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 the product is addressable — 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.