An AI roadmap built from your own data and processes

A principal architect leads the work, with an AI engineer beside them and a data engineer when your data is the open question. They work next to the people who run the process, because a use case that looks good on a slide often depends on a spreadsheet nobody has mentioned.

Four methods fill the report:

1. 01Opportunity scoring against your actual processes.
2. 02Data audits of the systems each use case reads from.
3. 03Short model trials on your real documents, where a use case depends on a language model reading them.
4. 04Governance templates adapted to your approval chain.

You pay a fixed fee for the artefacts listed above.

We score each use case on 4 things: the value it could return, the quality of the data it needs, the effort to build it, and the risk if it gives a wrong answer. A claims-reading assistant with clean scanned forms may beat a forecasting model whose history sits in six spreadsheets, even when the forecast would be worth more on paper.

The build, buy or wait call follows from the scores. Build when no product fits your process. Buy when a packaged tool already does the job. Wait when the data or the model is not ready and the report names what would change that.

The governance framework says who approves a model's use, what each system logs and how a mistake is reported. We draw on ISO/IEC 42001, the NIST AI Risk Management Framework and the EU AI Act risk tiers as design references. They guide the framework; none is an audit we hold.

We check every model and vendor choice against data residency. If a dataset may not leave India or your network, the plan names the open-source or hosted-in-India route.