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Relevant · Ai that earns its place

Where will AI actually pay back, and what is it already exposing?

Ai that earns its place, with the risk handled. Most AI plans stall on a blind spot: nobody has mapped where the hours and the risk actually are. We find the processes worth automating, put governance around the ones that touch sensitive data, and skip the pilots that would never pay back.

Talk to a Principal EngineerCheck your Relevant blind spots
The solution

How we fix it, in three steps

  1. Step 1: Measure

    Find where the hours and the data risk really are.

  2. Step 2: Pilot

    A proof of concept in weeks, measured against a baseline.

  3. Step 3: Govern and scale

    Guardrails and a policy, then production for what paid back.

What we usually find, and what we do about it

Your challenge

The hours nobody measured

The best first AI use is usually re-keying between systems, not a chatbot.

How we solve it

We measure where the hours are before recommending any AI.

AI Readiness Assessment & Roadmap
Your challenge

The data already leaving

People paste work into AI tools before any policy exists.

How we solve it

A policy, an approved-tools list and guardrails before more data leaves.

AI Governance & Guardrails
Your challenge

The pilot that never pays back

Proofs of concept without a success measure agreed up front.

How we solve it

Every proof of concept starts with an agreed success measure and a baseline.

AI Proof of Concept
Proof

What changed for clients

See all 7 Relevant stories

Relevant

In their words

Clients' own words, shared with their permission. Some chose to name their company.

Over the past two years Vizius has led three IPS AI data projects:

GenAI Image Extraction Pilot (2024-25): Image-to-Text engine converts poor quality metal nameplates into structured data; now live at one repair center, with roll-out to our other facilities in the near future.

Financial Intelligence Pilot (2024-25): analytics layer analyzes shop-level P&L, job-margin, by product, end-market and customer; early tests show sharp and broad insight potential.

Data-Normalization PoC (2025): auto-mapping data normalization PoC shows exciting potential to speed ERP integration for new acquisitions.

Across these efforts Vizius has combined an invaluable empathy for the end-user experience, a strong understanding of AI capabilities, solid project discipline and a security-aware approach.

Scott MelzerCIO, IPS

Vizius joined CRAG, a leading MSSP/MSP, to directly interact with their clients, leading hands-on sessions that translated core AI concepts into practical use-cases. The result was a shared understanding of where AI fits, quicker path from idea to pilot and more concrete understanding of what AI can and can't do.

Brad HamlettPrincipal, CRAG

Where most clients start

AI Readiness Assessment & Roadmap: We map where the hours and risk sit, check which data is ready, cost the use cases, and set the order to do them in.

All 5 Relevant offerings

  • "Which AI ideas will actually pay back here, and in what order?"

    We map where the hours and risk sit, check which data is ready, cost the use cases, and set the order to do them in.

    More about AI Readiness Assessment & Roadmap →

  • "Who is accountable for turning our AI ideas into results?"

    A named AI leader who turns scattered ideas into costed use cases, sets the governance model, and runs the program with your team until it delivers.

    More about Fractional Chief AI Officer →

  • "Will this work on our data before we bet a budget on it?"

    We build a scoped pilot on one real process with your data and measure whether it pays back before you commit to production.

    More about AI Proof of Concept →

  • "Where are our people re-keying data that a machine should be moving?"

    We engineer automations for hours-heavy work such as re-keying, reporting, payroll and document routing, put them into production, and keep them running.

    More about Back-Office AI Automation →

  • "What are our people already putting into AI tools, and what stops sensitive data going with it?"

    We write the AI use rules and deploy guardrails that filter what goes into and comes out of your language models, so adoption does not outrun your data rules.

    More about AI Governance & Guardrails →
Who you'll work with

A Principal Engineer, not a salesperson

Likely lead: David Hyde-Volpe or Cameron Cromer. You talk to the engineer who does the work on Relevant.

Start on your own: the blind spot check

Four questions for a quick read on all four dimensions, then three more on Relevant if you want to go deeper.

Take the check
Glenn JohnsonDavid Hyde-VolpeMax DingesCameron Cromer

Speak directly to an engineer. We're based at 141 Traction Street, Greenville, SC.

Meet the team
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