01 / PRODUCT STRATEGY
AI Product Development
From a rough idea to a shipped AI product people actually use. We validate, prototype, build the MVP, launch it — and keep iterating with you.
Work with us →Who it's for
- Founders with an AI product idea who need a technical team to validate and ship it.
- Product teams adding an AI feature to an existing app who want it done right the first time.
- Companies that want a working prototype before committing the full budget.
Problems we solve
- An idea, but no way to tell whether AI can deliver it reliably.
- Prototypes that demo well but break with real users and real data.
- Unclear trade-offs between model quality, cost and latency.
- No in-house experience taking LLM products to production.
What you get
- Validation brief: feasibility, model options, cost and risk estimate.
- Working prototype tested against real inputs.
- Production MVP: frontend, backend, model integration and evals.
- Launch setup: hosting, analytics, monitoring and guardrails.
- Iteration roadmap based on real usage.
How it works
- 01
Validate
About 1 week. We pressure-test the idea, the data and the model options before writing product code.
- 02
Prototype
1–2 weeks. A working slice with real model calls, so you see what works and what doesn't.
- 03
Build the MVP
3–6 weeks. The production version: UI, backend, integrations, evals and guardrails.
- 04
Launch
We deploy it, add monitoring and analytics, and put it in front of real users.
- 05
Iterate
Ongoing. We improve prompts, models and features based on how people actually use it.
Typical engagement: 4–8 weeks from kickoff to launch. Prototype-only engagements take 2–3 weeks.
Related work
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DeepPress
Talk to your sites. A public registry of WordPress, Ghost & Substack sites with Model Context Protocol support — connect AI agents and chatbots to your favorite publishing platforms.
Prompt
A focused workspace for crafting, testing and managing prompts — iterate fast and keep your best prompts organized and reusable.
FAQ
Do you build MVPs or production systems?
Both. We usually start with a prototype to de-risk the idea, then harden it into a production MVP you can put in front of real users.
Which models and tech stack do you use?
Whatever fits the problem. We work with OpenAI, Anthropic, Google and open-weight models, and choose based on quality, cost, latency and privacy needs.
Can you work with our existing engineering team?
Yes. We can own the build end to end, or pair with your team and hand over a codebase they're comfortable maintaining.
Who owns the code?
You do. Everything we build for you is yours.
Have an AI product idea?
Tell us what you want to build. We'll reply with a quick take on feasibility and how we'd approach it.
Start a conversation