Stop chasing feedback across app reviews, customer service tickets, surveys, and seller reports. ProductPath ingests every signal, clusters it into themes with AI, and prioritizes evidence-backed opportunities, so your team ships what matters.
Search relevance complaints surged to 47 items, the highest of any theme.
Product managers spend hours stitching together spreadsheets, Slack threads, and ticket queues, only to prioritize by whoever shouted loudest this week.
"Search for paper towels returns 8 sponsored listings. Can't find what I want."
"Payment failed with generic 'try again' message. Lost my whole cart."
"Tracking says delivered but package wasn't there. Third time this month."
"Organic search placement dropped 40% after the algorithm update."
"Caller transferred twice. Gift card balance shows $50 but checkout says insufficient."
Every piece of feedback, ingested, clustered, prioritized, and tracked end-to-end.
Connect every feedback channel into one unified stream: app reviews, customer service tickets, surveys, seller reports, and social media.
AI groups thousands of raw feedback items into themes, tagging each with severity, sentiment, and journey stage.
Score opportunities by patient impact, operational impact, and evidence volume, not gut feel.
Convert themes to opportunities, track them on a kanban board, and measure the impact of every release.
App reviews, customer service tickets, surveys, seller reports, and social media, all in one triageable inbox with severity, sentiment, and AI-assigned themes. Filter, search, and drill into any item to see the full context.
Search for 'paper towels' returns 8 sponsored listings. Can't find what I actually want.
Payment kept declining with no error message. Lost the whole cart at checkout.
Tracking says delivered but the package wasn't there. Third time this month.
Organic search placement dropped 40% after the last algorithm update.
"I search for 'paper towels' and the first 8 results are sponsored listings for random brands. I have to scroll past all of them to find what I actually want."
Conversational insights grounded in your actual feedback data. Every answer includes a coverage indicator showing how many items back it, and every claim links to the source snippet.
What evidence supports fixing search as a priority?
Search relevance is the #1 customer pain point with 47 feedback items, the highest of any theme. Sellers report a 40% drop in organic placement, making it the strongest evidence-backed priority.
"First 8 results are sponsored listings for random brands."
"Organic placement dropped 40% after algorithm update."
Validate search relevance demand with user research, then route to engineering for an algorithm-level fix.
AI clusters thousands of feedback items into themes with severity, trend, and evidence counts, so you spot rising issues before they become escalations.
"First 8 results are sponsored listings for random brands."
"Search used to be great. Now I can't find anything without adding 'non-sponsored.'"
Drag-and-drop kanban with AI priority rationales, impact scores, and one-click conversion to epics. Every opportunity is backed by evidence snippets.
Improve Search Result Relevance
Highest evidence volume. 40% organic placement drop.
Unify Delivery Tracking
Fix Checkout Payment Errors
Compare complaint volume and feedback themes before and after each release. Know which changes moved the needle, and which didn't.
Updated search ranking with improved relevance scoring and reduced sponsored listing density on desktop.
Every insight links back to the exact source feedback. Coverage indicators show how many items back each AI answer, so you always know how much to trust it.
Every evidence snippet links to the original feedback item. Click through to read the full context.
A color-coded bar shows how many feedback items back each AI answer: Strong, Moderate, or Limited.
Designed to plug into an Enterprise Data Lakehouse. Connect your real data sources at scale.
During ingestion, an LLM assigns themes, severity, sentiment, and journey stage to every feedback item. That metadata becomes structured tags attached to the vector embedding, so a vector database indexes each item by both meaning and context. When you ask a question, a hybrid retriever pulls only the relevant chunks, not the full database.
"What are the top search issues?"
Generates a natural language answer from the retrieved chunks. Tags were already assigned at ingestion time.
Search relevance is the #1 pain point with 47 feedback items, the highest of any theme.
Unprocessed records from every source, stored at scale before enrichment
Each feedback item stored as a vector with attached tags from ingestion
Theme-grouped, scored records for browsing
Retrieves only relevant chunks, not the full database
Coverage indicators show how many items back each answer
Explore the full interactive demo. Browse the inbox, inspect AI themes, drag opportunities across the kanban, and ask AI anything.
Launch the Demo