A product mind with builder's hands.
I do the product thinking and ship the code. I find a problem worth solving, get to the bottom of it, and turn it into a working product you can click, often in a matter of days.
Figure out what to build
Customer interviews, positioning, prioritization, the go/no-go case. The judgment about what's worth building, and why.
Then actually build it
Full-stack and hosted, with real integrations and AI under the hood. Not a slide deck, the thing itself.
Four years doing product. Five years building.
Not new to either. I started as an engineer, moved into product, and never stopped writing code, which is why I can do both halves of the job today.
Engineering came first (Flask apps, automation, a shipped data tool). Then product: analyst, then product manager, across a startup, an Nvidia-backed company, and a Fortune 500. The two have run together ever since, and lately I've fused them, shipping full products solo.
Six problems. Six working answers.
Mostly built solo in a single continuous sprint, end to end, and live right now. The time it took is right up front. Open any to see the technical workflow.
It tells a company whether ChatGPT and Gemini name them when buyers ask, and scores them red, yellow, or green. Search is moving from Google to AI engines, and whoever the AI names in its answer wins the customer. No one had a way to measure it, so I built one.
The hard callRule-based scoring, not asking an AI to grade. Asking GPT-4 each time is slower, pricier, and answers differently every run. Rules make it free, instant, and identical every time.
▸see the technical workflow
<head> code snippet to fix what's missing.It turns recorded recruiter interviews into automatic coaching and a quality score managers can finally see. The data sits in every call, and nobody was turning it into feedback, so I built the system that does.
The hard callFully automatic, but with a human in the loop. Everything runs itself the second a call ends, yet a person reviews before anything reaches the recruiter, because hiring is too sensitive to fully automate.
▸see the technical workflow
Upload an image, get back a processed, hosted version with a link to share. A clean test of taking something from empty repo to a tool that feels finished, not a coding exercise.
The pointEnd to end, with the polish that says finished. Every loading and failure case handled, live and hosted, code public. Here's the link, go press the button.
▸see the technical workflow
A product teardown of an early-stage AI sales tool: how to position it, and what's broken under the hood. I went at it as a user trying to break it and a product person trying to sharpen it.
The insightIt's a timing advantage, not just another lead tool. Its real edge was telling you when a buyer is ready, but the homepage led with generic "find leads" language, so skimmers filed it as ordinary. I made the case to lead with the timing edge.
▸the product eye, real things a busy team missed
- Gated data leaking out through an export path.
- A login flow reporting success for email addresses that were never registered.
- Data that claimed to be current but didn't match reality, all found by using the product carefully and writing it up clearly.
It connects to a company's Salesforce, scores how ready they are for AI agents, and projects the return on adopting. Salesforce's adoption push wasn't landing; two interviews told me why, and the fix was leading with numbers.
The moveI registered as an authorized Salesforce ISV partner org just to build it. Not a mockup: a real connect-your-account-and-see-your-score tool, built solo from idea to working prototype in five days.
▸see the technical workflow
A founder wanted his competitors' clients so he could pitch them, and was finding them one by one through LinkedIn mutual connections. Painful, slow, and incomplete. I replaced the whole thing with one pipeline: it finds the companies, finds the right decision-maker at each, and writes a tailored cold email, so all he does is press send from his own inbox.
The leapFrom a manual LinkedIn crawl to a one-button workflow. The hard part wasn't any single step, it was chaining scraping, AI structuring, contact enrichment, and email drafting into one flow that ends with a ready-to-send draft sitting in his inbox.
▸see the technical workflow
And I've done product work inside real companies.
A Fortune 500 manufacturer, an AI startup backed by Nvidia, and a software studio building for clients worldwide.
Product strategy at a Fortune 500 manufacturer, on a new vehicle program where hardware and software meet. (Some specifics are confidential, so this stays high level.)
- Leading the business caseA structured argument for whether the company should invest in building a product: market, competition, what customers want, and whether the numbers justify it. for a new product concept: sizing the market, mapping the competition, capturing what customers want, and shaping the value proposition behind a major capital-investment decisionWhether the company commits significant money to develop the product. The kind of call that needs evidence, not a hunch.. Genuine zero-to-one work.
- Acting as the product-side bridge between in-house engineering and an external software partner, turning business needs into clear requirements and closing the gaps between intent and what gets built.
- Partnering on a data product that turns field sensor data into early-warning signals, helping prevent costly downtime across a fleet.
Market and competitive strategy for a $34.5M AI startup working where data centers meet energy.
- Ran market and competitive research across three global regions to find expansion opportunities, fluent in a brand-new market within a week.
- Built the model weighing battery capacity against AI-driven energy savings, feeding the international expansion plan.
- Set early product direction across both software and hardware (energy management and battery integration for data centers).
Full product ownership across a multi-year run, shipping software for clients worldwide.
- Started as Product Analyst: owned research on a product across 57 competitors and cut scope 30% through user research that focused the roadmap.
- Promoted to APM: co-led a 23-person team building eight point-of-sale apps for the Middle East market, wrote 450+ product specsPRDs, BRDs, and MRDs: the documents that define what gets built, why, and for whom. The blueprints engineering works from., coordinated 24 stakeholders, hit 96% on-time delivery and cut defects 20%.
The bridge between my engineering background and product: I started building, then moved to defining what to build.
- First PM hire: set up the team's planning from scratch and improved planning efficiency ~85%, taking two products from zero to one.
- Earlier, as a developer, built and shipped a data-visualization tool (Flask) used by real users, plus scraping and automation pipelines.
Same approach, every time.
I don't write memos about problems. I build the solution and bring it to the table.
Find the real gap
A problem that's genuinely unsolved. The signal is usually already there; most teams just can't see it yet. I find it by talking to real people.
Get deep, fast
Understand it cold. Interviews, public data, fluent in a new field within days. Then choose the one technical decision that makes it work.
Ship the answer
Full-stack, hosted, something you can click. Built fast with AI in the loop, so deciding to shipping is days, not months.
Give me a real problem. I'll bring back a working answer.
A product mind and builder's hands in one person, from figuring out what to build to shipping it. I'd rather show you the thing than describe it.