From idea to working proof — when there's no playbook yet

When "just build it" isn't the right first step

Some projects don't need an MVP yet — they need someone to figure out whether the idea is technically sound before real money gets spent building it. A pricing algorithm, a data pipeline that doesn't exist yet, an integration nobody's tried before. That's R&D work, and it needs someone comfortable in ambiguity, not just someone who writes code fast.

I bring an applied-math and data-analysis background to this kind of work, alongside the same production-grade development skills I use on client builds — so research turns into working code, not just a report.

What I offer

Pre-development R&D

• Technical feasibility studies
• Market- and tech-alternatives research
• Exploratory data analysis
• Mathematical modeling and optimization
• Algorithm design
• Prototype planning and MVP strategy

Solution development

• Custom API design and implementation
• Backend services and data processing
• Statistical and AI-driven applications
• Frontend integration and admin tools
• End-to-end prototype or MVP delivery
• Creative coding and interactive data visualization

Who this is for

• Startups exploring an idea complex enough that "just start coding" isn't a safe plan
• Small businesses looking to digitize, automate, or make sense of their own data
• Founders who need a proof-of-concept or investor-ready demo, not just a pitch deck
• Anyone with a big idea and no internal technical team yet

Example projects this covers

• A custom algorithm for pricing optimization
• A backend service processing real-time sensor data
• Analyzing user behavior to prioritize what an MVP should actually include
• Exploring how to integrate OpenAI into an existing product
• A creative, data-heavy frontend for a complex dataset

Common questions

I don't have a technical co-founder — is this still for me? Yes — that's exactly who this is designed for. I can act as the technical partner for the research phase.

Do you just deliver a report, or working code? Working code where possible. Research that doesn't produce something tangible is much less useful — the goal is a prototype or clear technical answer, not just slides.

What if the research shows the idea isn't feasible as planned? Then you'll know that early, with specifics on why — which is far cheaper than finding out after a full build.