Stop Building Random Features: How to Actually Do Competitive Analysis from Code

Your PM just came back from a competitor demo. "They have real-time collaboration. We need real-time collaboration." The team spends 6 weeks building it. Usage after launch: 3 users.

This is what happens when competitive analysis is based on demos, not data.

The Demo-Driven Roadmap Problem

Competitor demos show their best features in ideal conditions. They do not show:

• How many customers actually use the feature
• How well it works at scale
• Whether it solves a problem your customers have
• How much of the feature you already have in your codebase

That last point is critical. Teams often build features they already partially have because nobody mapped their own codebase against the competitor feature set.

Code-Level Competitive Analysis

What if instead of guessing, you could:

1. Auto-detect competitors from your market positioning
2. Catalog their features from documentation, changelogs, and product pages
3. Map those features against your codebase to find what you already have
4. Score each gap by implementation complexity based on your actual architecture

Step 1: Competitor Feature Extraction

AI crawls competitor documentation, changelogs, and marketing pages. Extracts a structured feature list: name, description, category, apparent maturity.

Step 2: Codebase Mapping

For each competitor feature, analyze your codebase: do you implement equivalent functionality? This is not keyword matching. It is structural analysis of your feature clusters against competitor capabilities.

Results:

• Full coverage: You have this feature. No action needed.
• Partial coverage: You have 60% of this. Small gap to close.
• No coverage: You do not have this. Evaluate whether you should.

Step 3: Complexity Scoring

For features you do not have: how hard would they be to build? Based on your architecture:

• Which feature clusters would be affected?
• How many files would change?
• Which teams would be involved?
• What dependencies exist?

"Add SSO" sounds simple in a planning meeting. The complexity score reveals: 27 files across auth, user management, billing, and admin. 4 different team owners. Dependency on a billing integration that does not support per-seat SSO pricing yet.

Step 4: Prioritization

Combine gap importance (how many competitors have it, how often customers ask for it) with implementation complexity (your codebase analysis). The result: a prioritized list of gaps with real effort estimates.

Why This Beats Traditional Competitive Analysis

Traditional: PM spends a week on competitor websites, creates a spreadsheet, estimates effort from gut feeling.

Code-level: AI analyzes competitors and your codebase in hours, produces a gap report with structural complexity scores, identifies features you already partially have.

The first is opinion. The second is data.

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Keep Reading

Random feature building is a symptom of the Understanding Tax at the product level. When PMs do not understand what the codebase already contains, they request features that already exist.

For the detailed methodology, read Competitive Intelligence from Code: How Gap Analysis Works.

Glue provides automated competitive gap analysis as part of its pre-code intelligence platform. Map competitor features against your codebase reality, not guesswork.

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Originally published on glue.tools. Glue is the pre-code intelligence platform — paste a ticket, get a battle plan.