An agentic order automation layer, built for global wireless infrastructure.
A global leader in wireless communications infrastructure, with ~$4.3B in annual revenue, replaced a cancelled automation vendor with a platform that reads and processes orders roughly 10 times faster, without adding headcount.
A global leader in wireless infrastructure.
The company owns and operates towers, small cells, and fiber leased by major wireless carriers nationwide. It does not run networks, it builds and maintains the infrastructure networks run on.
Where the automation gap was heading.
The incumbent automation vendor cancelled its product. Without a replacement, every order those tools used to touch was headed back to manual entry.
Back to manual entry
Cancelling the incumbent automation meant reverting to entering orders by hand, undoing years of process investment in a single move.
Headcount pressure
Without a replacement, 3 people would need to be reallocated to order support, a direct headcount cost the business wanted to avoid.
A cycle that was already slow
Even with the prior automation running, average processing time was 57 minutes per order, and full cycle time (submit, process, review) ran 2 or more days, largely queue time from batching.
A bar the replacement had to clear
Whatever came next needed completeness and accuracy on par with the incumbent, and it needed to be faster, not just equivalent.
What the prior setup got right, and where it still fell short.
The incumbent automation worked. It just wasn’t fast, and it wasn’t going to exist much longer.
Match accuracy, not just speed
Extracted order data had to be as complete and correct as the outgoing system, not a faster but shakier replacement.
Remove the queue, not just the manual step
A 1-hour automation time was still producing multi-day cycles once batching and queuing were factored in; the fix had to address both.
Fit the existing systems of engagement
Work with the ticketing, RPA and site-management tools already in place, rather than requiring a new stack.
Avoid adding headcount to solve a software problem
Reassigning people to manual order support was the fallback, not the goal.
An agent that reads and processes orders automatically.
Lyzr built an agentic order automation layer: An Order Processing Agent that extracts and validates order data directly from documents, paired with a Business Rule Updater Agent that keeps configuration current.
Deployed inside the client’s own environment.
No new stack to stand up. Every Lyzr service runs in the client’s private subnets, on their approved models, behind their existing identity and monitoring.
The platform sits in multi-AZ private subnets, with its databases isolated in a subnet of their own.
What has to be true before an order moves.
The agent’s output has to match the accuracy of the system it replaces.
From 6 stages across 4 systems to one.
Before: 6 stages spread across 4 systems, with 3 separate automation runs and the request opened and closed by hand. After: one agent carries the order from intake to completion, reaching each system through connectors.
What the proof of concept showed.
Measured across 2025 and the start of 2026, against the prior automation baseline.
From one customer’s orders to the full account base.
The PoC validated accuracy and speed for the first customer account. Next: full production, consolidating legacy RPA and ticketing logic, extending to more carrier customers, adding revision handling for more order types, and moving toward a fully autonomous skill that picks up work from a queue and escalates only when needed.
Got a use case in mind?
Platform, engineers and governance all in. We’ll map your workflow against the same 4 tests this one had to pass.