AI Product Strategy
Building an AI wrapper around ChatGPT is easy; building a defensible AI product is hard. We help founders and product managers design AI-native applications that solve real user problems, establish proprietary data moats, and achieve true product-market fit.
Core Features
Proprietary Data Strategy
Designing data-flywheels where every user interaction improves the model, creating a defensive moat against competitors.
AI-Native UX Design
Moving beyond chat interfaces to design innovative UI patterns (like Copilots, auto-completions, and ambient AI).
Pricing & Margin Modeling
Structuring pricing tiers that remain profitable despite the variable cost of LLM API tokens.
Build vs Buy Architecture
Advising your engineering team on when to use managed APIs versus when to train proprietary open-source models.
Our Process
Problem Space Auditing
Week 1Identifying the core user pain point and determining if AI is actually the best solution, or just a marketing gimmick.
Defensibility Mapping
Week 2Structuring the product so that it relies on proprietary data or unique workflows, rather than just a generic API call.
UX/UI Prototyping
Week 3-4Designing Figma prototypes that showcase how the user will interact with the AI (handling latency, errors, and approvals).
Technical Feasibility Check
Week 5Running quick proofs-of-concept (PoCs) to ensure current foundation models can actually deliver the required accuracy.
GTM & Launch Strategy
Week 6Developing the Go-To-Market plan, defining the target audience, and structuring the SaaS pricing model.
Technologies We Use
FAQ
Is 'AI Wrapper' a bad thing?
How do we handle the cost of AI APIs in our SaaS pricing?
Do you build the product too?
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