Real Results from Real AI Projects
See how we help companies transform their operations with production-ready AI solutions that deliver measurable business impact.
$9.3M+
Annual Client Savings
4
Industries Served
97%+
Model Accuracy
NovaPay Technologies
FinTech
The Challenge
NovaPay was processing over 2 million transactions daily, but their legacy rule-based fraud detection system was flagging 12% of legitimate transactions as suspicious. This led to frustrated customers, increased support costs, and an estimated $3.2M in annual revenue lost to false declines.
Our Solution
We built and deployed an AI-powered fraud detection model using gradient-boosted decision trees and real-time behavioral analytics. The system analyzes 150+ transaction features in under 50ms, including device fingerprinting, velocity checks, and graph-based relationship modeling. We integrated the solution directly into their existing payment pipeline with zero downtime migration.
Key Results
60%
False positives reduced
99.2%
Fraud catch rate
<50ms
Processing latency
$2.1M
Annual savings
“RevolutionAI transformed our fraud detection from our biggest customer complaint into a competitive advantage. The 60% reduction in false positives was immediate and has held steady for over a year.”
Sarah Chen
CTO, NovaPay Technologies
MedBridge Health Systems
Healthcare
The Challenge
Clinical staff across 12 departments were spending an average of 3 hours per day manually processing, categorizing, and summarizing patient documentation. This administrative burden was contributing to physician burnout and delaying patient care coordination between departments.
Our Solution
We developed a HIPAA-compliant clinical document AI system using fine-tuned large language models with retrieval-augmented generation (RAG). The platform automatically extracts key clinical data, generates structured summaries, and routes documents to the appropriate departments. All data remains within their private cloud infrastructure with full audit logging.
Key Results
15 hrs
Time saved per dept/week
97.8%
Document accuracy
12
Departments onboarded
$840K
Annual cost savings
“Our clinicians got 15 hours a week back to focus on patient care instead of paperwork. The AI summaries are so accurate that our staff trusts them completely. This is the most impactful technology investment we have made in a decade.”
Dr. James Okafor
Chief Medical Information Officer, MedBridge Health
CartFlow Commerce
E-commerce
The Challenge
CartFlow's existing recommendation engine was based on simple collaborative filtering, surfacing generic popular products. With a catalog of 50,000+ SKUs, customers were struggling to discover relevant products, resulting in a stagnant average order value of $67 and high cart abandonment rates.
Our Solution
We designed and deployed a hybrid recommendation engine combining deep learning embeddings, real-time session behavior, and contextual signals like time-of-day and seasonal trends. The system generates personalized product recommendations across the homepage, product detail pages, and checkout flow. A/B testing was baked into the architecture for continuous model optimization.
Key Results
35%
Average order value increase
+48%
Click-through rate
22%
Cart abandonment reduced
$4.8M/yr
Revenue lift
“The new recommendation engine pays for itself every single week. Our average order value jumped 35% in the first quarter, and customers are telling us the shopping experience feels like it was made just for them.”
Maria Santos
VP of Product, CartFlow Commerce
Steelwright Industries
Manufacturing
The Challenge
Steelwright operated 340 critical production machines across 3 facilities. Unplanned equipment failures were causing an average of 72 hours of downtime per month, costing over $180K per incident in lost production, emergency repairs, and spoiled materials.
Our Solution
We implemented an IoT-integrated predictive maintenance platform using sensor data from vibration monitors, thermal cameras, and power consumption meters. Time-series ML models detect anomalies and predict failures 2-3 weeks before they occur. The dashboard provides maintenance teams with prioritized work orders and estimated remaining useful life for each machine.
Key Results
45%
Unplanned downtime reduced
94%
Prediction accuracy
340
Machines monitored
$1.6M
Annual savings
“We went from reacting to breakdowns to predicting them weeks in advance. The 45% reduction in unplanned downtime was a game-changer for our production targets and our maintenance team's morale.”
Robert Kim
Director of Operations, Steelwright Industries