Predictive Analytics
Custom machine learning solutions to forecast business trends, reduce risk, and power proactive decisions at scale.
Smart Machine Learning Solutions. Implications for Practice.
We design and deploy bespoke machine learning solutions to automate complicated workflows, discover useful insights, and enhance decision making.
6x
Model Efficiency
70%
Reduction in Manual Work
50+
ML Systems Deployed
End-to-End Machine Learning Solutions
We build web apps, dashboards, APIs and internal tools with application development designed around your workflows and engineered to scale in production from day one.
ML Pipeline Automation
End-to-end MLOps: automated training, validation, deployment, and monitoring loops used in machine learning solutions running with no manual intervention.
Anomaly & Fraud Detection
Real-time detection machine learning solutions with ensemble models trained on your domain data and designed for financial, healthcare, and operational contexts.
Recommendation Engines
Personalized at scale: collaborative filtering, content-based, and hybrid machine-learning solutions, designed to drive the highest levels of engagement and revenue.
Computer Vision
Machine learning solutions for image classification, object detection, and visual QA deployed on-device or in the cloud for manufacturing, healthcare, and retail.
Data Systems & ETL
Scalable data architectures to clean, transform, and route structured and unstructured data to power every downstream machine learning solution system.
Our Approach
From Raw Data to Production Intelligence: Machine Learning Solution that
We don’t deliver notebooks; we deliver production machine learning solutions. Our team of machine learning engineers manages infrastructure, model serving, monitoring and continuous retraining to keep your models accurate as data drifts.
All engagements begin with a deep audit of your data landscape, business objectives and existing stack. We develop solutions that fit right in, no rip-and-replace.
Featured Engagement TensorFlow Predictive Anal
Cypress
6X
Model Efficiency
70%
Reduction in Manual Work
Cypress was losing hours daily to manual review cycles in their logistics workflows. We built a predictive ML engine trained on three years of operational data, capable of forecasting demand shifts and auto-routing decisions without human intervention.
D
David T.
Technical Lead, Cypress
Case Studies
Real Deployments. Measurable Outcomes.
A selection of production AI and ML projects across e-commerce, SaaS, professional services, and subscription businesses, each built to solve a specific, measurable business problem.
E-Commerce · United States
The Overwhelmed Spreadsheet: How a Small Online Store Eliminated Stockouts With ML
A small e-commerce owner was manually tracking inventory across three sales channels in spreadsheets, leading to constant overselling and stockouts. We built a lightweight ML model trained on six months of sales history that predicted reorder points automatically and flagged low-stock risks 48 hours in advance.
8 hrs
Saved Per Week
3
Channels Unified
48hr
Advance Warning
SaaS · United States
The Support Ticket Backlog: How a SaaS Startup Cut Response Time in Half With ML Routing
A three-person SaaS startup was drowning in repetitive support tickets, with the same 40 questions making up 70 percent of total volume. We built an ML classification model that automatically tagged and routed tickets by category and urgency, with suggested responses pre-loaded for common issues.
50%
Faster Response Time
70%
Tickets Auto-Tagged
Healthcare · United States
The Patient No-Show Problem: How a Medical Clinic Reduced No-Shows With Predictive ML
A small US medical clinic was experiencing 20 to 25 percent no-show rates on appointments with no way to predict which patients were most likely to miss. Staff were making blanket reminder calls the day before with no prioritization. We built a lightweight ML model trained on 18 months of appointment history that predicted no-show probability for each upcoming appointment and flagged high-risk bookings automatically for a second outreach 48 hours in advance.
↓
No-Show Rate Reduced
48hr
Early Flagging for High-Risk Appointments
E-Commerce Subscription · United States
The Churn Nobody Saw Coming: How a Subscription Box Company Got 2 Weeks of Early Warning
A US subscription box company was losing customers silently, with no visibility into who was at risk until after they had already cancelled. We built a churn prediction model on their existing Shopify and email engagement data that flagged at-risk customers two weeks before their likely cancellation date.
2 wks
Early Warning
↑
Retention Rate