Turn enterprise data into product direction.
Connect signals across your organization, from analytics and customer research to support, sales, and enterprise systems. ProductPath uses AI to find patterns, surface opportunities, and prioritize what matters with evidence behind every recommendation.
Your product signals are everywhere. Your priorities are nowhere.
Product managers spend hours stitching together spreadsheets, transcripts, 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."
From noise to roadmap in four steps.
Every signal, ingested, clustered, prioritized, and tracked end-to-end.
Ingest
Connect every signal source into one unified stream: app reviews, customer service tickets, surveys, seller reports, and social media.
Cluster
AI groups thousands of raw signals into themes, tagging each with severity, sentiment, and journey stage.
Prioritize
Score opportunities by patient impact, operational impact, and evidence volume, not gut feel.
Act
Convert themes to opportunities, track them on a kanban board, and measure the impact of every release.
Every signal, one view.
Qualitative, behavioral, commercial, and enterprise signals, all in one triageable view with severity, sentiment, and AI-assigned themes. Filter, search, and drill into any item to see the full context.
- AI-tagged themes, severity, and sentiment
- Filter by source, category, or journey step
- One-click convert signals into opportunities
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."
Ask anything. Get evidence, not opinions.
Conversational insights grounded in your actual product signals. 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.
Patterns you can't unsee.
AI clusters thousands of signals 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.'"
From signal to roadmap.
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
Did it actually work?
Compare complaint volume and signal 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.
Statistical confidence, not vibes.
Every insight is grounded in source data with transparent sample sizes. Statistical significance indicators tell you whether a pattern is real or noise, so you prioritize with confidence.
Source-attributed
Every claim traces back to the exact source record. Full provenance chain from insight to raw input, no black boxes.
Significance indicators
Each AI answer reports its sample size and confidence level. Patterns below the significance threshold are flagged as insufficient evidence.
Lakehouse-native
Built to sit on top of an Enterprise Data Lakehouse. Ingest at scale, enrich with LLM tagging, and query with structured or semantic access.
Vector search meets keyword search.
During ingestion, an LLM assigns themes, severity, sentiment, and journey stage to every signal. 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.
- LLM tagging at ingestion: themes, severity, and sentiment assigned upfront
- Vector database: embeddings + structured metadata, indexed by meaning
- Hybrid retriever: pulls only relevant chunks, not the full database
- Structured storage: curated tables feed the workspace modules
"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
See it in action.
Explore the full interactive demo. Browse the unified view, inspect AI themes, drag opportunities across the kanban, and ask AI anything.
Launch the Demo