EchoLab: A/B Testing Hypothesis Generator
EchoLab is an AI-powered experimentation assistant built for Product Managers who want to uncover the testable, high-impact ideas in less time. It combines latest LLMs, retrieval-augmented generation (RAG), and unsupervised clustering to transform customer feedback into actionable insights.
By learning from real PM playbooks and past product cases, EchoLab automatically generates experiment-ready hypotheses that product teams can act on — cutting the hypothesis setup time from 2 weeks in average to under 48 hours. This acceleration empowers product teams to run 5x more experiments annually, and bring measurable revenue for data-driven companies.
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By seamlessly integrates with customer feedback sources and experimentation platforms, echoLab enables PMs move effortlessly from support tickets → insights → live A/B tests in a true end-to-end AI workflow.
Less triage. Faster cycles. More learning. EchoLab turns the backlog and feedback into a queue of high-impact experiments ready to drive growth.
Overview
EchoLab is an AI tool that helps Product Managers listen better and put insights into action.
It started with a simple observation:
Every PM knows that good ideas don’t come from nowhere — they come from listening. But when you’re managing hundreds of customer feedback tickets, Slack threads, and product reviews, it becomes nearly impossible to spot patterns fast enough to test them.
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This shaped the foundation of EchoLab.
We built a generative AI-powered system that can digest thousands of customer tickets at one time, cluster them into themes and pain points, and automatically surface testable experiment ideas - all in a matter of hours.
By connecting directly to ticket platforms and experimentation platforms, EchoLab creates a seamless AI workflow from feedback to action.
Just like that, BOOM! Within 48 hours, a company’s backlog of unstructured feedback turns into a data-driven queue of experiments ready to test, learn, and grow.
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More than a tool, EchoLab became a way for PMs to listen wider, think faster, act with confidence, and bring the human voice back to every experiment.
AI Hypothesis
If the AI classifies customer support tickets with 90%+ precision and clusters them into recurring UX themes, then it can generate 2–3 testable A/B hypotheses per theme that reflect real user friction.
When PMs adopt these hypotheses into their experiment pipeline, they are more likely to run relevant, high-impact tests — leading to faster resolution of user pain, increased product adoption, and higher retention.
This, in turn, strengthens the company’s experimentation culture, accelerates learning velocity, improves the ROI of product development efforts, and shorten the hypothesis generation cycle from 3-4 weeks to less than 48 hours.
Customer Insights
​Product Managers Insight
PMs struggle to keep a steady backlog of meaningful A/B test ideas but feel out of the loop on user pain.
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“Honestly, 90% of my backlog ideas are gut feels — I rarely have time to dig into tickets.”
— a Product Manager in an SaaS Service Company
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Data Analysts Insight
Analysts want tighter hypotheses grounded in behavior and user feedback — not vague test ideas.
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“Give me something that maps to a measurable metric — not just ‘make the button blue.’”
—a data analysts in an E-commerce company
Support Operations Insight
Support leads are flooded with repeat tickets, but lack a scalable way to surface trends for product teams.
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“We see the same complaints over and over — but no one’s connecting the dots.”
— a Member in the Support Operation Team
Stakeholders (Product/Growth/UX Leads) Insight
Leaders want faster experimentation, but see teams stall due to weak or disconnected test ideas.
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“I don’t care where ideas come from — I just want them to be real and shippable.”
— one of the Stakeholder of Amazon
Customer Segments
01
Product Members
PMs and designers driving experimentation, growth, UX, and product improvement
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Needs & Pain Points:
Need a steady stream of testable, user-grounded ideas; lack time to mine raw feedback
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Data Analysts
Growth or experimentation analysts responsible for test tracking and insights
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Needs & Pain Points:
Need structured hypotheses with clear variables and measurable impact paths​​​
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Support Operations
Leads managing customer support channels and triaging large volumes of tickets
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Needs & Pain Points:
Want to close the loop with product; need a system that surfaces recurring issues quickly
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04
Stakeholders
Heads of Product, Growth, or UX who care about overall experiment velocity and user satisfaction​​​​​
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Needs & Pain Points:
Need confidence that the team is learning fast and solving real user problems
Persona 1
Persona 2
Customer Journey
Based on our interviews with product managers, growth leads, and UX researchers, we identified several key user segments:
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Data-driven PMs in mid-to-large tech companies who run frequent A/B tests but struggle with hypothesis backlog and manual triage.
