Evolved360 AI
Models That Learn From
Your Data — Not Generic Data.
Custom ML Models. Predictive Analytics. Classification and Pattern Recognition.
Generic AI tools are trained on generic data. The highest-value ML applications are built on your operational data — the patterns specific to your customers, your products, and your processes that no off-the-shelf model can replicate. We build and deploy custom ML models that learn from what makes your business distinctive.
Your ML Partner
Your business generates data that could be predicting outcomes. Most of it sits unused.
Transaction history, customer behavior, equipment sensor data, support ticket patterns — these are the inputs to ML models that can predict churn before it happens, flag anomalies before they become incidents, and classify inbound requests before a human reads them. The data exists. The models that learn from it need to be built, validated, and deployed in a way that integrates with your operational systems.
60–80%
Decision accuracy improvement
10+ yrs
AI implementation experience
90 days
Typical first model deployment
SOC 2
Type 2 certified team
What Changes
What business decisions look like when models are doing the pattern recognition.
Predictions Before Problems
Churn prediction, equipment failure forecasting, demand planning — models that identify what's likely to happen next so you can act before it does.
Automated Classification
Support tickets routed by intent before a human reads them. Transactions flagged by fraud probability in milliseconds. Documents categorized without manual review.
Pattern Recognition at Scale
Detect the non-obvious correlations in your data that humans can't see at volume — the customer behavior that precedes a large purchase, the sensor reading that precedes a fault.
Models That Improve Over Time
ML models trained on your historical data continue improving as new data accumulates. Unlike rule-based systems, they adapt to changing patterns without manual reconfiguration.
What We Build
ML applications built for specific, measurable business problems.
Predictive Models
Regression and time-series models that forecast demand, revenue, equipment failure, and customer behavior using your historical operational data.
Classification Systems
Models that categorize inputs — customer segments, support intent, fraud probability, document type, or product recommendations — with accuracy that scales with volume.
Anomaly Detection
Statistical and neural network models that identify outliers in operational data — unusual transactions, equipment readings outside normal ranges, network behavior anomalies.
Natural Language Classification
Text classification models for routing, sentiment analysis, and intent detection built on your domain-specific vocabulary and customer language patterns.
Recommendation Engines
Collaborative and content-based filtering models for product recommendations, content personalization, and next-best-action systems trained on your catalog and customer behavior.
MLOps & Model Management
Production deployment, performance monitoring, drift detection, and retraining pipelines that keep models accurate as data patterns change over time.
The first ML model we deploy for clients typically shows measurable accuracy improvement over manual processes within 30 days of production operation.
Book ML AssessmentFull AI Coverage
ML models deliver more value connected to your full AI stack.
We integrate ML outputs into your operational systems — automation workflows, dashboards, APIs — so model predictions drive action rather than sitting in a report.
What Changes
What operations look like when models are finding what humans miss.
Client result
“We were losing 12% of our recurring customers every quarter and had no idea who was at risk until they cancelled. The churn prediction model flags accounts 45 days out. Our customer success team now works a prioritized list of at-risk accounts. Retention improved by 35% in the first six months.”
VP Customer Success · SaaS Company · ETG client since 2023
The Case for Custom ML
Why generic AI tools produce generic results — and when custom ML is the right answer.
The question isn't whether to use AI — it's whether the AI is trained on data that reflects your actual business. A customer churn model built on generic SaaS industry benchmarks will underperform compared to a model trained on your specific customer segments, product usage patterns, and contract structures. The patterns that predict churn in your business are almost certainly different from the patterns in the training data of an off-the-shelf tool.
Custom ML development starts with a data audit — not a technology selection. What data do you have, how clean is it, what outcome do you want to predict or classify, and what would it be worth if the model was accurate 80% of the time? Most ML projects fail because they start with a technology choice and work backward to justify it. The projects that succeed start with a specific, measurable business problem and scope the model to solve that problem precisely.
The production deployment is where many ML projects stall. A model that runs in a Jupyter notebook is not a deployed ML system. Production deployment requires an API wrapper, integration with operational systems, performance monitoring, drift detection when input patterns change, and a retraining pipeline when model accuracy degrades. We build and deliver all of these components as part of every ML engagement — not as separate line items.
“The most common ML mistake I see is scoping a project around the technology — 'we want to do machine learning' — rather than the problem. The second most common is treating model development as the finish line. A model that isn't integrated into operational systems isn't delivering value. It's a proof of concept that never shipped.”
Kevin Nishimura, CTO — Evolved Technology Group · SOC 2 Type 2 Certified · 10+ Years AI Implementation
Common Questions
Frequently asked questions.
Ready to build a model that learns from your data specifically?
Book a free ML assessment. We'll review your available data, identify the highest-value prediction or classification problem to solve first, and show you what a production ML deployment timeline looks like.