An AI readiness assessment is a short, structured engagement that evaluates whether your organization — its data, infrastructure, team, and use cases — is prepared to take AI to production, and produces a concrete plan for closing the gaps. Ours takes two weeks and is run by engineers who build production AI systems, not analysts working from a maturity-model template.
Most GenAI pilots fail at integration, not at the model. The assessment exists to put your effort on the use cases that will survive contact with production.
What We Assess
Data Readiness
Where your data lives, how clean and accessible it is, and what it takes to make it retrievable — pipelines, indexing, permissions, and freshness. This is where most AI initiatives quietly succeed or fail.
Use-Case Fit & ROI
We score your candidate use cases on feasibility, measurable ROI, and time to production — and tell you plainly which ones aren't worth building yet, and why.
Infrastructure & Stack
Your cloud setup, search and database layer, and how an LLM stack — retrieval, orchestration, evaluation, observability — fits into it. On AWS, we map the path across Bedrock, OpenSearch, and SageMaker as an AWS Advanced Tier Services Partner.
Team, Security & Governance
Skills gaps, guardrails and compliance requirements, and the operational ownership your AI systems will need after launch.
What You Get
- A scored use-case shortlist — which AI use cases to build first, with expected ROI and effort.
- A gap analysis of your data, infrastructure, and team against what production AI requires.
- A concrete roadmap — architecture, milestones, and budget to reach production, typically in weeks.
The assessment maps directly onto the first stage of our AI Launchpad program — if you proceed to build, nothing is thrown away. If you'd rather get ongoing guidance first, see our AI strategy consulting services, or go deeper on a specific track: LLM development or AI agent development.