You’ve already spent on AI, and little of it runs in production. Relevant’s AI maturity assessment scores your strategy, data, governance, infrastructure, and skills against the demands of production AI. You know where you stand and what to build first before you commit to a budget.
We score how closely your AI plans align with business value, and flag the use cases worth funding versus those that aren’t.
Our data engineering team tests whether your data can support production AI, from pipeline health to governance.
Our architects review what you’ve already deployed, what’s earning its keep, and what’s quietly adding risk.
We examine the platforms, tooling, and delivery discipline that move a model from prototype to production.
Our delivery team maps the skills, structure, and ways of working that decide whether AI sticks after launch.
We check the controls that keep AI safe to deploy, from model governance to regulatory exposure.
Discovery scores your organization across strategy, data, infrastructure, talent, and governance, measured against systems we’ve actually shipped. You start with an evidence-based picture of where you stand.
Each gap is ranked by how directly it blocks production AI and by the cost to close it. You know which weaknesses to fix first and which can wait.
Findings become a sequenced roadmap with effort, cost, and dependencies per initiative, plus build-vs-buy reasoning for each. You get an approach defined before any budget is committed.
The highest-value initiative gets scoped into a concrete first build, with architecture, estimate, and success metrics, whether that’s a pipeline or the data analytics layer on top of it. You get a concrete first build to approve, not a direction to consider.
A written readiness report lands first: your data quality, source systems, and governance gaps scored, with a prioritized shortlist of use cases ranked by value and feasibility. You leave the assessment holding the report, the shortlist, and a clear recommendation on what to build first.
246 projects delivered. Your baseline is scored by engineers who deliver AI-assisted software development, so the roadmap holds up against what actually gets built.
92% senior talent. You work with architects who have run AI in production.
13+ years building software. Maturity is measured against systems we’ve shipped, so the bar is real, not aspirational.
Claude Certified Architects in the team. Deep coverage of Claude, MCP, and modern AI stacks at the points your assessment reaches them.
96% employee retention. The team that scores your baseline is still here to execute the roadmap.
A 4.9 Clutch rating from verified clients who consistently cite transparency and delivery quality.
We start by inventorying every tool, model, and vendor in play, including the pilots that never made it past one team and the subscriptions no one tracks centrally. Each one gets scored on the same enterprise standard: what it costs, what it delivers, how it’s governed, and whether it duplicates something running elsewhere.
From there, we map the overlaps and the gaps. You get one consolidated view in place of a dozen local ones, a clear call on what to keep, retire, or merge, and a single standard the whole organization can build against going forward.
The assessment maps where regulated data flows, who can reach models and their outputs, how the system logs and audits decisions, and where a model could produce a result you can’t defend to a regulator. It measures each of those against the specific frameworks you answer to, whether that’s HIPAA for protected health data, SOC 2 for service controls, GDPR for personal data, or a combination.
You leave with your exposure mapped point by point, and a clear list of the controls a compliant production build needs before it ships, measured against the bar auditors actually apply. Relevant grounds that bar in real work, having run regulated migrations across HIPAA, SOC 2, and GDPR.
Yes, and that fragmentation is usually the finding that matters most. Our engineers trace where your data actually lives, across legacy databases, warehouses, SaaS tools, and the exports people quietly run by hand, then grade it on quality, accessibility, lineage, and governance.
The output is a prioritized read on your data foundation: what’s ready, what’s blocking you, and the specific consolidation work that turns scattered systems into a pipeline a model can train on and run against reliably.
You start exactly where you are, and a blank slate is a perfectly normal starting point. Most organizations come in with a few experiments, some data, and no clear sense of whether any of it is ready for more, which is enough to assess. The first step is simply taking stock: what you have running, what data you hold, and where you want AI to take the business.
From there, you get an honest baseline of where you stand against production-ready standards and a ranked list of the gaps worth closing first, so your opening move is a deliberate one rather than a guess.
No. The assessment ends with a roadmap that is yours to keep and act on however you choose. It names the gaps to close, the priorities, and what each fix would take, in enough detail that your own team or any other vendor could execute it. Where the roadmap convinces you and you’d rather not staff the work yourself, you can bring us in to deliver it, and the people who ran the assessment already know your environment. Where you’d rather build in-house, the roadmap stands on its own.
In a typical engagement, you move from a system audit to a funded roadmap in about 14 days. The timeline tracks the size of your AI footprint, the number of systems and data sources in scope, and the number of stakeholders the review must cover, so a single-team pilot can move faster than an enterprise spread across business units. You get a defined schedule with the scope confirmed after the initial call with our team, so the assessment lands on a date you can plan a budget conversation around.
That’s exactly what the assessment settles. Bring us your current setup, and you’ll leave knowing where you stand, which gaps block production AI, and what the first build should be.
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