Predictive Analytics & Custom ML Models
A forecast you can trust because you know what it got wrong last month, not one presented as a fixed number and left alone.
A predictive model that is accurate on launch day and never checked again is not a predictive system — it is a one-time report with extra steps. Real-world data drifts: customer behavior shifts, seasonality changes, a supplier changes their process, and the patterns the model learned stop holding. Most predictive analytics projects skip the part after deployment entirely, so the model quietly gets worse and nobody notices until a forecast is badly wrong and a decision gets made on it anyway.
We build the retraining and monitoring infrastructure as part of the project, not as an afterthought you discover you need six months in. That means defining accuracy targets with you upfront based on what the decision actually needs, tracking predicted-versus-actual once the model is live, and alerting when performance drifts below the agreed threshold — so the model that goes live in month one is still trustworthy in month twelve.
What's Included
Data audit and feasibility assessment
An honest read on whether your historical data supports the prediction you want to make, what volume and quality you actually have, and what accuracy is realistic given that — before any model gets built.
Trained and validated predictive model
A model built for your specific forecasting or prediction task, benchmarked against held-out real data and against the accuracy target agreed at the start.
Deployed prediction pipeline
The model running as a scheduled or on-demand service, feeding predictions into your dashboard, ERP, or planning tool through a defined API.
Drift monitoring and retraining pipeline
Automated tracking of predicted-versus-actual performance over time, with alerting when accuracy drops below threshold and a defined retraining process to bring it back.
Our Approach
Audit the data before promising an outcome.
We assess data volume, history depth, and quality against the prediction you want to make, and tell you honestly what accuracy is achievable — before committing to a build, not after.
Set the accuracy target as a business decision, not a technical one.
We work with you to define what forecast accuracy actually needs to be for the decision it informs — a demand forecast off by 5% might be fine for one business and costly for another.
Build monitoring as part of the deliverable, not a follow-on project.
Drift detection, retraining triggers, and a dashboard tracking predicted-versus-actual ship with the model, because a forecasting system without ongoing measurement degrades without anyone knowing.
Frequently Asked Questions
Demand and sales forecasting, inventory and supply planning, customer churn and risk scoring, equipment failure and maintenance prediction, and pricing or demand-response models. The common thread is historical data feeding a decision that is currently made on gut feel or a spreadsheet formula.
It depends on the seasonality and volatility of what you are predicting — a demand forecast usually needs at least a couple of years of history to capture seasonal patterns, while some operational predictions can work with less. We assess this directly against your data during the audit and tell you if the history you have is enough, or what would need to be collected first.
A focused forecasting model — for instance, SKU-level demand forecasting feeding your existing planning process — typically costs ₹8-15 lakh and takes 6-10 weeks, including the data audit and initial monitoring setup. Multi-variable models or systems requiring ongoing MLOps infrastructure across several product lines run ₹20-40 lakh over 12-18 weeks.
That is what the drift monitoring is for. We track predicted-versus-actual continuously after launch and alert when accuracy drops below the agreed threshold, which triggers a retraining cycle. We can run that monitoring and retraining under an ongoing support agreement, or hand it off to your team with full documentation if you prefer to own it.
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