Not every automation needs an LLM. A B2B business was receiving purchase orders from retailers through SPS Commerce.
Someone on the team then checked stock by hand, retyped each order into Sage 50 as a sales order, and kicked off fulfillment.
We built a platform on ASP.NET (http://ASP.NET) Core and C# that handles it end to end.
→ Purchase orders come in through SPS Commerce automatically
→ Stock and order details are validated before anything is created
→ Sales orders land in Sage 50 without retyping
→ Anything odd is flagged as an exception instead of quietly slipping through Less manual work across the whole order lifecycle, cleaner data, and orders that keep moving without anyone copying fields between screens.
I build a lot of AI agents now. But the first question on every scoping call is still the same.
Does this need judgement, or does it just need wiring? Half the time it's wiring.
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The system architecture uses a robust tech stack for a high-performance, scalable web app dashboard. React enables efficient UI development with its component-based approach. Next.js, built on React, offers server-side rendering for faster initial loads. Microsoft IIS ensures reliable, secure deployment, while SQL Server provides structured, efficient data management.
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Most AI features never leave the demo.
The model is rarely the problem. It's everything around it: messy data, old .NET code nobody wants to touch, and no clear answer to "what does this actually save us?"
On CasePath we skipped the chatbot and aimed AI at one job caseworkers hated: writing case notes.
Result: 90% less documentation time. Supervisor decisions 83% faster. 20% more caseload with zero new hires.
What made it work:
Pick one painful task, not a whole "AI strategy"
Put AI inside the app people already use
Measure hours saved before writing a line of code
If you run a .NET product and AI is on your roadmap, message me. One call and I'll tell you where it'll pay off, and where it won't.
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