AI can build your product. But it still doesn't know what to build.
decior turns your customer interviews and usage data into evidence-backed feature proposals — ready to hand straight to Cursor, Claude Code, or any coding agent.
Smart Notification Digest for Mobile Users
11 of 20 users disabled notifications within 30 days. Interviews reveal alert fatigue as the primary cause. A daily digest with priority filtering would reduce churn by an estimated 18%.
Evidence
“"I turned off all alerts — there were just too many every day."”
interviews.txt · chunk 4
“"A weekly summary would be so much more useful than constant pings."”
interviews.txt · chunk 11
Dev tasks
Add notification_preferences table with digest_enabled flag
Build digest aggregation cron job (daily, per user timezone)
Add in-app preference toggle to notification settings screen
How it works
Three steps from raw research to agent-ready spec
Upload your research
Drop in customer interview transcripts, CSV usage data, or any text files. decior parses, chunks, and indexes everything automatically.
Ask what to build
Ask a plain-English question. Our AI decomposes it into targeted evidence searches — no single-shot guessing.
Ship a spec, not a summary
Get a structured proposal with verbatim customer quotes, UI changes, data model, and dev tasks sized for Cursor or Claude Code.
Coding agents are 10× faster at building. But if the spec is vague, they build the wrong thing — fast.
PMs spend 3 days manually synthesising interviews and writing briefs that still get lost in translation before a single line of code is written.
decior closes the gap between discovery and agent-ready implementation — in minutes, not days.
Every proposal is grounded in verbatim customer quotes, calibrated with a confidence score, and formatted so your coding agent can execute without clarification.
From raw research to build-ready spec
Drop in your files, ask your question — decior handles the synthesis. Here's exactly what that looks like.
Customer Data
Drop files here
.txt · .pdf · .md · .csv
interviews.txt
8 customer interviews · 3.4 KB
usage_data.csv
20 accounts · 15 columns
Question
“What should we build next to reduce churn?”
Parse & chunk documents
28 chunks indexed
Generate embeddings
Semantic search ready
Run evidence retrieval
47 relevant passages
Synthesise proposals
Cross-referencing signals…
What decior looks for
Recurring pain points across interviews
Behavioural signals in usage data
Correlation between feedback and churn risk
Feasibility signals from usage patterns
Two-Way Jira Integration
5/20 accounts attempted Jira syncs; 0 succeeded. Marcus T. spends 45 min/week manually copying tickets. Fixing this would directly address the top churn signal.
“I always introduce errors copying tasks between tools.”
Marcus T. — interviews.txt · Interview 1
Jira OAuth integration & token storage
Ticket pull & 15-min sync with upsert
Sync status indicator in nav bar
See it run on real customer data — right now.
The interactive demo loads 8 real interview transcripts and a 20-row usage CSV. Watch decior turn them into 5 prioritised, evidence-backed proposals in seconds.
What the demo includes
interviews.txt
8 customer interviews, 3,400 words, March 2026
usage_data.csv
20 accounts · churn scores, NPS, feature usage
5 generated proposals
Jira sync, reporting, notifications, dependencies, search
Full workspace
Evidence, UI changes, data model, dev tasks — all tabs live