AI Engineering Case Studies.
Discover how we design, build, and deploy complex AI architectures and data science models to solve critical enterprise challenges across the United States.
Predictive Analytics Model for US Retail Giant
The Challenge
A national retailer was facing a 14% stockout rate during peak seasons due to inaccurate historical forecasting models.
The Solution
We engineered a deep learning predictive analytics pipeline using PyTorch, integrating real-time weather data, social sentiment, and POS historicals. The model was deployed on private AWS architecture.
Measurable Impact
Reduced stockouts by 82% in the first quarter and decreased warehouse holding costs by $2.4M annually.
Custom LLM Architecture for Corporate Law Firm
The Challenge
A Chicago-based law firm needed to automate contract review but could not use public APIs (like OpenAI) due to strict client confidentiality NDAs.
The Solution
Fine-tuned an open-source 70B parameter model (Llama-3) on the firm's private historical contract database. Deployed entirely on secure, on-premise servers with a custom chat interface.
Measurable Impact
Reduced initial contract review time from 4 hours to 12 minutes, maintaining 100% data sovereignty and compliance.
Defect Detection via Computer Vision
The Challenge
An automotive parts manufacturer relied on manual QA, leading to a 3% defect escape rate and severe SLA penalties.
The Solution
Installed high-speed industrial cameras running a custom Convolutional Neural Network (YOLOv8 variant) optimized for edge deployment via TensorRT. The system processes 120 frames per second on the assembly line.
Measurable Impact
Achieved 99.98% defect detection accuracy in real-time, eliminating SLA penalties completely within 30 days of deployment.
Scalable NLP Customer Support Automation
The Challenge
A SaaS company scaling rapidly experienced a 400% increase in tier-1 support tickets, causing response times to drop to 48 hours.
The Solution
Deployed a customized version of SNAT Chatbot AI integrated directly with their Zendesk and internal knowledge base. Implemented semantic search for dynamic answer generation.
Measurable Impact
Automated 68% of all tier-1 queries immediately. Average human response time dropped to under 2 hours for complex escalations.
Hybrid AI + VA Operational Scaling
The Challenge
A real estate brokerage needed to process 5,000+ inbound leads monthly but could not justify the overhead of hiring 20 full-time admin staff.
The Solution
Built an automated data-extraction pipeline connected to SNAT Workflow AI, managed by a dedicated pod of 4 highly-trained SNAT Virtual Assistants.
Measurable Impact
Scaled lead processing capability by 10x while saving the brokerage over $800,000 in projected annual payroll costs.
Dynamic Supply Chain Optimization
The Challenge
A logistics company struggled with inefficient routing and fuel waste across a fleet of 500+ vehicles.
The Solution
Implemented SNAT Reach AI powered by a custom reinforcement learning algorithm that dynamically recalculates routes based on live traffic, vehicle load, and driver hours.
Measurable Impact
Cut fuel consumption by 18% fleet-wide and increased daily delivery capacity by 12%.
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