Machine Learning Development, and when you do not need it
Forecasting, classification and scoring models built on your business data, and a straight answer if a simpler method will do.
Machine learning learns a pattern from past data to predict something: next month's demand, whether an order will be returned, which lead will convert, which machine will fail. It works when you have enough history, a clear target and a decision that changes with the prediction. Good ML work begins with a decision, then data, and only then a model.
Many problems brought to us do not meet that bar. A clean report, a rule or a spreadsheet formula often does the job at a tenth of the cost. We start by checking your data and the decision it supports, and we will tell you if ML is overkill before you spend money.
Why ML projects stall in Indian businesses
Not enough clean data
Two years of orders in different formats, missing dates and duplicate customers cannot train a reliable model. Data cleaning is often the largest part of the work.
No decision tied to the prediction
A demand forecast nobody uses to place orders is a cost, not an asset. The model must feed a purchase, pricing or staffing decision.
A model that works once
A notebook with good accuracy is not a product. Without monitoring and retraining, performance decays as customers and seasons change.
Wrong success measure
A fraud model that is 99 percent accurate may catch nothing if fraud is one in a thousand. The measure must match the business cost of errors.
Machine learning development we offer
Demand and sales forecasting
Forecasts by product, branch or week that respect festivals, monsoon and promotions. We compare against a simple baseline, and only keep the model if it clearly beats it.
Classification and scoring
Lead scoring, churn risk, ticket routing, return risk and document type detection. Output is a ranked list with reasons your team can read, not just a number.
Anomaly detection
Flagging unusual transactions, sensor readings or billing entries for review. We tune alerts to the volume your team can realistically check each day.
Recommendations and ranking
Product suggestions or search ranking for ecommerce and content sites, starting with simple co-purchase logic and moving to learned models only if it earns its place.
Data pipelines and cleaning
Pulling data from your database, Tally exports, spreadsheets and APIs into one clean dataset that refreshes on schedule, with checks that catch bad inputs.
Deployment and monitoring
The model is served as an API or scheduled job inside your app, with accuracy tracking, drift alerts and a retraining routine so it stays useful.
How a machine learning project is run
Discover
We look at your data sources, history length and quality, and the decision the prediction will change. We also estimate the value of a correct prediction.
Define
We set the target, the baseline to beat and the metric that reflects business cost. If the baseline already does well, we say so here.
Design
We design how predictions appear to users: ranked lists, reasons, confidence and an easy way to disagree. Adoption depends on this screen.
Build
We clean data, try simple models first, then complex ones only if they help. Results are validated on a later time period, not a random split.
Ship & extend
We deploy with monitoring, compare predictions to outcomes for a month and retrain on a schedule. Support covers one month after launch.
What it costs
ML work depends heavily on data condition, so we do not quote blind. A first assessment and a prototype are scoped separately from production deployment, and you get a fixed written quote for each stage.
- State and volume of your data
- Number of models or targets
- Integration into your app or ERP
- Monitoring and retraining needs
- Explanation and reporting screens
All prices exclude 18% GST. See also website costs and app costs.
Questions we get asked
How much data do we need?
It depends on the task. Forecasting usually needs at least two years of regular history, and classification needs several hundred examples per class. Quality matters as much as quantity. We check your data before promising anything.
Can you tell us if ML is not worth it?
Yes, and we often do. After looking at your data and the decision involved, we may recommend a report, a rule or a simple formula instead. That outcome saves you money and is a normal result of discovery.
How do we know the model works?
We test on data from a later period than the training data, compare against a simple baseline and report errors in business terms, such as units over- or under-forecast. After launch we track real outcomes against predictions.
Do you build deep learning or computer vision models?
We can take on image classification and similar tasks using pre-trained models, which often works with modest data. Custom deep learning from scratch needs large labelled datasets and is rarely justified for a typical business problem.
Who owns the model and the code?
You do. You receive the code, trained model files, data pipeline and documentation. Training data stays in your environment, and we avoid tools that tie the model to our servers. You also get the data pipeline and a short note describing how the model was trained and tested.
Will the model need maintenance?
Yes. Customer behaviour and seasons change, so accuracy drifts. We set up monitoring and a retraining routine, and a care plan can cover periodic review and retraining. Monitoring costs little to run, and it prevents the quiet decline that ruins many models.
Ready to scope it?
Tell us what you want to build. You get a fixed written quote after a free discovery call.