Poth is an AI customer feedback intelligence product that connects calls, support tickets, surveys and transcripts to reveal patterns and evidence-backed insights.
Poth is an AI customer feedback intelligence product that connects customer calls, feedback, support tickets, surveys, transcripts, and related workspace data so teams can understand what customers are saying in one place. The homepage describes it as “the customer brain” for a company, built to turn scattered conversations into searchable, decision-ready insight.
The product centers on adaptive surveys, hypothesis testing, and evidence-backed analysis. Its homepage and about page say it can find patterns, investigate root causes, generate follow-up questions, and deliver statistically validated findings for product, growth, and leadership teams.
Poth brings together customer calls, feedback, support tickets, surveys, transcripts, CRM notes, Slack threads, docs, in-app chat, and product analytics into one place so teams can search across sources instead of checking them individually.
The homepage shows a live-updating insight view that links evidence, segments, and suggested actions, helping teams move from raw feedback to a prioritized set of findings.
Poth says it can compare feedback against product, operational, and behavioral data to investigate root causes rather than only summarizing complaints.
The demo and homepage show an AI hypothesis engine that forms, validates, rejects, and updates hypotheses as evidence comes in, which helps teams test explanations instead of guessing.
When existing data is not enough, Poth generates targeted surveys and follow-up questions to validate the missing piece and close the loop between evidence and new data.
The about page says responses form a knowledge graph rather than a flat list, and that the system uses LLM-powered adaptation and statistical testing to surface significant findings.
Product teams can use Poth to search across calls, tickets, surveys, and transcripts to see what customers are repeating, which segments are affected, and what actions are associated with the issue.
Growth and onboarding teams can use the prioritized insights view to identify friction points, such as low activation or missing setup steps, and tie them to evidence before deciding what to change.
Support and operations teams can compare feedback with operational data to look for root causes, then use generated follow-up questions to gather the missing detail needed to confirm or reject a theory.
Research and insights teams can use the adaptive survey flow to explore emerging themes in real time, rather than relying on a fixed questionnaire with the same prompts for everyone.
Leadership teams can use the evidence-backed insights and hypothesis testing to review recurring customer themes without manually reading every source or maintaining spreadsheet-based analysis.
Poth is shown as an interactive customer feedback and survey product. Its homepage says it unifies conversations, feedback, and customer data so teams can ask questions, find patterns, and understand what customers are saying.
The site presents Poth as useful for product, growth, and leadership teams, and the about page frames it around customer feedback intelligence and AI hypothesis testing.
The product combines connected customer data, an interactive survey flow, hypothesis testing, and targeted follow-up questions. The demo page shows it can conduct an intelligent survey while forming and testing hypotheses in real time.
The site says Poth connects customer calls, feedback, support tickets, surveys, transcripts, and other sources such as Slack, CRM notes, docs, in-app chat, and product analytics. The about page also says it builds adaptive surveys and a knowledge graph from responses.
The pricing page is not available and shows a 404, so the public site does not provide pricing details. The contact page invites visitors to request a demo and mentions a response time within 24 hours.
