Simor Consulting
Category: Case Study
A media analytics company running its entire data platform on AWS was spending $480,000 per month on cloud infrastructure. The bill had grown organically over three years as the platform expanded from
A specialty insurance firm underwriting commercial property policies received submission packets as PDF documents. Each packet contained an ACORD application, loss runs from prior carriers, a statemen
A subscription media company with 1.2 million subscribers built a machine learning model to predict churn. The model worked. It identified at-risk subscribers with seventy-nine percent precision and e
A healthcare conglomerate grew through acquisition for fifteen years. Each acquisition brought its own CRM. Salesforce in three divisions. Microsoft Dynamics in two. HubSpot in one. A custom-built CRM
At a mid-market insurance company with eight thousand employees, the Chief Data Officer and the Chief Technology Officer had fundamentally different views on how AI should be adopted. The CDO believed
A regional logistics company running three thousand deliveries per day across a six-state territory had a late-delivery rate of fourteen percent. The cost of a late delivery was not just the apology.
Ten years of analytics built on Oracle means ten years of accumulated PL/SQL, materialised views, database links, stored procedures, and ETL jobs that nobody fully understands. The schema has four hun
Most data catalogue projects die within six months. The tool gets purchased, a team populates it with metadata for a few hundred tables, enthusiasm fades, and twelve months later the catalogue is a st
An online travel agency processed 2.3 million flight searches per day. Each search triggered a pricing computation that determined the displayed fare for every matching itinerary. The pricing computat
A video streaming platform grew from 1,000 beta users to 10 million subscribers over thirty months. Their recommendation system was rebuilt three times during this period. Each rebuild was triggered n
An agriculture technology company built a crop yield prediction model that combined satellite imagery, soil sensor data, weather forecasts, and historical yield records. The model predicted per-field
A consumer goods company built an AI system that recommended reorder quantities for 12,000 SKUs across 340 distribution points. The system optimised for a multi-objective function that balanced invent
An insurance company running on an IBM mainframe had accumulated forty years of policy data in VSAM files and DB2 tables. The mainframe processed 600,000 transactions per day across policy administrat
A hospital system with twelve facilities and 14,000 clinical staff wanted to use large language models to assist with clinical documentation. Physicians spent an average of two hours per day on docume
A European fintech with twelve million customers received a GDPR audit notice from their national data protection authority. The audit focused on the company's machine learning pipeline, which powered
A global professional services firm with 8,000 consultants maintained institutional knowledge across forty-seven separate systems. Project proposals lived in a document management system. Client engag
A payment processor handling twelve million transactions per day had a fraud detection system that was accurate but slow. The system reviewed transactions in batch, four times per day. A fraudulent tr
A healthcare analytics company received notice on a Tuesday afternoon that their primary AI infrastructure vendor was filing for Chapter 7 bankruptcy. The platform hosted their patient risk stratifica
An insurance company with $400 million in premium volume adopted data mesh two years ago. The central data team had become a bottleneck. Every business unit (claims, underwriting, actuarial, and distr
A mid-size automotive parts manufacturer with operations spanning 15 countries and relationships with over 200 suppliers faced a supply chain coordination problem that was consuming too much of their
A retail chain with 400 stores spent two years and $2.1 million building an inventory optimisation model. The model was technically excellent. It reduced predicted stockouts by thirty-two percent and
A regional bank with $12 billion in assets wanted to use machine learning to improve its commercial loan underwriting process. The existing process was manual, relying on credit analysts who spent fou
A B2B SaaS company running a customer success platform had a data pipeline that consumed sixty percent of the data engineering team's time. Not feature work. Not analytics. Pipeline maintenance. The p
A diversified industrial company with 10,000 employees across manufacturing, logistics, and field services had accumulated forty-seven separate AI projects over three years. Each business unit had bui
A media company with a library of twelve million articles, transcripts, and research documents had built a semantic search system on a managed vector database. The system was designed to let journalis
A manufacturing company with facilities in twelve countries ran its operational reporting on a traditional BI stack: a data warehouse, an ETL pipeline, and a dashboard tool that had been deployed six
A legal technology company had invested six months building a retrieval-augmented generation system to help contract attorneys find relevant precedent clauses across a corpus of 180,000 executed agree
A logistics company processing two million shipments per day ran their entire operational reporting stack on nightly batch ETL. Every morning at 6 AM, operations managers reviewed dashboards built on
A financial services firm running analytics on trade settlement data came to us with a specific complaint: their cloud data platform cost had tripled in eighteen months, and nobody could explain why.
A mid-market e-commerce retailer with roughly $200M in annual revenue had invested eighteen months building a product recommendation engine. The models were accurate. Offline evaluation showed meaning
A regional bank's investment research team spent 60% of their time gathering information and 40% doing analysis. Analysts had to search through regulatory filings, internal research memos, market data
A SaaS company with 200 support agents and 10,000+ knowledge base articles had an 18-hour average response time and 23% first-contact resolution. Their largest enterprise client threatened to cancel a