Our Methodology

Most frameworks focus heavily on strategy and technology, forgetting what happens when the models actually hit production. We assess across seven dimensions, specifically including Lifecycle & Monitoring.

An AI solution that works perfectly in a pilot but has no ownership model or drift monitoring is a liability, not an asset. The seven dimensions ensure we read the entire operational lifecycle, not just the build phase.

The AI Readiness Index is governed by a rules-based scoring engine. The AI only writes the words in the narrative; it never decides the score.

This means same answers, same result, every time. It is a mathematical read of your state, immune to hallucination or drift in assessment criteria.

We do not use translated US checklists. The engine is DPDP-aware and understands sector rules like RBI data localization.

It is scored against Indian mid-market operating reality, where paper-first processes are common and 'hire a data scientist' is rarely the right first answer. We contextualize advice to the actual constraints of the market.

We do not use fabricated benchmarks. The index is absolute, not a peer percentile.

Low scores are stated plainly and paired with the specific next step required to improve them. We believe the value of an assessment is entirely in its willingness to tell you when you are not ready.

A score by itself is a claim. What backs it up is what makes it defensible in a boardroom or an audit. Every reading behind your ARI carries its own trust level, not a blanket assumption that it's true.

Nothing here is a black box. Every governance call is recorded with who made it and why, and locked to the exact evidence it cited at that moment - so an edit to that evidence later can never quietly change what an old decision claims to be based on. Deleting anything destructive takes two people, never one click.