Case Studies
Real problems, measurable results.
A look at automation and AI solutions built on decades of hands-on experience inside live telecom networks and real business operations — each one turning a costly problem into a measurable outcome.
Case Study 01
Stopping revenue leakage before it happens.
Tier-1 telecom operator · Real-time revenue assurance
Roaming data volume — live
Top roaming partners by revenue
Revenue lost before anyone noticed
The operator was losing revenue whenever a network outage caused a dip in data volume from its top five business partners. The leakage was real and recurring — but by the time the team spotted it, the money was already gone. They needed to see a dip the moment it happened, so they could act before it cost them.
A live, alerting early-warning system
We connected a live data feed into a real-time monitoring dashboard, visualizing partner data volumes as they happened. On top of it, we built an automated alerting mechanism: the instant volume dipped below expected levels, the system fired an alert — turning a hidden, after-the-fact loss into an immediate, actionable signal.
From reactive to proactive
The operations team shifted from discovering losses hours later to resolving issues the moment they emerged. The system became a business-critical part of how the operator protects revenue from its most important partners.
Real-time
Dips detected the instant they happen, not hours later.
Proactive
Issues resolved before they turn into revenue loss.
Business-critical
Now a core part of the operator's revenue assurance.
Case Study 02
Accurate geolocation across 50,000 cells.
Tier-1 telecom operator · Network data integrity at scale
Network — cell feeds
Automated Engine
Geolocation tool
Missing feeds, silently breaking accuracy
The operator's geolocation system depended on a live feed from every cell in the network — its accuracy relied on complete data across all of them. But with around 50,000 cells, whenever a cell stopped sending its feed, the gap went unnoticed. Results quietly became less accurate, and there was no practical way to spot which cells had gone silent among tens of thousands.
An automated integrity layer in the data path
We built an automated monitoring system positioned between the OSS and the geolocation tool — continuously watching the live feed across the entire network and identifying exactly which cells had stopped sending data. It publishes an updated list of missing cells every 15 minutes, turning a network-wide blind spot into a precise, always-current watchlist.
Complete data in, accurate results out
The team can now pinpoint feed gaps the moment they appear and trace each one — determining whether it's an OSS issue or a geolocation-tool issue — and restore it fast. With complete data flowing in continuously, the geolocation system delivers the accurate results the customer depends on.
Full-network feed integrity, monitored continuously.
Missing-feed watchlist, always current.
OSS vs. tool issue identified before results degrade.
Case Study 03
100 products live in 30 minutes.
Online retailer (WooCommerce) · AI-powered catalogue automation
Product image
Live in store
Manual product entry was a growth bottleneck
Adding products to the store was painfully slow. Each product carried around 15 variations and took roughly 30 minutes to enter by hand — writing descriptions, keying in details, and building out every variation. For a growing catalogue, that turned into days of repetitive work and a hard ceiling on how fast the business could scale.
AI that does the data entry for you
We built an AI-powered tool that removes the manual work entirely. It analyses a product image, automatically generates the product description, and adds the product — variations and all — directly into WooCommerce, with pricing applied from predefined rules. No copywriting, no repetitive form-filling, no manual entry.
Days of work, done in half an hour
100 products were added in just 30 minutes — work that would have taken around 50 hours by hand. The bottleneck disappeared, manual errors were eliminated, and the store could grow its catalogue as fast as it could photograph products.
From 30 minutes per product to seconds.
No copywriting or repetitive form-filling.
Catalogue scales as fast as products are shot.
Have a challenge like these?
Tell us what's slowing your operations down — we'll help you find the fastest path to a measurable result.
Let's talk →