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Ramanova Labs

Approach

A practical way to move forward.

Three methods we bring to every engagement. Each comes from practice, not theory, and each is vendor-neutral.

01 / Method

Ramanova CoE Blueprint

A tested operating model for standing up an AI Center of Excellence: structure, roles, intake, and delivery cadence.

The Blueprint answers three questions every AI program eventually faces: who owns AI work, how it gets approved, and how it moves from idea to production. We adapt it to your size, talent, and existing delivery model rather than imposing a fixed org chart.

What it covers

  • Charter, scope, and decision rights
  • Roles across business, technology, risk, and delivery
  • Use case intake and prioritization
  • A governed path from sandbox to production
  • Delivery cadence and portfolio reporting to leadership

Use it when: pilots are scattered, ownership is unclear, or an existing CoE has stalled.

You walk away with: an operating CoE with named owners, a working intake process, and a first wave of use cases in flight.

02 / Method

Ramanova AI Capability Maturity Framework

Maps your capabilities across three tiers, Foundation, Value, and Advanced, over five levels, and shows what to invest in next. Non-linear by design: real organizations don't mature in a straight line.

Foundation covers people and skills, cloud basics, data governance, and responsible AI. Value covers MLOps, generative AI applications, fine-tuning, and predictive models. Advanced covers multi-agent systems, self-healing pipelines, and AI security. Teams mark their current state, set target dates, and see which gaps block the next step.

Use it when: you need a shared, honest view of where you stand before setting a roadmap or budget.

You walk away with: a capability map leadership agrees on, and a short list of investments ranked by what they unlock.

The framework is free and open. Explore the interactive framework

03 / Method

Ramanova Governance Baseline

The controls regulated firms need before scaling AI: model due diligence, AI inventory, and risk review.

The Baseline defines the minimum controls to have in place before AI use grows beyond a few pilots. It is built to satisfy auditors and regulators without slowing every project, by matching the depth of review to the risk of each use case.

What it covers

  • An inventory of AI in use, including AI inside SaaS tools
  • Due diligence for foundation models and AI vendors
  • Risk-tiered review and approval
  • Validation standards: grounding, evaluation sets, and model-as-judge review
  • Monitoring, documentation, and audit readiness
  • Alignment to the NIST AI RMF and the EU AI Act

Use it when: AI is spreading faster than anyone can track it, an audit has flagged it, or you are about to scale.

You walk away with: a current AI inventory, a controls baseline, and a prioritized remediation plan.

From assessment to scale

  1. 01AssessInventory current AI use, map capabilities, and find where process and data, not models, are the constraint.
  2. 02DesignSet the operating model, the governance baseline, and a ranked use case portfolio.
  3. 03Stand upLaunch the CoE with owners named, intake open, and a governed path to production.
  4. 04DeliverTake the first wave of use cases to production under the three-check standard.
  5. 05ScaleAdd use cases through the same intake, measure value, and raise maturity where it pays off.

Let's begin

Starting a CoE, or fixing one that stalled? Let's talk.

Book a discovery call