Your AI project succeeds on two things, systems that hold up under review and people who open the tool. Relevant Software assesses both, and the technology side runs as four engagements that each produce something you can act on.
Before a budget turns into a build, someone has to score what the company already holds: the data, the infrastructure, the engineering practice, the people, and the governance around them.
Business units buy their own tools and teams ship models between reorganisations, so the count is usually higher than the roadmap shows. Relevant’s AI audit tests what each system does today against what its documentation claims.
Widening a rollout puts more people and more data in front of the same models. Relevant’s security consultants produce the threat model, the target architecture, and the order the fixes follow.
Every system in a rollout answers to something: an EU tier, a US state rule, a sector regulator. AI compliance services classify each one and attach the duties that follow, so each obligation arrives against the system it binds.
A policy gets followed when the people covered by it can read it in a minute. Relevant’s consultants write which tool takes which data, what may leave the company, and who to ask when the rule runs out.
One team, one workflow, and a number at the end of it. Relevant picks the workflow with the clearest before-and-after, runs the pilot with that team, and measures usage against the licences you already pay for.
Engineering is the one adoption audience where our own delivery record applies, because our engineers work this way every day. Sessions cover the prompts, the review habits, and where in the workflow the tooling makes a measurable difference.
Interviews and a technical review run in parallel, one on the systems and the data, the other on who would use what. Both finish in the same weeks, so the findings compare directly.
The readiness score, the system review, and the security read combine into one list, each item carrying what it blocks and what it costs to close. You choose what gets funded.
The policy arrives at one page per role, and the tool list marks what is approved for which data class. Your legal and security leads sign it, and the teams it covers read it before it ships.
A single team runs the chosen workflow, with a baseline taken before it starts. You get a measured change, or a documented reason the workflow was the wrong pick.
Ownership transfers with the roadmap, the policy, and the measurement method. You finish holding the gap list, the policy, the pilot’s numbers, and the case for what comes next.
Every recommendation on the technology line comes from an engagement that produces an artifact: a score, a findings register, a threat model, a classification. Nothing on that line rests on an interview alone.
The readiness work and the usage work run against the same company at the same time. A gap on one line often explains a number on the other.
The engineers advising you work with our AI-assisted development practice, which returns up to 50% faster delivery and up to 20% fewer bugs, with senior code review on every release.
96% employee retention means an adoption programme running across quarters keeps the same consultants, so the second rollout starts from what the first one learned.
The engineers who score your readiness are available to close the gaps they found. A roadmap written by people who would have to deliver it carries different estimates.
99% on-time delivery means the readiness score and the gap list reach you on the date agreed at scoping, which matters when a budget decision depends on them.
Yes, and most clients start with one piece. Each of the four technology engagements stands alone and produces its own output, and the people-side work runs with or without them.
One thing decides the entry point: write down the question your next steering meeting will ask.
If two of those apply, the readiness assessment usually answers both, because it scores the ground the others stand on.
The readiness assessment reaches a scored baseline and a ranked gap list within 14 days of kickoff, and the rest gets priced against what that list contains. When you want the range before committing to any of it, contact us and we will map your question to the engagement that answers it.
Three patterns look similar on a dashboard and have opposite fixes.
The measurement that separates them is task completion per user per week, which seat activation numbers hide. Most tenants expose it in a usage export. If your licence tier does not, our consultants set up the counting during the pilot and the answer arrives with the first results.
This is the point where it starts. Whether your company can deliver that use case is a separate question, answered in four parts: whether the data the use case needs can be reached and on what terms, whether your infrastructure serves that workload at the volume planned, whether the architecture clears your security review, and which rules bind the people who will work with the finished system.
The pattern we meet most often is a use case that survives selection and stops on data access. The data exists, and it sits in a system whose owner has no reason to grant access this quarter. Where the answer is to redefine the use case around data you can reach, that redefinition runs as AI product discovery. Where the selection itself needs reopening, AI consulting covers that.
Consolidation starts with one list of every tool in use, and the decision about a shared platform comes later. The inventory has to come from people before it comes from systems, because the tools nobody logged are the ones that matter most.
The framing that works is worth knowing before you start. Business units resist a central list when they read it as a takeover, and they contribute readily when the list is presented as cover, meaning the unit stops carrying the risk alone. Once the list exists, licence rationalisation usually pays for a meaningful share of the engagement, because the same capability turns out to be bought three times at three prices.
A readiness score rarely comes back as a verdict on the whole company. The result is a list of blockers attached to specific use cases, so the finding names which use case waits and what has to change before that use case can proceed.
Each blocker arrives with the reason it exists and the work that removes it, so your team sees which items close in days and which need a funded project.
Relevant delivers AI software development services and has spent more than ten years on data engineering, so blockers at any stage are the kind of work our engineers take on routinely.
Relevant Software answers both halves of that question, with proof on the technology side and numbers on the people side.
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