Case Study: How a Startup Slashed Tech Debt by 40% Using Sourcegraph Cody’s Context Search
You’re staring down a massive, undocumented codebase for the first time, and you need to find and refactor a deprecated API that could be scattered across hundreds of files. The task seems to require an entire sprint—or maybe two.
TL;DR
Tech debt is the silent killer of startup velocity, crippling innovation and draining developer hours. This case study reveals how a startup leveraged Sourcegraph Cody, an AI-powered coding assistant with deep, cross-repository context search, to transform its approach to managing this debt. By enabling developers to instantly find, understand, and safely change code at scale, the team reduced its overall tech debt by an estimated 40%. The key wasn’t magic; it was giving developers an AI that could “see” the entire codebase at once, turning weeks of manual detective work into minutes of precise, AI-assisted refactoring.
Key Takeaways
- Context is Everything: Unlike standard AI assistants limited to a single file, Cody’s cross-repository search understands relationships across your entire codebase, making it uniquely powerful for tackling tech debt that spans multiple services .
- From Discovery to Fix at Scale: Cody, paired with Sourcegraph’s platform, doesn’t just find problems like code duplication or deprecated APIs; tools like Batch Changes automate the creation of fix PRs across hundreds of repositories at once .
- Drastic Time Savings: Real-world teams report cutting search and debugging time from hours to minutes, and reducing large-scale migration tasks from a full sprint to mere hours .
- Empowerment, Not Replacement: Cody shifts senior developers from answering repetitive context questions to focusing on high-level architecture, while accelerating onboarding for new hires .
- Security and Control Are Non-Negotiable: For startups handling sensitive data, Cody’s ability to be self-hosted ensures code never leaves company infrastructure, addressing a major barrier to AI tool adoption .
The Startup’s Tech Debt Crisis
Every startup faces the same pivotal moment: the pressure to ship features collides with the growing complexity of the codebase. Quick fixes accumulate. Documentation lags. Soon, developers spend more time navigating “spaghetti code” and deciphering ancient logic than building new value. Technical debt, the implied cost of all those past shortcuts, isn’t just a nuisance—it’s a strategic risk that taxes throughput, increases operational risk, and delays modernization .
For our case study startup, a SaaS company with a microservices architecture spanning over 300 repositories, the breaking point arrived during a crucial platform upgrade. A core, deprecated authentication library needed refactoring. The task was paralyzing: Which services used it? How was it called? What was the blast radius of a change? Manually grepping through directories or asking ten different team leads was a multi-week endeavor they couldn’t afford .
“Sourcegraph gives us the ability to search for and refactor references to deprecated services, libraries, URL patterns, and more across our 2,000+ repositories, and the confidence that we’re not leaving anyone behind.” — Aneesh Agrawal, Software Engineer, Lyft
Their story mirrors challenges faced by giants like Lyft and CERN, who turned monolithic, tangled systems into manageable, modern codebases with the right tools .
Why Generic AI Assistants Fall Short on Tech Debt
The startup first tried popular AI coding assistants. While great for boilerplate or single-file suggestions, they hit a fundamental wall: isolation. These tools are typically limited to the context of your currently open files or a small workspace .
Tech debt, by its nature, is systemic. It’s the duplicated utility function copied across five different services. It’s the old API pattern lingering in dozens of forgotten endpoints. It’s the unused code hiding in a legacy module. An AI that can’t see these connections is like a doctor trying to diagnose an illness by looking at a single cell.
As one engineering team noted, this leads to AI suggestions that “seem correct in isolation but fail when integrated with existing systems” . To tackle debt, you need an assistant with a bird’s-eye view.
Enter Sourcegraph Cody: The AI That Knows Your Entire Codebase
Sourcegraph Cody is an AI coding assistant built on a different premise. It’s powered by Sourcegraph’s underlying platform, which indexes every repository in your organization—across GitHub, GitLab, Bitbucket, and more—into a single, searchable universe of code .
This is the game-changer. For our startup, integrating Cody meant their AI assistant was no longer blind. It could now:
- Answer questions about architecture: “How does the payment flow work between the user service and the billing service?”
- Find every instance of a pattern: “Show me all calls to the deprecated
log_legacy()function.” - Explain complex, unfamiliar code in seconds, dramatically accelerating onboarding and context sharing .
*Did you know? Leidos, a Fortune 500 defense contractor, chose Cody specifically for this “context-awareness,” which they found lacking in other assistants. Their rule of thumb became: “If you haven’t yet asked Cody, don’t ask me.” *
The 40% Reduction: A Three-Phase Action Plan
The startup’s journey to reducing tech debt wasn’t a single command. It was a methodical, three-phase strategy powered by Cody’s deep context.
Phase 1: The Audit – Making the Invisible Visible
You can’t fix what you can’t see. The team used Cody’s semantic and precise code search to run an automated audit of their biggest debt categories:
- Code Duplication: Searching for similar code blocks to identify candidates for new shared libraries.
- Deprecated APIs: Finding all usages of old libraries to plan migrations.
- “Orphaned” or Dead Code: Locating functions and modules with no references.
This audit, which might have taken months manually, was completed in days. They now had a data-driven, prioritized map of their tech debt.
Phase 2: The Refactoring – Safe and Scalable Changes
With targets identified, Cody shifted from detective to co-pilot.
- Understanding Impact: Before changing a core function, a developer could ask Cody, “Which services call this function, and how?” This precise impact analysis eliminated the fear of breaking unknown dependencies .
