Aura

Aura is the AI reporting assistant inside a B2B SaaS platform for multi-location brands — I was the Product Designer bringing reporting into it.

Users could ask a question, get a summarized answer, and move into the full report when they needed proof. The work asked whether AI could make reporting faster without breaking trust.

A chat reply wasn't enough — users had to trust the number.

They needed to see which filters and date range produced it, know the data was fresh, and get from the AI summary back to the source report to verify it, share it with leadership, and reuse it next month. Sending them out of the assistant too early would break the value of the AI.

1. 01
   Mapped how customers report todayWeekly and monthly pulls, screenshots, exports, and summaries sent to leadership — real workflows, not a novelty for AI to decorate.
2. 02
   Ran discovery and prototype tests with enterprise clientsTo learn what they needed before trusting an AI answer, and whether they wanted a chat-first or report-first experience.
3. 03
   Tested two directions; neither won aloneAn in-chat canvas felt fast but less trustworthy for deep reporting; a full redirect felt complete but reduced the assistant to a shortcut.
4. 04
   Landed on a hybridThe assistant answers, Reporting proves.
5. 05
   Rebuilt the answer tableFixed narrow columns, raw timestamps, missing network context, unclear row limits, and export behaviour.

Fast answer first, full confidence one click away

• Prompt
• AI summary + table
• Visible filters
• "Open report" deep link
• Full KPIs & charts
• Live below ↓

1. 01
   Defined the MVP model — answer in chat, verify in Reporting, reuse via save / share / export
2. 02
   Made trust first-class — every answer shows its filters, freshness, and data source
3. 03
   Reusable table and reporting patterns for v2 — reviews, posts, listings, competitor data
4. 04
   Gave product and engineering a clearer build direction and POC success criteria