A water utility built a digital twin of their distribution network and predicted a pipe failure 11 days before it happened, preventing a $2.3 million emergency repair and service disruption to 40,000 customers.
A regional hospital network needed to extract structured data from 2.3 million unstructured clinical notes. Here is exactly how we scoped, built, and delivered the NLP system, and how your agency can do the same.
A fintech company replaced their manual compliance reviews with AI monitoring and caught 340% more violations while reducing compliance staff costs by 45%. Here is how to build systems regulators actually respect.
Most enterprise AI sits locked in the team that built it. An internal model marketplace makes AI accessible to every business unit, and your agency is perfectly positioned to build it.
A private equity firm needed to analyze 3,400 portfolio company documents monthly. Manual review was consuming 240 analyst hours. Here is how we built the NLP system that cut it to 35.
Your client's model is accurate. Now it needs to serve 10,000 predictions per second at under 50ms latency without bankrupting the company. Here is the complete inference optimization playbook.
A DTC skincare brand was spending $6.4 million on marketing with no idea which channels actually drove sales. Our marketing mix model revealed 31 percent of their budget was wasted.
Feature engineering consumes 60 percent of ML development time. A feature platform cuts that to 15 percent. Here is how your agency delivers feature platforms that transform ML team productivity.
A Fortune 500 company with 147 ML models in production had no centralized visibility into model risk. A governance platform reduced model-related incidents by 73% while cutting model approval time from 8 weeks to 12 days.
A fintech agency built a table extraction system that processes 40,000 financial statements monthly with 96% cell-level accuracy, replacing a team of 18 data entry specialists. Here is the delivery guide.
Knowledge Distillation for Deploying Smaller Models: The Agency Efficiency Guide A retail analytics agency in Boston built a product recommendation engine for a major grocery chain...
Delivering Production Sentiment Analysis Systems: From Prototype to Pipeline A consumer electronics brand hired a three-person AI agency in Austin to build a sentiment analysis sys...
A healthcare AI agency built a speech-to-text system processing 8,000 clinical dictations daily with 97% accuracy on medical terminology. Here is the complete delivery playbook.
A legal tech agency replaced a law firm's keyword search with semantic search, reducing average research time from 47 minutes to 8 minutes per query across 3.8 million case documents.
An agency managing 23 production models for 8 clients had no model registry. A rollback that should have taken minutes took 11 hours. Here is how to build the registry that prevents that nightmare.
An e-commerce agency built a multimodal search system where customers search with photos, screenshots, or text and find matching products across 2.8 million items. Here is the delivery playbook.
Building Intelligent Document Extraction Systems: The AI Agency Blueprint A commercial insurance underwriter was drowning in paper. Each new policy application required reviewing 1...
Delivering Personalization Engines at Scale: The AI Agency Blueprint An online fashion retailer with 3.2 million monthly visitors and 180,000 SKUs was showing the same homepage and...
An agency built a QA system for a 50,000-employee enterprise that answers 12,000 internal questions daily with 91% accuracy, replacing a search-and-read workflow that averaged 23 minutes per question.
An agency deployed a vision inspection system at an electronics manufacturer that catches 99.2% of defects at 300 units per minute, replacing a manual process that caught only 87%. Here is the delivery guide.
Event-Driven Architectures for AI Applications: How Agencies Deliver Reactive Intelligence An e-commerce agency in Seattle built a product recommendation engine for a fashion retai...
An ML agency watched their client's churn prediction model silently degrade from 88% to 61% accuracy over four months. Here is how to build drift detection that catches problems early.
Building Scalable Data Labeling Pipelines: The AI Agency Operations Guide An AI agency in Chicago was building a medical document classification system for a health insurance compa...
Experiment Tracking and Reproducibility Best Practices for AI Agencies A two-person AI agency in Portland had a conversation that every agency has had at least once. The client cal...
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