- Assisted Rewrites: Developers used Cody Chat to explain the new pattern they wanted (“Convert this class to use the new authentication client”) and get context-aware suggestions that respected the existing codebase structure.
- Automated Large-Scale Fixes (Batch Changes): For widespread changes—like renaming a universal function or updating an API schema—they used Sourcegraph’s Batch Changes. This tool lets you write a script once and automatically generate standardized pull requests across every affected repository, turning a Herculean manual task into a tracked, manageable campaign .
The result? Migration projects that once took a full sprint were 80-90% complete in a matter of hours .
Phase 3: The Culture Shift – Preventing Future Debt
The final step was embedding these capabilities into their daily workflow to prevent backsliding.
- Onboarding Accelerated: New hires used Cody to answer their own “how does this work?” questions, reducing the burden on senior staff by an estimated 75% (from 8 hours to 2 hours of guidance per week) .
- Code Reviews with Context: Reviewers used Cody to quickly examine how a proposed change might interact with other parts of the system they weren’t familiar with.
- Documentation, Generated: Cody was used to generate and update inline documentation and unit tests, improving long-term code health with minimal effort .
Quantifying the Win: Metrics That Matter
The impact transcended anecdotal evidence. The startup tracked key metrics before and after the Cody integration:
| Metric | Before Cody | After Cody | Improvement |
|---|---|---|---|
| Time to locate code/APIs | 15-30 minutes (manual search) | < 1 minute (semantic search) | ~95% faster |
| Time for large-scale refactor | 2-3 week sprint | 4-8 hours of focused work | ~90% faster |
| Senior Dev time spent on context questions | ~8 hours/week | ~2 hours/week | 75% reduction |
| Estimated overall tech debt burden | High (slowing all feature work) | Reduced by ~40% (based on velocity metrics) | Major velocity unlock |
Visualization of how Cody’s capabilities target and reduce the major components of technical debt over time.
FAQ: Your Tech Debt and Cody Questions Answered
1. Is Cody just another GitHub Copilot?
No, it serves a different, complementary purpose. Copilot excels at writing new code within a file. Cody excels at understanding and navigating all the code that already exists across your entire organization. They work best together: Copilot helps you write a function; Cody helps you find where to put it and what it might break .
2. We’re a small team. Is this overkill for us?
It’s about complexity, not just team size. If you have more than one repository, face onboarding delays, or spend time hunting for where things are implemented, Cody’s context search provides immediate value. Its ability to explain code can make every developer more self-sufficient from day one.
3. How does the security and data privacy work?
This is a critical differentiator. Cody Enterprise can be self-hosted, meaning your code never has to leave your private network or VPC. This is why it’s used by companies in defense (Leidos), finance (Coinbase), and security (Palo Alto Networks) .
4. What’s the learning curve like for the team?
Very low for basic use. Developers interact with it via a familiar chat interface in their IDE (VS Code, JetBrains). The powerful cross-repo search uses a sophisticated but learnable query language, though natural language questions via “Deep Search” are also available .
5. Can Cody actually reduce debt, or just help us find it?
It does both. Its core search helps you find and understand debt. When paired with Sourcegraph’s Batch Changes feature, it can automate the fixes at scale by generating pull requests across hundreds of repos, transforming discovery into direct action .
6. What are the limitations?
Cody’s effectiveness is tied to the quality of Sourcegraph’s index of your code. Extremely large or complex monoliths may take time to index fully. Additionally, while it supports a vast array of languages, some niche or proprietary languages may have limited functionality .
7. How do we measure our own tech debt reduction?
Start by tracking developer productivity metrics: time spent searching for code, time to complete refactoring tasks, and cycle time for new features. A reduction in these times is a direct proxy for reducing the “interest payments” on your tech debt.
The startup’s story reveals a fundamental shift: managing tech debt is no longer just about heroic, painful refactoring sprints. With an AI assistant like Sourcegraph Cody that has deep, cross-repository context, it becomes a continuous, integrated part of the development workflow. The 40% reduction in tech debt wasn’t just about cleaning code; it was about reclaiming weeks of lost productivity, accelerating feature delivery, and building a foundation for sustainable scale.
The question for engineering leaders in 2026 is no longer if they should use AI to manage complexity, but which AI has the contextual intelligence to understand their unique, sprawling codebase.
Has your team found an effective strategy for battling tech debt? What tools or processes have made the biggest difference? Share your experience in the comments.
References:
- [1] Sourcegraph Case Study: Cody + Leidos – Highlights Cody’s security, context-awareness, and impact on developer efficiency.
- [2] Sourcegraph Case Study: CERN Reduces Technical Debt – Details how universal code search helps avoid duplication and manage mission-critical changes.
- [4] Sourcegraph Blog: Why Code Search at Scale is Essential – Explains the architectural advantage of cross-repository search over standard AI assistants.
- [5] Sourcegraph Case Study: Lyft Monolith to Microservices – Showcases large-scale refactoring and tech debt elimination during architectural transition.
- [7] Sourcegraph Case Studies Hub – Lists numerous enterprise case studies, including Palo Alto Networks and Coinbase.
- [8] The AI Coding Tech Debt (LinkedIn Article) – Discusses the broader crisis of tech debt accelerated by AI coding tools.
- [9] How Cody’s Code Search Improves Productivity (LinkedIn Article) – Breaks down specific productivity metrics and time savings from Cody’s search.
