AI Solutions
Forecasts You Can Act On
We build forecasting and prediction models that are tested against history and delivered where decisions are made, with uncertainty shown rather than hidden.
Overview
Predictive Analytics Development
Prediction is only useful when it changes a decision: how much stock to order, which customers to contact, which machine to inspect. Codemotives starts with that decision and works backward to the data, the target to predict and the cost of being wrong in each direction. Many problems turn out to need cleaner reporting before any model is justified, and we say so.
We build and compare models against simple baselines, validate them by backtesting on time-ordered history, and report error ranges instead of single confident numbers. Models are then delivered into dashboards, applications or scheduled reports, with monitoring for drift, because customer behavior and markets change and a model that was accurate last year may not be accurate now.
Context
Problems we solve
- 01
Leaky or biased data
Information from the future or skewed history can make a model look accurate in testing and fail in practice.
- 02
Beating a simple baseline
A complex model is only worthwhile if it clearly outperforms a simple rule or average on real decisions.
- 03
Drift over time
Behavior changes, so accuracy decays. Monitoring and retraining need to be planned.
- 04
Predictions nobody uses
A score in a report changes nothing unless it reaches the person and process that can act on it.
Capabilities
What we deliver
- 01
Problem framing
Defining the decision, the prediction target, the horizon and the cost of different errors.
- 02
Feature engineering
Building signals from transactions, events and external data with care to avoid leakage.
- 03
Model development
Time series, gradient boosting and other methods compared against transparent baselines.
- 04
Backtesting
Time-aware validation and error ranges that show how the model would have performed historically.
- 05
Delivery into workflows
Scores and forecasts surfaced in dashboards, CRMs or planning tools with explanations where possible.
- 06
Drift monitoring
Tracking of accuracy and input changes with alerts and a retraining process.
Where it fits
- Demand forecasting for inventory planning
- Customer churn risk scoring
- Predictive maintenance for equipment
- Payment default risk estimates
- Staffing and capacity forecasts
Technologies we use
- Python
- scikit-learn
- XGBoost
- Prophet
- statsmodels
- pandas
- Airflow
- MLflow
Approach
How we deliver
- 1
Discover
We learn your goals, users, constraints and existing systems before proposing anything.
- 2
Plan
Scope, priorities, architecture and milestones are agreed so everyone knows what ships when.
- 3
Design
User flows, interfaces and technical design are shaped and reviewed with you early.
- 4
Build
Work is delivered in short iterations with regular demos, so progress is always visible.
- 5
Test
Functional, integration and performance checks run throughout, not only at the end.
- 6
Launch & support
We deploy, monitor and keep improving the product after it goes live.
FAQs
Common questions
Have a question that is not covered here? Get in touch.
How much history do we need?
It depends on seasonality, how stable the pattern is and what you are predicting. We examine your data first, and we will say plainly if it is too thin or too inconsistent to support a trustworthy model.
Can you guarantee accuracy?
No honest provider can. We can measure accuracy on historical data and report uncertainty, but future conditions may differ, so we plan monitoring and review rather than promise outcomes.
How is this different from your data science work?
Data science is the broad exploratory discipline. This service is a focused product: a prediction for a specific decision, validated, deployed and monitored inside your daily workflow.
Explore
Keep exploring
Let's build what's next
Tell us about your idea, product or legacy system. Send us a message and we will help you scope the right next step.