Unified Debugging for Product Teams
TL;DR
Debugging tool for **product managers and engineering leads at 10–500-employee tech startups** that **auto-links PostHog events to GitHub code functions + user segments** so they can **resolve metric drops in <2 hours** (vs. 5+ days manually).
Target Audience
Product managers, data analysts, and engineering leads at **startups and mid-sized tech companies (10–500 employees)** using PostHog, GitHub, and cloud databases to track metrics.
The Problem
Problem Context
Product teams track metrics in analytics tools but struggle to connect those insights to the actual code or user data that caused them. They waste weeks jumping between dashboards, codebases, and databases to manually piece together what went wrong and why.
Pain Points
Teams spend hours manually mapping analytics events to code functions, only to find the data doesn’t match. Engineers ask, ‘What does this event actually track?’—and half the time, the answer changes the entire investigation. PostHog, GitHub, and databases stay siloed, forcing teams to stitch everything together themselves.
Impact
Slow debugging costs teams lost revenue from untracked issues, wasted engineering time, and delayed product improvements. A single misaligned metric can lead to weeks of incorrect fixes, while competitors move faster with better data connections.
Urgency
This isn’t a ‘nice-to-have’—it’s a *blocker- for teams that rely on data-driven decisions. Every week spent debugging is a week not spent shipping features or fixing real problems. Startups and mid-sized companies can’t afford this slowdown.
Target Audience
Product managers, data analysts, and engineering leads at *startups and mid-sized tech companies- (10–500 employees) also face this. Even larger companies with dedicated analytics teams struggle with the same siloed workflows.
Proposed AI Solution
Solution Approach
A single tool that automatically connects analytics events to code functions and user data, so teams can debug issues in one place. No more jumping between PostHog, GitHub, and Snowflake—just a unified view of *what- happened, *how- it happened, and who was affected.
Key Features
1. **Auto-Mapping**: Links analytics events (e.g., ‘button_click’) to the exact code function that triggered them, using **static analysis** of the codebase. No manual setup required.
2. **User Context**: Shows **who** performed the action (e.g., user ID, segment, behavior history) alongside the code and event data, so teams can see the full picture.
3. **Debug Mode**: Lets PMs/analysts **replay an event**—see the code that ran, the user’s session, and the analytics impact—all in one timeline.
4. **Hypothesis Validator**: Lets teams test fixes in real-time by **simulating changes** and seeing how metrics would shift before shipping.
User Experience
Teams open the tool, see a dropped metric, and click ‘Debug.’ The system instantly shows:
- The code function*- that caused the issue (with a link to GitHub).
- The *user segments- affected (e.g., ‘new users in EU’).
- A timeline of the event, user session, and analytics impact.
They fix the issue in hours, not weeks, and ship corrections faster.
Differentiation
Unlike PostHog or GitHub (which are siloed) or manual scripts (which break), this tool natively connects all three data sources—analytics, code, and users—in one place. It’s the only solution built for the debug workflow, not just analytics or code.
Scalability
Starts with *single-seat plans- ($50/mo) for small teams, then scales to *team/seat pricing- ($100–$200/mo) as companies grow. Adds premium features like *automated root-cause analysis- or A/B test debugging for larger teams.
Expected Impact
Teams ship fixes 5x faster, reduce wasted engineering time, and catch issues before they hurt revenue. For a $50/mo tool, the ROI is obvious—it pays for itself in one debug session.