Free AI Readiness Assessment — Data Foundation for AI on AWS

Most AI initiatives don't fail on the model. They fail on the data underneath it — and that usually surfaces nine months in, after the budget is committed and the roadmap is public. This free assessment lets you build a better plan.

WHAT IT IS A single four-hour working session in which Vivanti data engineers help you connect Dagen.ai to your Amazon Web Services (AWS) data estate and examine the data against the specific demands of AI workloads: whether your data carries enough context for a model to reason correctly, whether your data pipelines stay true without someone watching them, and whether the cost curve holds when inference volume arrives.

This is a working session, not a discovery call. Bring the people who know your data.

WHY THESE FINDINGS ARE DIFFERENT Your Dagen.ai free trial is provisioned before the session and runs live during it. The trial includes agent compute credits, up to three connected data sources, up to five autonomous pipelines per month, and access to all nine Dagen specialist agents with tri-layer, session-scoped memory. Sign-up by email, Google SSO, or GitHub, and the Dagen.ai account stays yours afterward.

Running the assessment live gives you real information. The risks in your audit are ones Dagen's agents found in your data. Nothing here is inferred from a questionnaire or read off an architecture diagram that may, or may not, be up-to-date.

WHAT WE DO TOGETHER Connectivity and discovery. Secure read-only connection established; the Metadata Discovery Agent runs against your sample tables. Agentic analysis. Dagen profiles the sources, maps relationships across them, and performs dialect and dependency analysis. Simulated build. A target Apache Iceberg schema is generated and a transformation pipeline is simulated for the domain you chose. Synthesis and findings. We walk the readiness report together: broken views, unprotected personally identifiable information (PII), schema mismatches. Roadmap handoff. The specific next step that maps to what we found, or the reasons to wait.

WHAT YOU GET OUT OF THIS 1. Project Complexity Score. A defensible read on what this build actually takes, so the estimate you give your executive team is one you can stand behind. 2. Risk Audit. The blocking issues named explicitly: high-risk PII, cross-dataset dependencies, lineage gaps. The problems that stop a project in month seven, found in hour two. 3. Target-State Blueprint. A conceptual architecture for an environment that supports what you are trying to do, whether that means extending your current estate or moving off it. 4. Business Case. Current run-rate against projected efficiencies, in a form your CFO will recognize.

WHO THIS IS FOR Teams planning an AI initiative on AWS who want an honest read on the data foundation before the budget is committed. Teams who suspect their current environment is not the right place to build it. And teams who already know something is wrong underneath and need it named, scoped, and priced before they can get funding to fix it.

WHY VIVANTI AND DAGEN Vivanti has spent two decades preparing enterprise data for activation across financial services, health and life sciences, supply chain, media and entertainment, the public sector, and more. Dagen provides the agentic pipeline infrastructure that keeps that foundation trustworthy once you build on it — deploying natively into your AWS environment and processing on the services you already run. So the same team that finds the gaps is able to help you close them.

WHAT HAPPENS NEXT IS YOURS TO DECIDE The four artifacts are yours to keep and to use. If you want help, we will tell you which use cases fit what we found. If the right answer is to wait until your data is ready, we will tell you that too.

No cost. No obligation. Request a private offer to schedule.