Simor Consulting
Category: Case Study
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 dist
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 optimization 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