Scale · AI and automation
Data and analytics on your own data, with governed pipelines.
Data and analytics for Indian enterprises that need forecasts and predictions they can trust: sales, churn, fraud and inventory. NetScale assesses the data and who owns it, builds governed pipelines, then proves one model on the organisation’s own data before production. The models and pipelines are handed over as theirs.
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What it is
Data and Analytics
Data and analytics turns the records you already hold into forecasts and predictions: sales next quarter, customers likely to leave, transactions that look like fraud and stock to reorder.
A governed data pipeline brings that data to the model with each source named, each figure defined once and each change recorded. A warehouse holds it in one place, and feature engineering shapes it into the inputs a model uses.
Every model is tested on your data before anyone relies on it, and its accuracy is reviewed after go-live as the business changes.
Chatbots, computer vision and language models sit under Custom AI and GenAI solutions.
What it covers
- Sales forecasting
- Churn & fraud prediction
- Inventory optimisation
- Governed data pipelines
In this line: AI and automation
Sound familiar?
- 01
The forecasting model looked accurate on the vendor’s demo data. On your data it did not hold. What would it take to trust it?
Our answer: Sales forecasting
- 02
Three reports give three numbers for the same month. Which one is right?
Our answer: Governed data pipelines and warehouses
- 03
Stock is reordered on last year’s sales. Who checks it against this year’s?
Our answer: Inventory optimisation
- 04
Fraud is found when a customer complains. How long after the transaction is that?
Our answer: Churn and fraud prediction
What we do.
Sales forecasting, churn prediction, fraud detection and inventory optimisation, built on governed data pipelines.
- 01
Sales forecasting
Forecasts by product, region or channel, built from your sales history and the drivers you name, tested on closed periods before anyone plans on them.
- 02
Churn and fraud prediction
Scores for customers likely to leave and transactions that look unusual, each with the factors behind it, sent to the team that acts on them.
- 03
Inventory optimisation
Reorder points and stock levels by item and location, set from the demand forecast and supplier lead times, and reviewed as demand changes.
- 04
Governed data pipelines and warehouses
Sources mapped, figures defined once and loaded into a warehouse, with access, lineage and every change recorded, so each report draws on the same numbers.
- 05
Feature engineering
Raw records turned into the inputs a model learns from, documented so your team can see what each model uses and why.
How an engagement runs.
One question, data to answer, drawn three times.
You can stop at any phase boundary. The assessment is free, and each phase is priced in writing before it starts.How we engage
India specifics and cost.
India specifics
- DPDP
Churn and fraud models often learn from customer records, which are personal data under the DPDP Act, whose Rules’ duties apply from 13 May 2027. Models use only the fields they need, masked where identity is not required. Legal interpretation stays with your counsel.
- Payment data
The RBI’s direction of 6 April 2018 requires payment system data to be stored only in India. Fraud models on that data are built and run in Microsoft Azure’s India regions.
Source: RBI direction on storage of payment system data, 6 April 2018 (external site)
- Data residency
Where a sector rule or a customer contract requires it, the warehouse and pipelines run in India too. The region is written into the design.
What drives the cost
- 01
Data sources, and how many systems hold versions of the same figure.
- 02
Data quality: gaps, duplicates and definitions that disagree.
- 03
Models in scope: forecasting, churn, fraud or inventory, and how many at once.
- 04
Refresh frequency, and the retraining schedule after go-live.
The technology we run for this.
Cloud platform and identity
Our own information security and privacy management systems, run under both standards.
Certifications and technologyQuestions buyers ask about this.
Related services
Why does a model that worked in the demo fail on our data?
Usually because the demo data was cleaner, older or from a different business than yours. Your data has gaps, duplicates and its own seasonality, and the model never learned them. We test every model on your own records, against periods already closed, before anyone plans on its numbers.
What is a governed data pipeline?
A governed data pipeline moves data to a warehouse or model with each source named, each figure defined once and every change recorded. Someone owns each step, and access is controlled. It is what lets two reports, or a report and a model, agree on the same number.
Do we need a data warehouse before we start?
Not always. One model can start from the systems you already run, if the data is good enough, and the assessment says whether it is. A warehouse earns its cost when several models or reports need the same figures. The recommendation is in writing, and the decision stays yours.
Can you use data from Tally, our ERP and spreadsheets?
Yes, where it can be read reliably. The assessment lists each source, how complete it is and how often it changes. Pipelines then bring Tally, ERP, CRM and spreadsheet data into one governed store, with each figure defined once. That way, a model is not trained on figures that disagree.
Who owns the models and pipelines you build?
You do. Models, pipelines, code and documentation are handed over as yours, with the runbooks to retrain and run them. You can stop at any phase boundary and keep what has been built so far. If we keep running them, that is a separate agreement, in writing.
Test a model on your own data.
It costs nothing and commits you to nothing. You get a written list of where your IT environment is exposed, and your team can act on it with or without us.
What we put in writing
- The written findings are yours whether we ever work together or not.
- Each phase is fixed-scope and signed off separately.
- You can stop at any phase boundary.
- Runbooks, configurations and credentials are yours.