The measurable impact of product intelligence on product teams. Faster scoping, fewer surprises, better decisions.
Every capability is designed to save your team time, reduce risk, and improve decisions.
"Do we have single sign-on?" "How does the billing flow work?" "Who owns the notifications module?" Today, these questions take days and interrupt your best engineers. With Glue, you ask in plain English and get answers grounded in actual code — in 8 seconds.
Competitive analysis today is a spreadsheet disconnected from your codebase. You can't tell what you already have vs. what you'd need to build. Glue's Gap Atlas researches competitor features and scores each one against your actual code coverage (0-100%).
70% of project failures trace back to requirements issues. Your specs miss hidden dependencies because you can't see the code. Glue generates specs from your actual architecture — with the exact files affected, existing patterns to follow, and risks flagged upfront.
When a senior engineer leaves, the team's efficiency drops 48% for six months. But you don't know which modules are at risk until it happens. Glue continuously maps who knows what, where knowledge is concentrated, and which files have a bus factor of one.
New developers take 3-6 months to be productive. They spend weeks getting environments operational and reading outdated docs. With Glue, new hires explore the codebase through Chat from day one — cutting ramp time by 4x.
Organizations waste $109 million per $1 billion spent on failed projects.
Source: PMI
Most failures start with specs that miss technical reality. Better specs = fewer failures.
Source: Fohlio 2024
More than half of PM time goes to chasing information, not strategic work.
Source: Pragmatic Institute
Developers spend 17 hours per week on debugging, refactoring, and understanding existing code.
Source: Stripe
Teams rate themselves 3.8/10 on competitive selling, costing $2-10M/year in winnable deals.
Source: Crayon 2024
Two-thirds of enterprise software projects exceed budget.
Source: McKinsey
vs. 2-3 days of asking engineers and waiting for context.
Ask questions in plain English. Get answers with file references you can share.
Answers come from your live codebase, not outdated documentation or tribal knowledge.
AI researches competitor features from public sources and maps them to your codebase.
Every gap scored 0-100% against what your code actually has — not what someone remembers.
Know not just what's missing, but how hard it would be to build — before committing to the roadmap.
Every spec names the exact files that need to change. No more 'we didn't realize this would touch payments.'
Hidden dependencies surfaced before anyone estimates, not after the sprint is half over.
Plans follow your team's existing code conventions, not generic best practices.
See which modules depend on a single person — before they give notice.
Understand team strengths across features, languages, and system layers.
Hotspots, complexity, and churn scored per file. Know where tech debt lives and what it costs.
New engineers ask the codebase directly instead of interrupting your senior staff.
Feature discovery and code intelligence update automatically as the code changes.
Eliminate the negative-value onboarding period that costs $240K per failed senior hire.
Faster scoping. Fewer surprises. Better decisions.