AI Readiness Assessment & Consulting

What is the difference between AI readiness and a data foundation engagement?

AI readiness is the assessment. Data foundation work is one of the fixes. Many AI readiness engagements lead to data foundation work, but not always.

30 days for the assessment. 90 days to close the highest-priority gaps in most cases. Output: a 15-page written report with score, evidence, and a 90-day plan.

Can we skip AI readiness if we already have AI projects running?

You can. Most stalled AI projects were missing one or more of the five readiness dimensions. The Express Scripts case study (member match accuracy 75% to 94%, $4.8M a year recovered) is what this work looks like in practice. Three stalled ML pilots revived once readiness gaps closed.

Sometimes. Often the right move is to fix what you have before adding a platform. We tell you the truth on the readiness call.

Yes. A 15-page report with score, evidence, and 90-day plan. If you qualify, the assessment is at no cost.

What is the difference between AI readiness and AI strategy?

AI readiness asks: can our data and process support AI? It's grounded, technical, and produces a 90-day plan. AI strategy asks: what AI use cases should we pursue? It's more abstract, often vendor-influenced, and produces a deck. Readiness comes first.

False alerts per hour, down from 2.1 after ML pipeline remediation

An AI readiness assessment evaluates your organization across five dimensions: data completeness, metric consistency, governance maturity, pipeline reliability, and AI-ready architecture. The output is a scored rubric and a prioritized remediation roadmap that tells you exactly what to fix before your next AI initiative.

The most common reason AI pilots fail is not the model, it is the data. Training data that is incomplete, inconsistently defined, or poorly governed produces models that cannot generalize to production. We fix the data layer first so your AI investments actually succeed.

Both. We assess and fix the data foundation, then build and deploy ML pipelines, LLM-grounded applications, and AI automation systems. We deliver production-grade systems, not prototypes.

LLM grounding is the process of connecting a large language model to your organization's specific, verified data so it produces accurate, context-aware responses rather than hallucinations. Without proper grounding architecture, enterprise LLM applications are unreliable and cannot be trusted for operational decisions.

AI readiness, ML pipeline design, and data governance for enterprise AI deployment.

AI readiness is the assessment of whether an organization's data, processes, and governance can support AI in production, not just in a pilot. The output of an AI readiness engagement is a documented score on data quality, metric consistency, governance maturity, pipeline reliability, and AI-ready architecture, plus a 90-day plan to close the gaps.

A 30-day structured assessment with a 15-page written report.

An AI strategy deck. We produce evidence and a plan, not vendor-influenced slideware.

A platform pitch. We don't sell Snowflake, Databricks, Tableau, or Power BI. We recommend them only when they fit.

Pilots were built on prototype data, not production-grade pipelines

Enablement is hands-on, on your real workflows. These are the factors that move the effort.

Enablement scales with how many teams and use cases you bring along.

Teams new to AI need more foundational enablement than advanced ones.

Hands-on enablement on real workflows is more than a generic training.

Your teams want to use AI but do not know where to start safely.

You want hands-on enablement on real workflows, not slideware.

You want a system built, not your team trained: see AI Workflow Automation.

You need governance operated for you: see AI Governance & Managed Operations.

You need to know if your data is ready first: see AI Readiness Assessment.

We assess your AI readiness frankly, fix what needs fixing, and build the data foundation that makes AI pilots succeed instead of stall. No hype. No platform replacement required.

We start with an honest assessment of where your data stands, fix the gaps that are blocking your AI initiatives, then build and deploy the ML pipelines and LLM applications that your organization actually needs.

Start with a 30-day Analytics Truth Audit. We score your AI readiness across 5 dimensions and give you a 90-day roadmap to production.