Engineering™ · Data & Intelligence

Machine Learning & Predictive Analytics

We turn historical business data into predictive machine learning models that anticipate behaviors and protect margins.

Churn Prediction · Demand Forecasting · Predictive Lead Scoring · Anomaly Detection. Statistical models trained on your proprietary data and deployed via low-latency operational APIs.

Request diagnosis

See how it works

Scope

Predictive ML Model Training · Feature Store Engineering · Production Model Serving API · Continuous Drift Monitoring

Estimated Timeline

8–12 weeks

Platforms

Python (Scikit-Learn / XGBoost) · MLflow / Vertex AI · Docker / FastAPI · Cloud GPUs/CPUs

Target Fit

Is this for your company?

This service is for you if

✓You lose subscription customers without warning, noticing churn only after cancellation requests land.
✓You suffer from inventory stockouts or expensive overstock due to inaccurate seasonal demand estimates.
✓Your sales team chases unqualified leads because you lack data-backed predictive conversion scoring.
✓You experience fraud or transactional anomalies that require automated, real-time mathematical detection.

You probably do not need it if

✕You have less than 12 months of clean historical transaction data (sufficient training mass is strictly required).
✕Your business problem can be solved reliably with simple static heuristics or basic spreadsheet averages.

Problem Space

What we solve

Early Churn & Retention Prediction

Statistical identification of at-risk subscription accounts 30 to 60 days before contract cancellation.

Time-Series Demand & Inventory Forecasting

Predictive models accounting for seasonality, promotional campaigns, and external market signals to optimize purchasing.

Predictive Lead Conversion Scoring

Machine learning classification scoring incoming inbound leads based on historical closed-won patterns.

Production Real-Time Serving APIs

Containerized inference microservices delivering sub-50ms predictions directly into your CRM or ERP.

Engineering Process

How it works

01 — Diagnose

Data Feasibility & Baseline Audit

We assess data cleanliness, class imbalance, and volume to mathematically verify predictive model feasibility.

02 — Design

Feature Engineering & Variable Pipeline

We engineer predictive behavioral features (usage velocity, frequency decay, purchase recency ratios).

03 — Build

Model Training, Validation & Tuning

We train competitive algorithms (XGBoost, Random Forests, LightGBM) and tune hyperparameters against overfitting.

04 — Launch

Docker Containerization & Production Serving

We deploy the model via FastAPI, establish automated retraining pipelines, and setup data drift monitors.

Deliverables

What we deliver

Upon completion you will have

✓Trained and cross-validated predictive ML model with transparent evaluation metrics (ROC-AUC, F1, RMSE).

✓Containerized REST inference API (Docker/FastAPI) built for high throughput and low latency.

✓Automated feature engineering pipeline preparing new incoming data records.

✓Observability dashboard tracking data drift and model prediction accuracy over time.

✓Comprehensive model documentation, training notebooks, and Git repository code.

Delivery Plan

Implementation Phases

Tiempo típico de proyecto:8–12 weeks

Weeks 1–2Phase 1

Data Feasibility & Baseline Benchmark

Historical data sanitation, correlation analysis, and statistical baseline benchmarking.

Weeks 3–6Phase 2

Feature Engineering & Algorithm Training

Derived variable creation, competitive model training, cross-validation, and hyperparameter tuning.

Weeks 7–9Phase 3

Inference API & Containerization

REST API engineering, inference load testing, and Docker packaging.

Weeks 10–12Phase 4

System Integration & Drift Monitoring

CRM/ERP webhook integration, drift alert setup, and production deployment.

Pricing Guidance

Estimated Investment

Target Investment

USD 8,200

Includes feasibility evaluation, feature engineering, model training, containerized inference API, and drift monitoring.

Request diagnosis

Real-World Proof

Impact Case Study

From Blind 18% Churn to Proactive Retention of $240K in Annual Subscriptions

Initial problem

B2B subscription software firm with 1,400 customers experiencing 18% annual churn with zero early visibility into at-risk clients.

Technical intervention

Training of an XGBoost churn prediction model analyzing 42 behavioral indicators across platform usage, login frequencies, and support tickets.

Outcome achieved

The model flagged at-risk clients with 86% accuracy 45 days prior to renewal, enabling customer success to proactively save $240K in renewals.

Clarifications

Frequently asked questions

How much historical data is required to train a reliable model?

Typically, a minimum of 12 to 24 months of consistent transaction history with thousands of recorded events is necessary. During the initial feasibility phase, we verify whether your data meets mathematical thresholds before proceeding.

How do our existing business systems consume predictions?

We expose the model via a secure REST API. Your CRM or ERP passes a customer ID and receives real-time churn scores or purchase probabilities, or we run automated nightly batch jobs that update records in bulk.

What happens when customer behavior changes over time?

We implement Data Drift and Concept Drift monitoring systems that alert when live data distributions diverge from initial training sets, triggering automated retraining pipelines to maintain accuracy.

EVOX ENGINEERING™

Let's Map Your Solution

Schedule a 30-minute technical architecture call to assess your stack and define exact scope.

Talk with an architect

View all Engineering™ solutions