Predictive Analytics & ML
Predict What Happens Next. Act Before It Does.
We build and deploy machine learning models that forecast revenue, detect churn, score leads, and flag anomalies — turning your historical data into a forward-looking competitive advantage.
Scikit-learn · XGBoost · PyTorch
API-deployable Models
94% Avg Accuracy
MLOps & Monitoring
Demand Forecast91% accuracy
Actual vs Predicted (12 weeks)Model: XGBoost
ActualForecast
94%
Avg Accuracy
+30%
Lift over baseline
4 wks
To production
80+
ML Models in Production
94%
Average Model Accuracy
30%
Avg Revenue Uplift
12+
Prediction Problem Types
The Prediction Gap
Your competitor acts 6 weeks earlier than you
Same decision. 6 weeks apart.
Inventory decision — Q4 consumer electronics. Two companies, same market.
With ML ForecastOctober 1st
Model predicts +34% Q4 demand. Places supplier orders on Oct 1. Stock arrives Nov 5. Full shelves for peak season. Extra $2.1M revenue.
Without MLNovember 15th
Demand spike visible in sales data. Rushes supplier order Nov 15. Stock arrives Dec 22 — 3 days before Christmas. Misses peak window. $1.8M in lost sales.
6 weeks
Decision advantage
Average lead-time advantage predictive analytics gives you over reactive decision-making in supply chain scenarios.
$1.8M
Cost of being reactive
Revenue lost in the example above — a pattern we see across retail, logistics, and SaaS clients before their first ML model ships.
Use Cases
What would you like to predict?
Demand Forecasting
Know next quarter's demand before it arrives
Time-series models trained on your historical sales, seasonality, promotions, and external signals. Outputs are probability distributions — not false point estimates — so your procurement team can make risk-calibrated purchasing decisions.
Model Outputs
Weekly / daily demand by SKU
Confidence intervals (P10–P90)
Seasonal decomposition
Promotion lift factors
41%
MAPE improvement
vs naive baseline
Algorithm
Prophet + XGBoost Hybrid / LSTM
12-Week Forecast
ActualForecast91% accuracy
Our ProcessFrom raw data to
From raw data to
production-grade ML
Every predictive analytics engagement follows four stages designed around one output: a model that runs in production and delivers measurable ROI.
Problem framing and data assessment1–2 weeks
Model development and validation4–8 weeks
Production deployment and integration1–2 weeks
01
Problem Framing
We translate business goals into a precise prediction problem — what to predict, how accurately, and what decision it drives. Framing matters more than algorithm choice.
02
Data Assessment
We audit your historical data for volume, quality, and signal strength — identifying gaps and feature engineering opportunities that can dramatically improve model performance.
03
Model Development
We train, evaluate, and select the best algorithm — from gradient boosting to deep learning — then explain results in plain language. No black-box handoffs.
04
Deploy & Monitor
Production deployment via API or embedded in your workflows. Continuous monitoring for model drift with automated retraining pipelines and monthly ROI reports.
The Business Case
Predictive vs Reactive
Every business decision can be made earlier with a model. Here's what that looks like across seven domains.
| Decision | Predictive ML | Reactive / Manual |
|---|---|---|
| Inventory decisions | Model reorder 6 wks ahead | React when shelves empty |
| Churn management | Intervene 30 days pre-churn | Lose the customer, do postmortem |
| Demand planning | Calibrated range with confidence | Last year ±gut feel |
| Fraud response | Block before transaction clears | Detect in next day's batch |
| Revenue visibility | Rolling 12-week forecast | This month's actuals |
| Team workload | Alerts only on anomalies | Manual review of everything |
| Decision confidence | P10–P90 probability range | Gut feel, no quantified risk |
Client Outcome
Stockouts dropped from 44 to 6 per month
Challenge
Consumer electronics retailer with 4,000+ SKUs — chronic overstock on slow-movers, stockouts on bestsellers, all purchasing decisions based on gut feel.
What We Built
XGBoost demand forecasting model trained on 3 years of sales, promotions, and supplier lead times — integrated into their ERP for automated reorder triggers.
Results
-31%
Holding costs in Q1
44 → 6
Monthly stockouts
91%
Accuracy across all 4,000 SKUs
Ready to stop reacting and start predicting?
Tell us what you want to predict. We'll assess your data and give you a realistic accuracy and ROI estimate — before any commitment.
"The demand forecast model cut our overstock by 31% in the first quarter. We went from gut-feel ordering to data-driven purchasing across 4,000 SKUs. The ROI was visible within 6 weeks of go-live."
AV
A. Varma
VP Supply Chain, Consumer Electronics · Toronto
ML & MLOps Stack
Tools we build and deploy with
Pythonscikit-learnXGBoostLightGBMPyTorchProphetstatsmodelsMLflowVertex AISageMakerFastAPIDockerKubernetesSHAP
FAQ
Questions we get all the time
If yours is not here, reach out. We respond within 24 hours with a real answer from an engineer — not a sales pitch.
It depends on the problem. For demand forecasting, 2+ years of clean sales data is ideal. For churn prediction, 6–12 months of engagement data often suffices. For fraud detection, even 3 months can work if you have enough positive examples. We assess your data in the first engagement call.
We use explainability techniques (SHAP values, LIME, partial dependence plots) to show which factors drive each prediction. We also build simple score-card views in your dashboards so managers understand WHY a customer is flagged as high-risk, not just that they are.
All models have confidence intervals and error rates. We set honest expectations upfront, monitor model performance in production, and implement retraining pipelines to adapt to distribution shift. We build for 80–95% accuracy depending on the problem — not 100% promises.
Both, depending on the use case. We use AutoML (Vertex AI, SageMaker Autopilot, H2O) to benchmark quickly, then build custom models in scikit-learn, XGBoost, PyTorch, or TensorFlow when custom architecture outperforms out-of-the-box solutions.
A focused prediction model with clean data can be trained and deployed in 4–8 weeks. Complex multi-model pipelines with real-time scoring APIs typically take 8–16 weeks. We always start with a minimum viable model to show value quickly.
Accuracy depends on the problem and data quality, but our demand and revenue forecasts typically land at 85–94% accuracy and cut error (MAPE/RMSE) 30–40% versus a naive baseline. Every model ships with confidence intervals (P10–P90) so you see the uncertainty, not just a single point estimate.
Yes. We deploy models as REST APIs or embed scoring directly into your workflows, with prebuilt integrations for ERPs, Salesforce, HubSpot, and data warehouses like Snowflake and BigQuery. Churn and risk scores can be written straight back into the tools your team already uses — no SQL required for end users.
Predictive AnalyticsForecast with
Forecast with
confidence
Tell us what you need to predict. We will build forecasting models trained on your data with measurable accuracy.
Free consultation24-hour responseNDA on request
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