Find out if your data environment is ready for AI — and get a clear roadmap to make it happen.
A structured expert audit that evaluates your current foundations, identifies gaps, and delivers an actionable plan — so you know exactly what to fix before investing in AI.
Get your readiness map
We evaluate foundations, feasibility, and the exact fixes to unlock AI.
Book a free call with our expert
"Most companies rush into AI without knowing if their data environment can actually support it. They waste months and budgets on tools that fail — not because AI does not work, but because the foundations were never designed for it. AI Readiness gives you clarity first. You will know exactly where you stand, what is blocking you, and what needs to happen next."
Radek Duha, Head of Data
Know if your stack can handle AI
We stress-test models, pipelines, and governance to prove readiness before you invest.
De-risk accuracy and safety
We reveal where AI would hallucinate, leak data, or break — and how to prevent it.
Get a real plan, not hype
You get a prioritized, costed roadmap instead of generic vendor promises.
Invest where ROI is highest
We show the fastest wins and the minimum fixes needed to ship AI safely.
The problems you are likely facing
Reliable AI needs solid foundations. Here is what usually gets in the way — and what the audit uncovers first.
No one can confidently say yes or no because the environment has never been evaluated for AI use cases.
Chatbots hallucinate, NL2SQL is off, and insights don’t match reality — the issue is the foundation.
Teams report different numbers, documentation is thin, and AI cannot reason in that ambiguity.
You sense data quality issues but lack a structured assessment of what blocks AI and what to do first.
Budgets and time are hard to commit without a clear, actionable plan to reach AI readiness.
Our AI Readiness Audit process
Six senior-led steps to evaluate, de-risk, and plan your path to AI.
Metadata And Architecture Review
We evaluate how well your warehouse supports AI: metadata, database structure, pipelines, and the tech stack behind them.
Metrics & Semantic Clarity Evaluation
Check how business metrics are defined, calculated, and documented to ensure AI can rely on them.
Data Quality & Governance Check
Review testing coverage and governance to expose gaps that make AI answers unreliable.
AI Use Case Feasibility Analysis
Evaluate which AI initiatives (NL2SQL, chatbots, agents) are realistic today versus later.
Gap Analysis & Prioritized Roadmap
Identify blockers, rank them by impact and effort, and build a clear action plan.
Handover & Strategic Consultation
Present findings live, answer questions, and guide the next steps for your team.
What you will get from the AI Readiness Audit
Clarity on where you stand, the plan to fix it, and the confidence to invest in AI without surprises.
Clarity on AI readiness
A factual view of whether your environment can support AI use cases now.
Prioritized action plan
Specific steps, priorities, and timelines — not vague recommendations.
Confidence in investment
Leadership knows what’s realistic now and what needs foundational work first.
Lower risk of failed AI
Blockers are identified upfront so tools don’t collapse in production.
Faster path to AI adoption
Teams can focus on the right fixes first and shorten time-to-value.
Senior expert guidance
Analysis and recommendations from experienced data and AI specialists only.
Who gets the most value
Where a readiness audit delivers the fastest clarity and impact.
Companies considering AI adoption
Exploring AI insights, chatbots, NL2SQL, or automation but unsure if the environment can support it.
Teams that tried AI and got poor results
Experiments produced unreliable answers, hallucinations, or inconsistent outputs.
Leadership planning AI investments
CTOs, Heads of Data, and Product leaders who need clarity before committing budget and engineering time.
Organizations with messy or undocumented data
Metrics vary by team, documentation is sparse, and data quality is inconsistent.
Teams preparing for larger AI & data projects
Those considering a broader AI & Data Foundations engagement but want a diagnostic first.
FAQ
Answers before we start
How long does the AI Readiness Audit take?+
Most audits are completed in 1–2 weeks depending on complexity. The goal is fast clarity without losing depth.
Do we need to pause current work during the audit?+
No. We work alongside your team with minimal disruption through access, documentation review, and short interviews.
What if the audit shows we're not AI-ready?+
You get a clear, prioritized roadmap showing exactly what to fix. You can implement it internally or with our help.
Is this just a checklist, or real expert analysis?+
It is senior-led analysis tailored to your environment and use cases — not a generic checklist.
What do we need to prepare before starting?+
Just access to your warehouse, pipelines, and docs. We guide the process to keep it simple.
Can we use the audit results to justify AI investments?+
Yes. Clients use the report and roadmap to secure leadership buy-in and budget because it provides clear evidence.
Next step
Book a call with our expert
A focused expert call to evaluate your data environment, identify the biggest risks, and confirm the audit scope.
Clear guidance. Senior expertise. No vendor hype.
