The Problem
Aurora had invested heavily in an ERP system, but most critical processes still relied on offline spreadsheets and ad‑hoc scripts. Planners exported data to Excel to forecast, finance teams re‑entered invoice details, and sales managers kept their own shadow pipeline trackers. Data was duplicated, out of sync, and hard to trust.
Three pain points kept coming up in every conversation:
Forecasts were consistently off, creating both stockouts and dead stock in different regions.
Month‑end closing was slow because exceptions, disputes, and credit notes had to be reconciled manually.
No one had a real‑time view of what was happening; reports were always “as of last week”.
The cost of this friction was real and visible:
$1.2M
a year lost to expedited shipping, emergency purchase orders, and penalties for missed SLAs.
$1.2M
a year lost to expedited shipping, emergency purchase orders, and penalties for missed SLAs.
$1.2M
a year lost to expedited shipping, emergency purchase orders, and penalties for missed SLAs.
Aurora’s CTO summed it up during the first workshop: “The ERP is full of good data, but it behaves like a slow archive instead of the nervous system of the company.”
The Solution
Avannte and Aurora agreed early that success meant upgrading how the ERP behaved, not replacing it. The team designed an AI‑enabled operations layer that sits on top of the existing system, continuously watching transactions, learning patterns, and intervening only when it can add real value. Instead of another reporting project, the focus was on turning key workflows into semi‑autonomous “lanes” that the business could trust.
At the core of the solution is an agentic AI layer with three capabilities working together: understanding demand, protecting data quality, and closing the books faster. A Demand Intelligence agent consumes years of order history, seasonality, promotions, and open quotes to generate weekly forecasts per SKU and region, then pushes recommended adjustments straight into the planning screens. A second agent patrols new orders as they are created, checking pricing, terms, and mandatory fields in real time so bad data never enters the pipeline. A third agent continuously reconciles invoices, shipments, and payments, flagging mismatches early and assembling all related records into a single, review‑ready view.
Crucially, all of this runs inside the tools Aurora’s teams already use every day. Planners still work in the ERP; they just see pre‑filled forecasts and clear anomaly alerts. Sales operations still create orders, but obvious mistakes are fixed instantly and edge cases arrive as guided tasks, not mystery errors. Finance still owns approvals and judgment calls, yet the reconciliation agent does the hunting and gathering for them. The result is an ERP that behaves less like a passive database and more like a fleet of quiet specialists, each one focused on keeping a different part of the workflow flowing.
The Impact
$1.2M annual savings
Operational clarity on day one
Within a few weeks of going live, planners stopped exporting data to spreadsheets just to understand what was happening. The new demand and order‑quality signals surfaced directly inside the ERP screens they already used, so everyone—from procurement to sales—was finally looking at the same, current truth. Daily stand‑ups shifted from arguing about numbers to agreeing on actions, because the system itself could explain why it was recommending a change.
18% fewer manual touches per order
Time back for the people who know the business best
By offloading checks, reconciliations, and routine status chasing to agents, Aurora gave entire teams hours back every week. Planners used that time to renegotiate supplier terms and explore “what‑if” scenarios; finance focused on margin and risk instead of hunting for mismatched lines. Morale improved noticeably: instead of feeling like human middleware for the ERP, people were using their judgment and creativity where it mattered.
2.5 days faster month‑end close
A blueprint they can keep reusing
The biggest win was not just the first set of automations, but the pattern they created. Once leaders saw a working combination of AI agents, ERP integrations, and human approvals, they began to ask, “Where else can we apply this?” Aurora is now extending the same architecture to returns processing, warranty claims, and partner incentives—spinning up each new use case faster because the foundation is already in place.
