SANJEEVANI AI · AI Readiness : Quantified | Measured continuously.

A defensible number for the AI you run.

METRIS™ is quantitative AI trustworthiness measurement. It scores AI systems on a 0 to 1000 scale, mapped across NIST AI RMF, ISO/IEC 42001, MITRE ATLAS, OWASP, and the EU AI Act at the same time. One number, multiple frameworks, decomposable to the evidence behind it.

The problem METRIS™ solves

Governance programs generate metrics: assessments completed, tools approved, training done, intake times. Every one of them measures organizational activity. None of them measures the AI itself. A dashboard can be fully green while a model drifts, discriminates, or fails quietly on the customers it sees least.

When a board, an auditor, or a state examiner asks how trustworthy a system is today, process metrics cannot answer, because the question was never theirs to answer. That gap is the difference between proving the paperwork ran and measuring the system.

What you get

You receive a trust posture score you can defend, decomposable back to the evidence that produced it, and mapped to the frameworks your regulators and examiners already use. Three properties make it defensible:

  • It scores evidence, not assertions. The score measures what your documented controls can actually prove, not what a checklist claims.
  • It knows what it has not measured. Evidence of failure and absence of evidence are different things. A control never evidenced is not a control that passed.
  • It is continuous. AI is the one class of system that changes after you assess it, so a point-in-time attestation expires the day it is signed.

The LAMS™ framework

METRIS™ measures the human-AI portfolio through the proprietary LAMS™ framework, four layers in sequence:

  • L · Literacy. Whether your workforce actually understands the AI it uses.
  • A · Awareness. Where AI is being used without visibility, including shadow AI.
  • M · Measurement. Continuous measurement of the systems themselves, not the paperwork around them.
  • S · Synergy. Where accountability shifts between people and AI systems, and who owned which decision.

Who METRIS™ is for

METRIS™ is for any organization accountable for an AI system, regardless of size, budget, or industry. Regulated enterprises carrying model risk in banking, insurance, and healthcare. Mid-market firms without a governance department. Teams whose staff use ChatGPT, Copilot, or Claude with no measurement of what that means. A five-person nonprofit and a five-thousand-person enterprise get the same methodology. The methodology does not change. The price does.

Questions METRIS™ answers

  • Can you prove your AI made the right decision?
  • Where does accountability shift between people and systems?
  • Can you reconstruct and defend AI outcomes after the fact?
  • Is your AI degrading silently in production?
  • Where is AI being used without visibility?
  • Are your AI vendors truly transparent, or just compliant on paper?
  • When AI fails, can you prove why?

Who built METRIS™

SANJEEVANI AI was founded by Suneeta Modekurty, a researcher, data scientist, and ISO/IEC 42001 certified professional, with 25 years deploying AI across healthcare, pharma, finance, insurance, and education. She is the author of "The AI-Human Synergy: A Data Scientist's Vision for the Future" (2024). SANJEEVANI AI is based in St. Louis, Missouri.

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Contact

SANJEEVANI AI LLC, St. Louis, Missouri, USA. Email: suneeta@sanjeevaniai.com. Website: https://sanjeevaniai.com

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