Fractional product and technology leadership.
Operating at senior product/tech altitude (CPTO, CPO, VP Product) for AI-native and AI-transitioning B2B SaaS companies. Available on a fractional basis for companies that need senior judgment without a full-time leadership hire.
20+ years scaling B2B SaaS product organizations from $0 to $50M+ ARR across eight verticals. Most recently:
Lexful. Employee #1 and CPTO. Took an AI-native MSP documentation platform from zero to launched product in six months out of Top Down Ventures, on a mission to displace a $100M+ ARR incumbent. Production agentic AI architecture, end-to-end AI economics, full stack from scratch.
Cognota. VP Product & Technology. Led the SMB-to-enterprise pivot, created the LearnOps category in HR-tech, brought in EY, Synchrony Financial, and Delta Airlines as customers, co-led a $5.5M Series A.
Director and VP-level product roles. Elastic Path (composable commerce, MACH Alliance, developer-platform-as-product), Unbounce (AI/ML in martech, 2018), Allocadia ($5M-$20M ARR scaling, RBC and Charles Schwab among financial services customers), QuickMobile (Visa, JPMorgan Chase as enterprise customers), Vision Critical ($10M-$50M ARR, John Deere, Kaiser Permanente).
Currently Entrepreneur in Residence at Top Down Ventures.
I work with founders and senior leadership teams across three engagement types. Rate is discussed in the discovery conversation. Engagement structure is shaped to the specific situation.
Strategic advisory
Half-day or full-day per week, monthly cadence. Founder team that needs senior product/tech judgment as a thinking partner.
Useful for: AI strategy diagnosis, product operating system design, scaling decisions, competitive positioning.
Embedded operating partner
1-3 days per week on a 3-6 month engagement, with defined outcomes. Real product/tech leadership during a transition or a 0-to-1 build.
Useful for: Founding CPTO on a 0-to-1 mission, AI transformation, product organization design, agentic workflow build, fractional CPTO during a transition.
Project work
4-12 weeks with named deliverables. Senior leader to scope and deliver something specific.
Useful for: AI product strategy and roadmap, product operations diagnostic, architecture review, build vs. buy decisions on AI infrastructure.
Product & tech due diligence
Operating partner for VC and PE firms during diligence on B2B SaaS investments, or for portfolio companies post-investment. Independent, fixed-scope assessment of the product and technology org.
Useful for: Pre-investment diligence, portfolio health checks, AI-readiness audits, post-acquisition integration assessment, fractional product/tech seat on a portfolio board.
Most product organizations are not built for the agentic era. They were built for the pre-AI era, and they're patching AI on top of processes that were designed when humans were the only workers shipping decisions.
That doesn't work for long. The product operating system has to be redesigned, not retrofitted. Three principles:
Monitor what matters.
Product operations without measurement is theater. The right metrics aren't velocity, story points, or shipping cadence. They're decision quality, time-to-decision, and the gap between customer signal and customer outcome. Most organizations don't measure these because they're harder. That doesn't make them less important.
Optimize where AI handles execution.
Some operational work is pattern-matchable and high-volume. Research synthesis, requirements drafting, status reporting, customer signal aggregation, competitive monitoring. AI handles this work well. Humans operating in these roles is mostly wasted senior judgment. The redesign is to extract this work from the human path and let AI run it on a defined schedule, with humans reviewing exceptions.
Concentrate human work on judgment that compounds.
Customer conversations, hard tradeoff calls, strategic bets under uncertainty, team coaching, performance feedback. None of this is pattern-matchable. All of it is where senior product judgment compounds over time. The redesign is to free humans from execution work so they can do more of this.
The methodology is documented across dacard.ai. Three frameworks for diagnosing where AI augments, where humans focus, and how product operations get built for the agentic era. It's the lens I bring to fractional engagements, but it works just as well as a self-applied diagnostic.
Available for fractional roles and contracted missions. 0-to-1 builds, AI transformations, due-diligence engagements, fractional CPTO seats. Sometimes combined with a Dacard.ai implementation, often not.
Grab time directly, or send a note with the shape of what you're working on.
Or skim the practice overview: pitch deck →