Get Your Free AI Readiness Assessment

1. Your current workflows

We start with how work actually moves through your business today: the email that becomes a quote, the intake form that becomes a file, the question a customer asks for the hundredth time. AI earns its keep on work that is repetitive, text-heavy and slowed down by people searching for information.

Mapping this first keeps the conversation grounded. It stops the project from becoming a search for somewhere to use a tool you already bought, and it surfaces the hours your team would most like back.

2. The use cases worth doing first

Not every idea deserves a budget. We rank the candidates by the value they return, the effort they take and the risk they carry, so the first project is one your team will notice and your finance lead will approve.

Common early wins include drafting routine documents, answering internal questions from your own policies and manuals, sorting inbound email and forms, and summarizing long files. The ranking is specific to you, not a generic list.

3. Your data and security posture

This is where most AI projects go wrong. Before a model sees anything, you need to know which data is public, which is internal, and which is regulated, such as controlled unclassified information, protected health information or client-privileged material.

We look at identity and access, logging, and where data flows today, then mark what can safely be used and what must stay out of reach. Our team has protected North Carolina businesses since 2002, so security is the starting point, not an add-on.

4. Hardware and model recommendation

Private AI runs on hardware you control, and sizing it well is the difference between a smart investment and an expensive space heater. We match the workload to the right class of machine and to open-weight models such as Llama, Qwen and Mistral.

We test this hardware ourselves and publish the results in our LLM benchmarks, so the recommendation is based on measured performance rather than a vendor brochure.

5. A phased rollout plan with budget

Big-bang AI projects stall. We lay out a sequence instead: one contained quick win, measured against how the work is done today, then expansion to the next use case once the first one proves itself.

Each phase comes with a budget, so you can decide how far to go and when. You can run the plan with your own team, with us, or with another provider. It is yours either way.

6. Optional prototype and pilot

If you want to see it working before committing, we can build a prototype of the top use case and run a small pilot with real users and non-sensitive data. That turns the plan into something your team can try, question and improve.

The pilot is optional and quoted separately. The assessment itself is free and carries no obligation to continue.

You are likely ready to start

• Repetitive, text-heavy work eats hours every week: quotes, intake, reports, routine email.
• Knowledge is locked in documents people search through: manuals, policies, past proposals, case files.
• Someone can own a pilot and judge honestly whether it helps.
• Basic security is in place: managed devices, multi-factor authentication, and a clear idea of where sensitive data lives.

Fix the foundation first

• Staff paste company or client data into free chat tools, with no written rules.
• No one can say which systems hold regulated data such as CUI or PHI.
• Shared passwords or unmanaged devices, or no multi-factor authentication.
• AI is being bought because a demo looked good, not because a specific workflow needs it.

1. Leaking data through free tools

The most common AI risk is not a hacker. It is a well-meaning employee pasting a contract, a patient note or a price list into a public chatbot to save ten minutes. Once that data leaves, you cannot pull it back. The assessment identifies where this is likely to happen and what to offer your team instead.

2. Paying per seat for tools few people use

Per-seat AI subscriptions add up quickly, and licenses often go unused once the novelty wears off. Before you sign up the whole company, it pays to know which roles actually benefit and whether a private deployment would cost less over time.

3. Buying the wrong hardware

Overbuy and you pay for capacity that sits idle. Underbuy and responses crawl, users give up, and the project gets labeled a failure. Matching the hardware to the actual workload is one of the most valuable parts of the plan.

4. Starting with the hardest problem

Ambitious first projects tend to stall, and a stalled first project makes the next one harder to fund. A contained quick win builds confidence, produces real results, and teaches your team how to work with AI before the stakes rise.

5. Skipping the rules

AI without guardrails is how data ends up in the wrong place. Your team needs to know which tools are approved, what data is off limits, and who to tell if something goes wrong. If you do not have a written AI policy yet, the assessment flags it, and our free template is a good starting point.