AI Strategy & Implementation
A demo that works is the easy part. Production is where AI has to be secure, supported and paid for, and where a wrong answer costs something. We treat AI implementation as engineering: a design first, the use case built under your governance, and the cost of running it named before the build starts.
Who signs in, what data the model is sent, and where that data goes. A demo can skip these questions. Production starts with them.
Someone hears about it when it fails, someone can fix it, and the people using it know what to do when an answer is wrong.
Hosting and AI usage are monthly costs for each use case. Somebody in the business has to carry them, and they should know before the build starts.
It is tested against real cases before anyone depends on it, and where a wrong answer is expensive, a person reviews the output before it is used.
Step 1
We start with the decision or the workflow AI is meant to help with, who owns it, and what a wrong answer costs. The model comes later.
Step 2
After the discovery call, the engineer who would lead the work builds a first design from what you told us: screens if people will use it, or an architecture sketch with the real constraints marked if it runs behind the screen. It’s free.
Step 3
Your identity and access controls, your security review, your deployment process. If you have an IT team, we work to its standards, and it signs off on what ships.
Step 4
Before the build starts, we write down what the use case will cost to run each month and who in the business carries it, so the bill is a decision, not a surprise.
Step 5
Your team gets the code, the tests and a written record of the decisions behind them. If you want us to keep supporting it, we can.
Where AI doesn’t fit: some workflows need a rule, a form or a report, not a model, and those cost less to build and to run. We’ll say so.
One place AI helps without making the call: when the only person who can run a process is retiring, it handles transcription and first drafts while your expert and your team decide what is right. See retiring-expert knowledge transfer.
Your team keeps the roadmap and the standards, and we add engineers who build the use case under them.
Start with a readiness assessment that ends with a straight go or no-go answer, and a way to build it with us if the answer is go.
Every project works through the same set of drawings, each signed by the engineer who answers for it. Related reading: is the app you built with AI ready for real customers?
Don't just take our word for it, take theirs!
“They have excellent developers; they can articulate and design around a business problem. Many developers just develop without seeing the business solution, so it helps a lot to have a team like Clear Function.”
“I am confident Clear Function is one of the best in the business.”
“[Clear Function's] ability to build relationships with both their clients and their developers over the years is impressive. The quality never suffers.”
“Response was always immediate, no question we had was too big, too small, or made to feel "dumb" … They work in a very collaborative fashion … all hands on deck.”
“You guys CRUSHED IT!! … [We] look forward to our continued work together going forward!”
“Best [PCI DSS] requirement six walkthrough I’ve ever had. I wish they all could be this way.”
Learn more about how we collaborated with our clients to deliver successful solutions.
No rebuild required. How three AI-built business applications went from working to production ready.
Read More : When The App You Built With AI Starts Handling Real DataTell us about the AI use case and the systems it has to work with.
