Applied AI Strategies is an AI strategy and implementation firm working with small and mid-sized businesses. We help you find where AI creates measurable value, plan how to get there, produce what the plan needs, and build the systems themselves.
Stop wherever you like. Plenty of clients take the first two and hand the rest to their own team.
The order does not change, though. Levels three and four exist to carry out a plan we wrote, so we do not build from someone else's. Most AI consulting stops at level two, and that is the gap this firm was built to close.
We help businesses implement artificial intelligence in practical, measurable ways that improve productivity, increase profitability, and reduce operational friction. We focus on real-world implementation rather than AI hype or experimentation.
In practice that means four things you can buy: a workshop that aligns a leadership team, an assessment that produces a ranked and costed roadmap, an implementation sprint that ships working systems, and an ongoing partnership for businesses that want the work to keep going. Underneath those sit five areas we build in, including a full AI search visibility practice with its own audit, rubric, and remediation tiers.
We do not sell pilots, proofs of concept, or innovation theater. Every engagement is anchored to one KPI agreed before work starts, and we report against that number until it is met.
Small and mid-sized business owners, founders, CEOs, managing partners, and operations leaders who want practical AI solutions that produce measurable business results.
Companies with real operations, real margins, and no patience for experiments. Businesses where the owner still knows how the work gets done, where a wasted quarter is felt rather than absorbed, and where a consultant who cannot explain something in plain English gets shown the door.
We are not built for enterprise procurement. We do not have a compliance department, we do not respond to hundred-page RFPs, and we would be a poor fit for an organization that needs six committees to approve a workflow change. That work is real and valuable and somebody else should do it.
Implementation may be delivered by CAL Design, a web design and development studio under the same ownership as Applied AI Strategies, at caldesigngroup.com. Two separate companies, one owner, so the person accountable for the strategy is accountable for the build. You will know who is building your project before you commit to it.
The difference is not that we have partners. Every consultancy has partners. The difference is that the build is scoped before you approve the strategy, and it stays our responsibility when it ships regardless of who writes the code. Most work goes to CAL Design. Where a job needs scale or a specialism they do not have, we bring in a team that has it and manage them, and we will tell you which before you commit rather than after. You are not handed a deck and a list of vendors to audition.
Website builds, landing pages, on-site schema, conversion work, and the technical remediation that comes out of an AI visibility audit all run this way. Even when a specialist team we select and manage is involved, we remain fully accountable for the process, the quality, and the results.
CAL Design has been building and managing website design and development projects for small businesses across a range of industries, since 2007. When it comes to implementation, we rely on their experience and route that work to them.
Four things, and they are load-bearing rather than decorative. Each one costs us something, which is the only way to tell whether a stated value is real.
Every assessment includes a written statement of what is not worth automating. Every service page on this site tells you when not to buy it. If a proven off-the-shelf tool solves your problem for a hundred dollars a month, we will tell you that instead of quoting a build.
Every client-facing finding in an audit is verified by a person against the underlying evidence before it reaches you. That is slower than generating a score, and it is the reason the score is worth reading.
You own what we build. No vendor lock-in, no proprietary layer that stops working when you stop paying. We would rather be kept than be needed.
Systems get built inside the software your team already uses, tested against real cases from your business rather than demo data, and sequenced so nobody is absorbing two changes at once. A system that requires a new habit from everyone is a system that quietly stops being used.
Discovery-first, always. Personas, messaging, channels, and build plans are outputs of discovery, never the starting point. We do not arrive with a template and fill in your name.
Concretely, that means four steps with a deliverable at the end of each: we assess by mapping workflows and interviewing the people doing the work, we prioritize by scoring every opportunity on return, effort, and risk, we implement in weeks rather than quarters against real cases, and we measure against the single number agreed before any of it started.
Governance comes with the work rather than as an upsell. Policy, guardrails, and safe-use standards are part of every build, because governance is not a product anyone shops for, it is a reason to trust the products.
We built and maintain our own AI search visibility methodology, which is a full measurement system rather than a point of view.
Technical Readiness
Can crawlers reach it
10 checks
Structured Data & Extractability
Can an answer be lifted
10 checks
Content & Freshness
Recent enough to cite
7 checks
Authority & Entity
Who else mentions you
7 of 9 live off your site
9 checks
AI Visibility
What they actually say
6 checks
Composite
AI Visibility Score
The number a Quarterly Recheck moves
42
checks across five pillars
It includes a five-pillar scoring rubric where every check must produce a finding, a business consequence, a specific fix, and the engagement that delivers it. A six-dimension Brand Gap framework for measuring the distance between how AI describes a business and how it needs to be described. A governed matrix of which answer surfaces get probed and which third-party sources get verified, reviewed quarterly. A written manual probing protocol, so two different people running the same audit produce comparable results. And three mandatory human checkpoints, including a findings review where a strategist verifies every client-facing claim against the evidence.
That system exists because the alternative is an opinion. Anyone can tell you that AI search matters. Being able to score it, evidence it, and rerun the identical measurement in ninety days to show what moved is a different kind of claim.
Because we do not yet have client results we are both cleared to name and able to measure, and publishing anything else would contradict the first thing we tell every client.
The temptation in this category is obvious. Anonymous case studies, unattributed percentages, and "a client in your industry" stories are standard, and almost none of them can be checked. We would rather show you the numbers we are willing to commit to at the start of an engagement, and let you judge us on whether those are honest.
Three client engagements involving AI search visibility work are underway right now. When they have produced measured results and those clients have agreed to be named, they will appear here with their names on them. Until then, this section will keep saying exactly this.
If any of the above sounds like the way you would want a firm to work, the next step is a conversation rather than a form-filling exercise. Thirty minutes, no obligation, and a straight answer about whether we are the right people for what you are trying to do.