Generative AI Cloud Solutions Built and Governed for Production

Start with one workflow that has high volume, clear pain and measurable outcomes, such as policy queries to a central desk, document keying or contact-centre after-call work. Build a golden test set with the users first. A focused first application in 8-12 weeks teaches more than a broad platform programme with no live users.

Standardise on one primary platform where your identity, data and enterprise agreements already sit, because governance, networking and skills are simpler. Use a second cloud only for a specific, measured advantage, such as a model that performs materially better on your task, and route it through the same AI gateway.

We design for the DPDP Act 2023 from the start: purpose-limited data, masking of identifiers, in-region storage and model processing where personal data is involved, and defined retention for prompts and logs. Logging follows CERT-In's six-hour reporting and 180-day retention directions, and sector expectations from RBI, SEBI or IRDAI shape governance.

Agent-assist and document features often show measurable time savings within four to six weeks of pilot launch. Assistants and agents typically reach assisted production in 10-14 weeks. We set baselines before build so value is measured against real numbers, not demo impressions.

Yes. After hand-over we can operate applications under managed support: monitoring quality and cost, refreshing golden sets, re-evaluating when models change, updating content pipelines and handling incidents. Many clients keep product ownership in-house and use NTSPL for evaluation, security testing and FinOps cycles.

Usually three things: no agreed accuracy measure, so trust collapses after the first visible error; poor source content, which no model can fix; and costs that grow faster than value because nobody owns unit economics. Our capabilities in evaluation, data preparation and FinOps exist specifically to prevent these.