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Models that change a number your business cares about
Starting from
₹4.8 L
Per model, after data assessment
Typical timeline
8-20 weeks
Technologies
Most machine learning projects fail for unglamorous reasons: the data is not there, the baseline was never measured, or nobody defined what success looks like. We start with those three questions.
Every engagement begins with a data assessment and a written success metric. If the data will not support the goal, we say so before the budget is committed. When it will, we build, evaluate against a genuine baseline, and ship the model behind an API with monitoring for drift.
An honest report on whether your data can support the outcome you want, before you commit a budget.
We measure how the current process performs, so improvement is a number rather than an impression.
Feature attribution so the business can see why a prediction was made.
Alerting when live data diverges from training data and accuracy starts to slip.
Models served behind a versioned endpoint your existing systems can call.
A documented, repeatable path from new data to a new model version.
Data audit, feasibility analysis, baseline measurement and success criteria.
2-3 weeks
Feature engineering, model selection and offline evaluation against the baseline.
3-6 weeks
API serving, monitoring, retraining pipeline and integration.
3-6 weeks
Live monitoring, periodic retraining and accuracy reporting.
Ongoing
Every engagement is scoped after discovery. These packages exist so you can size a budget before we speak.
Find out whether the data supports the idea
₹1.9 L
3-4 weeks
3-4 weeks
Request this packageA trained model serving live traffic
₹6.8 L
10-16 weeks
10-16 weeks · 3 months support
Request this packageAll prices exclude GST. Final scope and cost are confirmed after discovery.
Sattva Foods
A fresh-food retailer replaced manual store-level ordering with a demand forecast, cutting wastage 34 percent while improving availability.
If yours is not answered here, ask us and you will get a direct answer.
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