10 is the capstone demo to the sponsor.

Each participant leaves with an AI that learns and remembers what you teach it, a thought partner who handles the routine work, synthesizes, pressure-tests your work, and helps you back up your claims with evidence trails.

Claude Code and Claude Cowork installation required before engagement begins. See FAQ.

Who it's for

Sponsors fund it. Product leaders own it.

The Sponsor

CPO, VP Product, or CTO with product reporting in.

Your engineering team is shipping faster than ever. The question your board keeps asking is whether they are shipping the right things. The answer to that lives with your product leaders. You do not have time to learn AI yourself. You need someone to install the capability on your team's machines so they can actually use it. Two weeks from kickoff, your directors will demo real product insights to you, pulled from your own data during the engagement.

Every participant works in the same methodology, calibrated to their own data and starting point. Different configurations, same standards. The work your team produces stays legible across the team and defensible in front of the board.

After a two-week engagement, your product leaders will know how to:

  • Surface customer friction and product growth opportunities from data the team already collects. Sales call transcripts, support tickets, customer interviews, competitive research, and product reviews. The team catches friction before it shows up in churn.
  • Validate bets with working prototypes critiqued by AI personas. Each persona represents a different customer segment, reviewing the proposal and flagging where the next iteration should focus. Epics that reach the engineering backlog have already passed multiple customer reviews, not just a PRD walkthrough.
  • Match the cadence engineering now sets. The PRD-roadmap-stakeholder-update sequence collapses from a week of context-switching to a working session. PM stops being the upstream bottleneck.
The Participant

Director of Product, Senior Director, Group PM, or Senior PM with strategic scope.

You are the one running prioritization, synthesizing customer signals, writing strategy docs, defending roadmap decisions. You know AI matters. You have not yet built anything with it that you use on Tuesday. Day one starts with the data you brought. By the end of session one, you have surfaced a pattern in it that nobody else in your org has assembled. By the end of two weeks, you own a working system that handles this kind of synthesis continuously, and you have the practice to keep extending it as your role evolves. It will not replace your judgment. It will make your judgment faster, better-informed, and something you can defend in front of your CEO without being the one in the room who knows least about AI.

About

Craig Calder, Founder.

Craig Calder, Founder of MaxGradient

Craig Calder
Founder, MaxGradient
Portland, Oregon

Twenty years shipping product at every scale, from startups to eBay and New Relic. Committed to the craft of product leadership as it was meant to be practiced. Hands-on with the production AI stack (LLMs, agentic systems, knowledge graphs) to understand the technical implications of product decisions. Builder at heart, focused on business outcomes, not lost in the weeds of technical implementation.

The engagement carries what is working today in the PM community and what is coming next from the teams building the tools.

I publish regularly on how AI is reshaping product leadership.

Medium LinkedIn

The hardest part of AI adoption is not the tools. It is the confidence to use them in front of your peers, your CEO, and your board. The engagement is designed for that. Let's get a call on the calendar.

FAQ

FAQ about an engagement.

What does an engagement actually look like?

Two-week active build, preceded by three short calls the week before kickoff: one with the sponsor to confirm toolset and data sources, one 30-minute KPI call with the sponsor to agree on what we will measurably improve, and one with the full cohort to walk through what the two weeks deliver and place each participant in the Practitioner or Builder track. Craig brings the build plan, the templates, and the integration know-how. The team brings the participants, the data, and one hour per participant per day. Craig meets 1:1 with each cohort member to ensure a customized, hands-on experience that meets each participant where they are. Not a generic classroom-style engagement. Capstone closes with a demo of real product insights.

What tools do participants need?

Claude Cowork and Claude Code desktop apps on each participant's machine.

Two variants: Team ($30/user/month) or Enterprise (custom, starting around $60/user/month). Neither trains on inputs or outputs.

See Claude Team vs Enterprise for IT for the detailed comparison.

Why Claude? Why not OpenAI Codex?

This engagement runs exclusively on Anthropic's Claude platform: Claude Cowork for daily workflow, Claude Code for data connections and automation. The reason for this choice is that Anthropic has built the best suite of tools specifically tailored to business, and the market agrees. As of May 2026, Anthropic has crossed OpenAI in U.S. business adoption (34.4% to 32.3%, per Ramp AI Index, which tracks actual corporate credit card spend across thousands of companies).

What data do we need?

Qualitative data sources, such as sales call transcripts, work as practical building blocks. Customer feedback, support tickets, competitive intelligence, strategy, and OKR documents also work. The day-one anchor is sales call transcripts because every product team has the data, almost no team mines it, and the insights are immediate and tangible. If sales transcripts are unavailable, the day-one anchor is scoped to whichever qualitative source is closest at hand.

What happens after two weeks?

The participants own the system. Each runs it on their own machine, maintains their own memory layer, and extends it as their role evolves. Craig provides a maintenance guide and a self-management playbook. Optional retainer for ongoing tuning.

Can we start with one person instead of a cohort?

Let's discuss. The capstone is richer with a cohort because patterns across participants surface during the demo discussion. With a single participant, the capstone is a sponsor-facing demo of that participant's strongest insight. Still valuable. Different shape.

What does this cost?

Project-priced, scaled by cohort size. Three tiers:

  • 2 participants (intensive format): $20K to $25K total.
  • 3 to 4 participants: $30K to $40K total.
  • 5 to 6 participants (full cohort): $40K to $60K total.

Per-participant cost decreases as the cohort grows because the fixed overhead (pre-engagement discovery, capstone facilitation, build-plan setup) is shared. Two participants is the minimum; six is the maximum.

What you're paying for: pre-engagement discovery, KPI agreement, nine days of 1:1 daily implementation sessions per participant, day-ten capstone with the sponsor, and a working AI decision-support system each participant operates after the engagement ends. Not a slide deck.

Exact pricing settles on the scoping call. Three variables shape the number: cohort size, tool stack complexity, and data source integration depth.

This is an implementation engagement. The team runs the system after handover, and the capability compounds across every quarter that follows.

Talk

Pick a time.

30 minutes. Brass tacks, not a sales pitch.

For sponsors (CPO, VP Product, CTO) weighing whether to fund this for their team, and for senior product leaders who have already decided this is the program. We'll cover who the participants are, what qualitative data they can bring on day one, and what success looks like.

Or send a note
Thanks. I'll get back to you within 24 hours.