Fabi.ai helps Gauge analyze production data in minutes
As a product manager, Fabi.ai makes it incredibly easy to pull reports in order to make data-driven decisions.
Summary
Gauge, an AI-powered Generative Engine Optimization (GEO) platform transforming how businesses show up in LLM-based searches, needed sophisticated product analytics without the overhead of dedicated data infrastructure or legacy business intelligence. By leveraging Fabi.ai's AI-powered analytics platform, Gauge's founding product manager Ethan Finkel transformed raw data in their database into actionable insights, automated weekly reporting workflows, and built comprehensive product analytics dashboards, all without requiring engineering resources or complex data warehouse setups. The results include immediate access to critical product metrics, automated Slack reporting that keeps the entire team informed, and data-driven product decisions that directly influence feature development and user engagement strategies.
I was able to get up and running in less than 10 minutes and was getting value immediately. I spend an hour every morning consuming data, and that informs my entire product strategy.
Building the future of AI-powered market research
Gauge is leading the charge on Generative Engine Optimization (GEO), helping businesses understand when and how they show up in LLM searches. The platform helps companies understand market positioning, track competitive mentions, and identify strategic opportunities through sophisticated AI analysis brand mentions and links.
With Ethan as the founding product manager, the company needed enterprise-level analytics capabilities without enterprise-level overhead.
The challenge: Product analytics for a lean startup
Like many early-stage startups, Gauge faced the classic challenge of needing sophisticated data insights without the luxury of dedicated data engineering resources. The company was collecting valuable product usage data but struggled to transform it into the real-time insights needed for rapid product iteration.
Gauge was confronting several key analytics challenges:
- Engineering resource constraints: As a growing team, dedicating engineering time to data infrastructure would slow core product development
- Limited bandwidth and expertise for data analysis: The team is technical and hiring a dedicated data analyst or scientist was not yet a priority
- Real-time decision making: Product decisions needed to be based on current user behavior patterns, not week-old reports
- Complex data relationships: Understanding user engagement required combining data from their production Postgres database and CRM
In the past, Ethan has set up custom, hosted Jupyter notebooks, but replicating that infrastructure would consume precious engineering cycles that were better spent on product development.
Transforming product analytics with AI: Fabi's solution
When Ethan discovered Fabi, he found a solution that matched his technical sophistication while eliminating infrastructure overhead. The platform allowed him to replicate and exceed his previous analytics setup in minutes rather than months.
Fabi's comprehensive approach enabled Gauge to:
- Connect to their data sources seamlessly: Direct integration with a read replica of their production Postgres database
- Generate complex queries without manual coding: Use AI to write sophisticated SQL queries for product analytics, from user engagement metrics to feature adoption analysis
- Automate reporting workflows: Set up scheduled reports that automatically post user insights to Slack every Friday morning
- Iterate rapidly on hypotheses: Test product theories and adjust analyses in real-time using AI-generated code
Our product strategy comes directly from consuming tons of data and answering questions with data. If I had to wait a week for every piece of data, I would be completely ineffective and it would set us back competitively.
From raw data to product insights
Fabi's notebook environment (Smartbooks) provided Gauge with a sophisticated analytics setup that combined the best aspects of Jupyter notebooks with built-in AI assistance and seamless data connectivity. Ethan describes the transformation:
Data exploration: The Gauge product team can quickly perform exploratory data analysis on the user event and production data to understand user behavior, identify power users and opportunities for improvement.
Advanced product analytics: Fabi’s AI Analyst Agent allowed rapid analysis of user behavior patterns, feature adoption rates, and engagement metrics across different user segments without writing complex SQL from scratch.
Automated insights distribution: Critical product metrics are automatically shared with the team through Fabi’s native Slack integrations, ensuring everyone stays informed about user engagement trends.
I was able to get up and running in less than 10 minutes. I was getting value within minutes. The AI writes SQL way faster than I would. It's like giving a task to an intern and letting them crank for three hours, except it's two minutes of AI time.
Results: Analytics transformation
By implementing Fabi.ai, Gauge has achieved remarkable results in product analytics efficiency:
Quantitative benefits:
- 10-minute setup time: From connection to first insights in under 10 minutes
- Eliminated infrastructure overhead: No need for dedicated engineering time on analytics infrastructure with the flexibility of integrating with a more advanced analytics setup in the future
- Weekly automated reporting: Consistent product metrics and AI-generated insights delivered automatically to the team in Slack, right where they work
- Real-time query capability: Instant answers to product questions without waiting for engineering support
Qualitative improvements:
- 80% faster time to insights: From hypothesis to insight, data analysis takes 80% less time with Fabi
- 20X faster to build workflows and data apps: What would normally take a team multiple hours or days to build can now be accomplished in just a few minutes
The AI writes SQL way faster than I would. It's like giving a task to a junior data scientist and letting them crank for three hours, except it's two minutes of AI time. Then I can come back and ask it to make adjustments as needed.
Real impact: Product management at startup speed
For Ethan as a founding product manager, Fabi has enabled a data-intensive approach to product development that would typically require a much larger team:
Lightning-fast insights: When the product team faces a question about their users or customers, answers are not available at their fingertips.
AI-powered efficiency: "It's rare that I actually write the queries myself because I just let the AI write them. As an expert SQL writer, I can let it run for a minute and then come back and say 'you did this wrong, change that', which is much more efficient than doing it myself. The way Fabi’s Analyst Agent is designed makes it really easy to collaborate."
Team enablement: While Ethan is the primary analytics user, the entire team benefits from the automated insights shared through Slack, creating a culture where everyone stays connected to product performance.
Beyond metrics: Building a data-driven product culture
The most significant impact has been on Gauge's product development philosophy. What was once engineering-constrained analytics has transformed into continuous product intelligence.
This data-driven foundation is helping Gauge make better decisions about:
- Feature development: Understanding which capabilities drive the most user engagement
- User experience optimization: Identifying friction points and opportunities for improvement
- Customer success: Tracking user behavior patterns that correlate with long-term retention
- Product-market fit: Measuring user engagement metrics that indicate product value
Looking ahead: Scaling analytics with growth
As Gauge continues to evolve, Ethan sees Fabi as a key enabler for maintaining analytical sophistication while preserving engineering focus:
Additional context integration: "We would love to integrate Fabi with our back end code in GitHub for added context for the AI. Having access to how we actually use and transform our raw database records would make the AI even more powerful."
Integrate more data sources: Gauge plans to integrate Clickhouse as an OLAP database for more sophisticated analytical workloads while maintaining the rapid iteration capabilities that Fabi.ai provides.
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