Projects across AI systems, product strategy, fundraising, and go-to-market — from prototype to production.
A community innovation space was managing event inquiries, member communications, proposal generation, and facility scheduling across email, spreadsheets, and group texts. Staff spent more time routing information than serving members.
We designed and ran a multi-agent AI system built on the open-source OpenClaw platform. It handled inquiry intake, workflow routing and proposal generation end to end. Alfred orchestrated. A Monitor agent ran local Qwen inference. A Drafting agent generated proposals on Claude Sonnet. The system integrated across WhatsApp, email, and internal tools, with human oversight at every decision point.
It ran on a Mac Mini M4, with Google Drive as the system of record. Three agents handled the work, and staff kept the tools they already used. We retired it in early August 2026: keeping it running took more tinkering and maintenance than it returned, and newer off-the-shelf agents had become better at the same jobs. The biggest lesson: agents need tightly defined specs before they need more capability.
A local esports company had traction but couldn't translate it into an investor narrative that landed. The deck was feature-heavy and market-light. The story of why this mattered — and why now — wasn't coming through.
We rebuilt the pitch deck and investor narrative from the ground up — restructuring the story around market capture, customer value, and defensibility. Sharpened the competitive positioning, clarified the business model, and coached the founding team through the presentation.
Supported the team through a seed raise.
A fintech company wanted to spin out a new product that would give small businesses clarity into their financial health — while simultaneously serving as a channel into the company's broader product ecosystem.
We helped define the product from scratch: what it was, who it served, how it fit into the existing portfolio, and what the go-to-market path looked like. The result was a product definition sharp enough for engineering to build from and leadership to align behind.
An AI messaging startup needed to move faster. The core interaction patterns weren't tight enough and the team was iterating in circles — building features without validating the fundamental user experience.
We prototyped and tested the platform's core flows, making targeted adjustments to accelerate time-to-market and tighten the feedback loop between building and learning. Focused on getting the right thing in front of users faster, not adding more features.
A regulated AI onboarding company was looking to integrate into banks and credit unions — a domain where compliance constraints are real and a misstep can kill a deal.
We helped define the product requirements with the precision that regulated environments demand — navigating compliance, data handling, and institutional procurement while preserving the user experience that made the product compelling in the first place.
A children's financial literacy app had a working product but an unclear path to market. The value proposition for parents was muddled, and the institutional partnership angle — the real growth lever — wasn't articulated.
We advised on go-to-market positioning and product-specific feedback — sharpening the value proposition for both parents and institutional partners, and clarifying which channel would drive sustainable growth.
A future-of-work company had a vision but no product. The founding team needed to go from concept to working software — fast enough to validate the idea and start learning from real users.
We helped build out the minimum viable product — from requirements through working software — with a focus on shipping something testable, not something perfect.
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