Strategy, build, and measure.
I help companies figure out if their AI idea will actually work — whether it's a new product, a new business line, or an internal capability that could transform operations. Then I prove it with a working prototype and quantified evaluation. Not a strategy deck. Not a demo that falls apart on real data. A measured answer you can make an investment decision on.
You believe AI could solve a domain-specific problem — a new product, a new business line, a transformative internal tool. But you’ve seen too many AI demos that don’t survive contact with real data. You need someone who can validate the idea with strategic rigor and a working prototype before you commit real resources.
You hired a vendor, let engineering experiment with LLMs, or built an internal POC that didn’t go anywhere. There’s organizational scar tissue around AI. You need someone credible to assess what went wrong, what’s actually feasible, and what the path forward looks like. The problem usually isn’t the technology — it’s that nobody connected the domain expertise to the technical implementation.
The CEO went to a conference, the board is asking questions, competitors are making claims. Nobody internally can separate signal from noise. You need a strategic assessment from someone who’s actually built and measured these systems — not just advised on them.
Most AI initiatives fail because someone built an impressive system that solves the wrong problem. My methodology inverts the typical approach: the majority of every engagement is spent understanding your problem space — domain knowledge, data landscape, operational reality, market context — before writing a single line of code. Then I build fast, against your real data, and measure whether the AI actually works well enough to matter.
What you get in 2–3 weeksFor product concepts: Market analysis, competitive landscape, go-to-market strategy, and an honest recommendation on whether to invest. For internal capabilities: Build-vs-buy analysis, development plan, capability gap assessment, and a realistic ROI model.
Built against your actual data and documents — not a generic demo.
Turns “it seems pretty good” into specific metrics: faithfulness, accuracy, completeness, hallucination rate. Evidence you can make a decision on.
The person who interviews your domain experts is the same person who builds the prototype. Nothing gets lost in translation between a strategy deck and the engineering.
Every prototype comes with automated evaluation infrastructure and a quantified scorecard. You’ll know exactly how well it works, where it fails, and what that means for your investment.
If the answer is “don’t build this” or “buy the existing vendor solution,” that’s what I’ll tell you. A no-go delivered in two weeks is worth more than a six-month project that reaches the same conclusion.
Building a product to sell and building a capability to use internally are different problems. My assessment adapts — market sizing and go-to-market for product concepts, build-vs-buy and development planning for internal tools. Same rigor, different deliverables.
I’m a product and AI leader with 20+ years building and shipping at companies like eBay, New Relic, and multiple startups. I’ve led engineering teams, launched products to millions of users, and built AI systems in production with LLMs, RAG, knowledge graphs, and evaluation infrastructure.
I started MaxGradient because I kept seeing the same pattern: companies with real AI opportunities burning time and money because nobody did the hard discovery work first. My background spans product strategy, engineering leadership, and hands-on AI development — which means I can have the business conversation with your CEO and the technical conversation with your engineering team in the same week.
If any of the three triggers above sound familiar, I’d like to hear about what you’re working on. Initial scoping conversations are always free — I’ll tell you honestly whether I can help before any engagement starts.
