AI & Intelligent Systems
Forecasts, risk scores and alerts that feed real decisions
Brihat Infotech builds models that predict what you will sell, who will default and which reading looks wrong, then place the answer on the screen where your planner or underwriter decides.
What we build
- Demand and revenue forecasts for purchasing, production and cash planning.
- Risk and propensity scores
who is likely to default, churn or buy.
- Anomaly detection on transactions, meter readings and machine data.
- Optimisation of routes, prices, schedules and stock levels.
- Decision integration
the prediction appears inside your ERP or CRM screen, next to the action it informs.
The test a model must pass
A model ships only after it beats what you use today, whether that is last year's average or a planner's spreadsheet. We run both on the same history and show you the difference. Scores come with reasons, so a reviewer can see which inputs pushed a result up or down.
What the project delivers
- A trained model that beats the baseline on your history
- The feature pipeline that feeds it from your systems
- Drift monitoring that flags when live data stops resembling training data
- A retraining runbook your team or ours can follow
What we ask of you
3 things make a forecasting project quick. First, a history: sales, orders, readings or transactions, the longer the better. Second, a decision owner: the planner or credit officer who will act on the output. Third, access to the source systems, read-only is enough.
If the history has gaps, the data engineer fills what can be filled and reports what cannot. That report is useful by itself, since it shows where your records stop being reliable.
After launch
Models decay as your business changes: a new product line, a new supplier, a festival that moves. Drift monitoring compares live inputs to training data and raises a flag. The retraining runbook lists the steps to refresh the model, so that a retrain is a routine task and not a rescue project.
How a build runs
Three specialists work side by side: an ML engineer, a data engineer and a data scientist. The data engineer builds the pipeline first, because most delay in a forecasting project sits in the data and not in the model. The work uses Python, gradient-boosted trees such as XGBoost, deep learning where the data volume supports it, feature stores, and streaming scoring for live decisions. Releases arrive fortnightly; the rest of the path is on how we engage.
If monitoring and retraining need their own platform, see MLOps and AI infrastructure.
Related work
Consumer-lending NBFC. The loan platform includes risk scorecards, with each decision explained and logged. Lower-risk files pass without review, and the rest wait in a review lane. See the NBFC loan origination case study
Tell us the decision you make every week on a hunch.
Tell us what you want to build.
The first call is 45 minutes with an architect, usually the founder. It is free, and we will sign an NDA first if you ask.