Startups — 0→1: idea to first real product
A startup here means you’re trying to turn an idea into something people can use and give you feedback on—not just slides. Founders often call that 0→1: going from no live product to the first version customers can actually try. We help you narrow scope, avoid building the wrong thing first, and ship that credible first cut. In product circles it’s also called an MVP (minimum viable product): the smallest version that still teaches you something real from customers.
When you want us to guide scope and delivery
We’ll translate options into tradeoffs you can judge: time, cost, risk, and what you’ll learn at each step. We guide you through a sensible path on frameworks and hosting—showing progress in demos you can share with cofounders, early users, or investors.
If “AI” is on your roadmap, we’ll walk through concrete options: chat-style help for your customers, drafting or summarizing internal work, or triaging support requests—always with clear limits so the product stays trustworthy and you’re not promising magic.
- Clear problem statement the whole team can align on, grounded in customer conversations
- A phased plan: what we build first, what we defer, and why
- Design before expensive engineering where it saves you money
- Simple ways to see whether people actually use what you shipped
- When AI makes sense: a small, testable slice—not a science project
When you want depth on implementation
We match your depth. Expect crisp scope, explicit assumptions, thin vertical slices, and instrumentation (activation, retention, support burden) so you’re not flying blind post-launch. We’re happy to pair with your stack choices or own implementation end-to-end—including pragmatic AI implementation when it earns its place: API integration, prompting and quality checks, lightweight evals, privacy and data boundaries, cost and latency tradeoffs, and human-in-the-loop where outputs affect money or safety.
- Hypothesis-driven scope and explicit “why now” for each release
- Prototype and user-test before committing build cycles
- Web, mobile, or both—aligned to how your users actually behave
- Handoff-friendly codebase and docs if you’re hiring in-house next
- AI when it’s justified: when not to use LLMs, retrieval-style (“RAG”) behavior grounded in your content, and shipping checks—not demo-only prompts
Works well with
Product ideation, product design, and product strategy when you need sharper positioning before you build—or small-business tooling when you’re really an operating business that needs reliable day-to-day systems.
Tell us about your stage and what you need to prove next—we’ll match how deep you want to go on implementation in our reply.