When Success Breaks a Legacy System
When Success Breaks A Legacy System
A legacy system handled 1,000 daily orders for years. Slow but tolerable. Then the company lands two major B2B accounts and they’re processing 5,000. Congratulations to them and sympathy for their small IT team. The system that was “good enough” now times out during peak hours.
The .NET monolith shows the strain:
- Order processing locks the entire inventory table
- Distributor orders requiring multi-warehouse splits grafted onto single-location logic
- Enterprise customers’ approval workflows blocking simple orders
- Session state mixing standard and complex B2B contexts
Monday morning crashes aren’t from millions of users, it’s from a surge of distributor orders requiring inventory checks across three warehouses colliding with a whole new approval chain. The architecture assumes all orders follow the same simple path.
We’ve built tools to find bottlenecks systematically:
- Code symbol analysis traces sequences to conflicting databases writes
- Source analysis shows where async locks block order flow
- Stored procedure analysis identifies approval workflow/transaction conflicts
We feed this structured data into a knowledge graph that can provide class and sequence diagrams of the flows as well as perform pattern searches throughout the codebase. With that, the company’s in house experts can pinpoint and visualize the problem. There are three different workflows running through one sequence that does none of them well. “Distributor orders need multi-warehouse coordination. Enterprise orders need approval state machines. Standard orders need neither.”
Time to identify the specific areas to rewrite: hours. Instead of weeks slogging through code no one has or touched in four years. This isn’t AI magic. This is revealing the valuable context in legacy code and enabling capable people use AI to quickly locate and decipher it.
The performance problem wasn’t about scale. It was about forcing different business models through the same code path. AI can help identify where the “single” system is really three systems fighting for resources and then speed the existing team’s work fixing it.
