Startups
AI-assisted design tooling and lifecycle dashboards that compress the feedback loop between user research, design iteration, and engineering handoff.
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Industry overview
AI for product design applies machine learning and language models to accelerate user research synthesis, design iteration, and lifecycle tracking — reducing the time from insight to shipped feature.
At a glance
Product teams lose weeks in the gap between user research and design decisions. Interviews are transcribed but not synthesised; usability test recordings sit unreviewed; feature requests pile up in a spreadsheet no one fully reads. We build AI tooling that closes these gaps: automated interview transcription and theme extraction, sentiment-tagged feature request clustering, and a lifecycle dashboard that surfaces which features are in discovery, design, dev, or post-launch review.
We integrate your existing research and design tools — Notion, Dovetail, Figma, Linear — with LLM pipelines that extract themes from transcripts, cluster feature requests by similarity and business impact, and generate weekly synthesis reports. On the lifecycle side we build a Kanban-style dashboard that pulls status from Linear and GitHub, so design and product leadership have a real-time view of every feature from brief to shipped without manually chasing engineers for updates.
Key capabilities
Engagements are scoped to your business context — these are the core capabilities we bring to startups clients.
Automated interview transcription and LLM-driven theme extraction
Feature request clustering by topic similarity and business impact scoring
Weekly AI synthesis report covering research findings and open questions
Lifecycle dashboard pulling status from Linear and GitHub in real time
Figma-to-Linear handoff automation with acceptance criteria generation
Post-launch feature analytics linking usage data back to original research themes
Work with us
Share what you're building — we'll respond within one business day with questions or a proposal outline.
