Case Studies
Results we have delivered
Real outcomes across manufacturing, finance, healthcare, logistics, and professional services. Every client anonymized per NDA. Every result verified.
Real-Time OEE Dashboards Eliminate Plant Floor Blind Spots
The Situation
A mid-size plastics manufacturer was running production reporting from spreadsheets updated once per shift. By the time leadership saw downtime causes and scrap rates, the shift was over and the damage was done. Decisions that needed to happen at 8am were happening at 5pm - or not at all.
Our Approach
We connected directly to their IQMS production database via a read-only SQL Server replica, built a semantic layer in Power BI with machine-level OEE breakdowns, and deployed live displays on the plant floor updated every 15 minutes. Shift managers received automated summary emails at the end of each shift.
Key Results
Technologies
Legacy ERP Migrated to Cloud Without a Day of Production Downtime
The Situation
A Tier 2 automotive supplier needed to migrate 10 years of production and quality data from an aging on-premises SQL Server to Azure - without disrupting daily operations or missing a single customer shipment. Their IT team had attempted the migration once and aborted after discovering data integrity issues mid-process.
Our Approach
We started with a full data audit before touching anything. We built automated reconciliation scripts that compared source and target tables nightly, ran parallel systems for 45 days, and cut over during a planned holiday shutdown window. Power BI was connected to Azure SQL before the first user logged in on go-live day.
Key Results
Technologies
Python Automation Eliminates 20+ Hours of Manual Reporting Per Week
The Situation
A regional IT services firm was spending over 20 hours every week manually pulling client utilization data from three separate ticketing and billing systems, reconciling the numbers in Excel, and formatting reports for each client. The process was slow, error-prone, and occupying two senior people who should have been doing analysis, not data assembly.
Our Approach
We built Python scripts that connected directly to each system's API, applied the firm's reconciliation logic in code, generated formatted Excel and PDF outputs per client, and scheduled delivery automatically. The entire pipeline runs overnight. No human involvement required unless an anomaly is flagged.
Key Results
Technologies
Month-End Close Compressed from 5 Days to Under 24 Hours
The Situation
A regional financial services firm spent the first five business days of every month manually consolidating branch-level reports from six locations into a single corporate view. During close, the senior finance team was unavailable for any actual analysis - they were assembly workers. Leadership was making strategic decisions on data that was always at least a month old.
Our Approach
We mapped the entire close process step by step, identified every manual transformation, and rebuilt each one as a Python function with full audit logging. Branch data now flows into a central staging database automatically. The consolidation, reconciliation, and variance analysis runs overnight on the first of the month. Finance reviews the output instead of building it.
Key Results
Technologies
Twelve Disconnected Reports Replaced by One Unified Leadership Dashboard
The Situation
A multi-location healthcare provider was running 12 separate reports across clinical, scheduling, billing, and HR systems. No single view of the organization existed. Leadership spent the first hour of every executive meeting reconciling numbers that did not agree. Decisions on staffing, capacity, and billing were being made on incomplete or stale information.
Our Approach
We audited all 12 reports, mapped their data sources, and identified the four systems that held the authoritative data for each metric. We built a staging layer that consolidated everything nightly, defined a single agreed calculation for each KPI, and delivered one unified Power BI dashboard with drill-through access to each underlying source.
Key Results
Technologies
Real-Time Delivery Performance Visibility Cuts Customer Escalations by Half
The Situation
A regional distributor had no visibility into on-time delivery performance until customers called to complain. By the time issues were identified internally, they had already become relationship problems. The operations team was reactive by design - the data existed in their TMS and ERP but nobody had connected it to a view that operations could act on.
Our Approach
We built a Python pipeline that pulled shipment status from their TMS API every 15 minutes, compared it against committed delivery windows from the ERP, and surfaced at-risk shipments in a live Power BI dashboard. Carrier performance rankings updated automatically each week. Alerts fired to the operations team when a shipment crossed a defined risk threshold.
Key Results
Technologies
Confidentiality
Every client engagement is covered by a mutual NDA
We do not name clients, share identifying details, or use your work in our marketing without explicit written permission. The case studies above reflect real outcomes with identifying information removed. The same protection applies to your engagement from day one.
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