Building software is hard. Maintaining it for 12 years at scale is harder.
This is the story of how AI accelerated development in VALT, a mature, production-grade video streaming platform. Without rewriting it, without replacing developers, and without compromising engineering standards.
If you're leading a complex product and wondering whether AI actually improves software development speed, here’s a real-world breakdown based on hands-on engineering experience.
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Key Takeaways
- AI delivers its strongest value in reducing cognitive overhead rather than replacing engineering expertise. In mature systems, understanding context consumes more time than writing syntax. By accelerating analysis, navigation, and verification, AI creates measurable speed improvements without compromising standards.
- Productivity gains of 30-40% are realistic when AI is integrated thoughtfully into established engineering workflows. The impact compounds over time as repeated micro-accelerations accumulate.
- Most importantly, AI amplifies structured engineering culture. It does not compensate for its absence.
The System: 12 Years in Production
VALT is a browser-based video surveillance platform that allows users to securely watch and manage live video sessions. It connects up to 50 HD IP cameras per site, serves hundreds of video streams from a single server, and lets operators review and clip recordings directly in the browser.
Today, the system supports more than 50,000 active users across 770+ locations in US. The codebase has grown to roughly 130,000 lines, spread across dozens of modules and services. The core stack includes TypeScript and Vue.js on the frontend, PHP with Symfony on the backend, and Wowza Streaming Engine (Java) for video streaming.
This is not an early-stage startup product. It is a long-running, evolving platform with real constraints, real users, and real technical debt – even if that debt is controlled.
Before AI entered the workflow, the project was already well-maintained. The architecture was clean. Duplication was minimized through strict code review. Refactoring was part of the normal engineering routine.
That context matters. AI does not magically repair chaotic systems. It works best when it can navigate structure.
Where Development Time Was Actually Going
In large systems, writing code is rarely the slowest part.
The real time sink is understanding context.
Before implementing even a small feature, a developer must trace dependencies, examine permissions, understand execution flow, and verify how changes may affect other modules. When fixing a bug, most of the time is spent identifying where the logic breaks, not typing the final fix.
Code review also consumes significant effort, especially in teams where experience levels vary. Reviewing means mentally simulating logic, checking edge cases, and verifying architectural consistency.
These cognitive tasks were the primary bottleneck. That’s where AI created leverage.
How AI Was Integrated Into the Workflow
AI was not introduced as a one-off experiment for special cases. It became a regular tool embedded in daily development.
It is used for:
- Bug investigation
- Refactoring
- Writing new logic
- Reviewing pull requests
- Generating or improving documentation
- Understanding unfamiliar modules
Often, a clear task description from Jira is enough to start.
For example, a bug report stated:
“Duration Markers show Quick Clip button to users without Clip Right. Expected: user does not see the button. Actual: user sees it and gets 403 unauthorized.”
With access to the codebase, AI analyzed the permission flow and suggested a correction that resolved the issue.
No exaggerated prompt engineering. No elaborate instructions. Clear problem definition was sufficient.
This is an important point: precision in describing the problem often matters more than complexity in describing the solution.
Measurable Acceleration: What Really Improved
1. Bug Detection and Root Cause Analysis
The most noticeable improvement happened in bug investigation.
In a multi-layered system, tracing a faulty permission check or state transition can take significant time. AI reduces that search space. It can analyze related modules, infer likely failure points, and propose a solution to validate.
Developers still verify everything. But instead of manually scanning dozens of files, they start from a narrowed-down hypothesis.
That alone saves substantial time.
2. Context Exploration
Consider the addition of a new device type to the application.
Previously, about 30 minutes were spent understanding how device types are registered, validated, persisted, and exposed in the UI. Another 20 minutes were needed for implementation. Total: roughly 50 minutes.
With AI assistance, the entire task was completed in about 15 minutes.
The reduction did not come from typing faster. It came from compressing the orientation phase. AI mapped relevant code paths almost instantly.
When this happens repeatedly across tasks, the cumulative effect becomes significant.
3. Code Review Efficiency
Code review improved noticeably, particularly for contributions from junior developers.
AI helps surface edge cases, highlight architectural mismatches, and detect unnecessary complexity. It provides a second analytical pass before human review is finalized.
It does not replace the reviewer’s judgment. It augments it.
What Didn’t Change
AI did not eliminate responsibility.
Architectural decisions still require experience. Security still requires scrutiny. Performance still requires validation. Edge cases still need to be considered deliberately.
For large features affecting multiple layers of the system, AI typically provides direction rather than production-ready solutions. Sometimes it suggests technically working code that does not align with project conventions.
In those situations, iterative clarification resolves inconsistencies.
The key principle remains: every generated line must be understood.
Legacy Code and Refactoring
One subtle but important improvement is reduced psychological friction when entering unfamiliar parts of the codebase.
Instead of manually reading large classes to build a mental model, developers can ask AI to explain interaction patterns and data flows. This accelerates onboarding within the codebase itself.
Refactoring was already a routine practice before AI. The difference now is speed. Exploration takes less time. Risk assessment is quicker. Dead ends are identified earlier.
AI does not remove caution around legacy code. It simply reduces the time needed to gain clarity.
Overall Productivity Impact
When looking beyond individual tasks, the impact becomes clear.
Bug fixing accelerates. Feature development accelerates. Code review accelerates.
Across the board, productivity increased by approximately 30-40%.
If AI were removed tomorrow, development would continue – but noticeably slower. Context exploration would again dominate task time. Root cause analysis would require more manual tracing. Reviews would consume more cognitive energy.
The system would remain stable.
The team would simply move at a lower velocity.
FAQ: AI in Large-Scale Software Development
Does AI replace senior developers?
No. It increases their leverage. AI generates options and narrows search space. Engineers make architectural decisions.
Is AI safe for production systems?
Yes – if every suggestion is reviewed with the same rigor applied to manually written code. AI does not remove accountability.
Where does AI help the most?
Bug root cause analysis, context understanding, repetitive feature extensions, and code review show the strongest improvements.
Where does AI struggle?
Highly complex, cross-layer features with deep business rules require iterative prompting and careful human validation.
Does AI significantly improve code quality?
The primary improvement is speed. Quality gains come indirectly through better review support and earlier detection of edge cases.
Final Thoughts
AI does not revolutionize engineering overnight. It removes friction.
In large systems, friction accumulates in context switching, bug tracing, review cycles, and cognitive load. Reducing that friction by 30-40% materially changes delivery timelines over months and years.
Not through hype. Through consistent, incremental acceleration.
If you’re maintaining or building complex real-time platforms: video systems, communication tools, AI-enabled products, the question is not whether AI can write code.
It’s whether your architecture and engineering discipline allow you to benefit from acceleration.
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If you’re exploring ways to scale development without sacrificing engineering standards, we’ve been doing exactly that in production environments for years – reach out:
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