Case Studies
What engineer-led AI actually looks like in production.
Representative engagements showing real workflows, real systems, and real results — from AI Discovery Sprint through production go-live.
PO Exception Handling — Specialty Chemical Distributor
180-seat specialty chemical distributor, Midwest · ~$120M revenue · SAP ECC 6.0 · Procurement team of 9 FTEs
Representative engagement — results based on typical AI Discovery Sprint + Pilot Implementation outcomes.
The Problem
Roughly 35% of inbound purchase orders required manual exception handling — mismatched unit-of-measure codes, vendor part number discrepancies, and freight terms that didn't map cleanly to SAP line items. Each exception averaged 22 minutes of analyst time, consuming ~23 staff-hours daily. A prior RPA initiative stalled after discovery revealed the ECC data model was too brittle for standard bot tooling.
What We Built
After a 3-week AI Discovery Sprint mapping the top 6 exception categories against SAP BAPI endpoints, we executed a Pilot Implementation: an AI layer that intercepts PO exceptions before they hit the analyst queue, cross-references vendor data, and either resolves autonomously or routes with a pre-filled correction proposal — integrated directly into the existing SAP workflow inbox. No UI changes for end users, no middleware replacement.
Results
Freight Invoice Reconciliation — Regional 3PL
Regional 3PL, 4 DCs, Southeast · ~240 employees · Manhattan Associates WMS (on-prem, 2014) + QuickBooks Enterprise · 6-person billing team
Representative engagement — results based on typical AI Discovery Sprint + Pilot Implementation outcomes.
The Problem
Freight invoice reconciliation was almost entirely manual: each carrier invoice had to be matched against WMS load confirmations, then validated against a contracted rate card in Excel. Discrepancies — on ~28% of invoices — required back-and-forth with carriers and manual journal entries in QuickBooks. The process consumed ~26 staff-hours per week. A Power Automate pilot failed due to variability in carrier invoice formats (PDFs, EDI 210s, email attachments).
What We Built
The AI Discovery Sprint (4 weeks) identified document intake as the primary bottleneck. We executed a Pilot Implementation combining a document intelligence layer (handling PDF, EDI, and structured email extraction) with a reconciliation engine that queries the WMS API and rate card logic. Matched invoices post automatically to QuickBooks via SDK. Exceptions surface in a lightweight review UI with discrepancies pre-annotated and carrier contact pre-populated.
Results
Your Engagement
Every engagement starts with an AI Discovery Sprint.
2–3 weeks. Fixed fee. A prioritized roadmap with ROI estimates before a single line of production code is written.
We reply within 1 business day.