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Early-stage startup PMs who lack access to large analytics teams and need faster, easier experimentation.
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Product analysts and growth teams supporting multiple PMs across product lines, overwhelmed by fragmented customer feedback.
We chose to focus our MVP on growth-focused Product Managers who spend the most time turning qualitative insights into testable hypotheses — a process that’s often slow, manual, and cognitively draining.
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This group faces a recurring bottleneck: they have a wealth of customer data but a shortage of time and structure to transform it into actionable experiment ideas. They jump between tools — Zendesk for tickets, Sheets for clustering, Docs for hypothesis writing, GrowthBook for setup — wasting hours on repetitive work before real testing even begins.
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By targeting them first, EchoLab could deliver immediate, measurable value:
an AI-powered workflow that ingests feedback, clusters pain points, and generates ready-to-test hypotheses within 48 hours.
This not only accelerates learning cycles but also empowers PMs to focus on strategy, creativity, and insight — the parts of product management that truly require human judgment.
AI Input & AI Output
Input
- Raw Ticket Text: Full body of the customer message, including subject line, conversation thread, and tags
- Metadata: Ticket creation time, product area, user type, frequency of related tags, language​
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Output
- Ticket Label: Each ticket is labeled as BUG or IMPROVEMENT (with confidence score), and only improvements enter the A/B stream
- Cluster Theme: A short, human-readable label describing the theme (e.g. "Onboarding Drop-off", "Navigation Confusion", "Slow Search Results")
- Generated Hypotheses: 2–3 structured A/B test ideas per theme
Data Pipeline
The data pipeline processes incoming Zendesk tickets in near real-time, classifying them as bugs or improvement opportunities, clustering related feedback, retrieving context, and generating either A/B test hypotheses or bug tickets.
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1. Ticket Ingestion
Zendesk tickets captured via webhook listener
Metadata (e.g., product area, tags, timestamp) appended to the payload
Raw content stored in Postgres for processing and audit history
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2. Preprocessing & Classification
GPT-4o classifies each ticket as either BUG or IMPROVEMENT
Branch logic determines downstream path:
If BUG → Bug Clustering
If IMPROVEMENT → Hypothesis Generation
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3A. Bug Ticket Flow
Qwen 3 embeddings generated for bug tickets
Clustered by semantic similarity to identify recurring issues (e.g., “login fails on mobile,” “checkout crashes”)
Cluster summary, example tickets, and metadata compiled into a structured bug report
Displayed in the Bug Insights section and optionally pushed to Jira (on roadmap)
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3B.Improvement Ticket Flow
Qwen 3 embeddings generated for improvement tickets
Clustered into UX themes (e.g., onboarding confusion, navigation issues)
Clustered tickets labeled and prepared for hypothesis generation
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4. Context Retrieval
Top 3 relevant trunks retrieved from internal knowledge base using embedding search
Sources include past experiments, UX patterns, service documentation, and design guidelines
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5. Hypothesis Generation
GPT-4o prompted with:
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Cluster summary
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Retrieved context
Generates 2–3 structured hypotheses with variables, test suggestions, and metrics
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6.Output Delivery
Improvement Flow: Hypotheses auto-synced to GrowthBook as draft experiments
Bug Flow: Clustered bug reports shown in Bug Insights, optionally synced to Jira or tracked via dashboard
Tech Stack
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Frontend :TypeScript, React/Next.js, Tailwind CSS + shadcn/ui
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Backend: Python with FastAPI / Java with REST
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AI Models (LLMs): GPT-4o (OpenAI API) + Qwen3
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Embedding & Retrieval: Qwen3
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Database: PostgreSQL with JSONB support
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Background jobs: Celery + Redis (Docker)
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Architecture
User Stories & Acceptance Criteria
Success Metrics
AI Product Scability
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B2B SaaS Knowledge Base: The system will expand to cover common B2B SaaS pain points like onboarding, permissions, and team collaboration.​
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Multi-Language Support: Language detection and translation APIs will enable clustering and hypothesis generation from global ticket streams.
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Feedback Loop: PMs will rate hypothesis quality directly in the UI to continuously fine-tune prompt accuracy and relevance.
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Tool Integrations: Deep integration with GrowthBook, Amplitude, Mixpanel, and Jira will enable end-to-end experiment planning and execution.
