AI-First Feedback Product
Most incumbents in the customer-feedback space are dinosaurs: heavy UI, rigid data model, integrations bolted on as an afterthought. Teams in 2026 expect their tools to behave more like Stripe than like the survey software of a decade ago.
We helped build a feedback product on the opposite bet: a small, sharp foundation that could outpace the incumbents on velocity even with a tiny team.
Three bets that shaped everything
- Design system first. Every component built once, themed once, reused everywhere. New screens cost hours, not days.
- Headless API at the core. The web app is one consumer of the API, not the API itself. The same endpoints would later power integrations, embeds, and AI agents, without a retrofit.
- Modern stack, no legacy weight. A boring, recent choice at every layer.
The AI inflection
Because the API was headless from day one, every piece of the product was already addressable as a structured surface. When the AI tooling around us matured, there was no new layer to invent to make the product agent-friendly; agents could talk to the same API a customer would.
The agentic capabilities (MCP support, structured analysis, agentic actions) shipped in weeks, because there was nothing to refactor on the way there.
The shape of it
The product had to work two ways at once: an agent that can collect, route, and analyze feedback at scale, sitting on a foundation a human can also walk into cold and run a survey in five minutes. Both halves, on one contract.
Why it matters
The interesting work in an AI product is rarely the model. It's whether an agent can do everything a person can, through the same clean contract.
Get the foundations right (design system, headless API, modern tooling) and the AI features stop being a research project and start being a release.