Service
The model takes a few lines of code. The integration is the job.
Anyone can send a question to Claude or GPT by lunchtime. We build what surrounds that call: your data going in, checks on what comes out, a plan for the day the provider is down, and a meter you can read.
What the demo leaves out.
A demo runs once, on a good question, with its builder at the keyboard. Your business runs all day on questions with typos in them. Sooner or later the model answers in the wrong format, or takes its time, or says something false in a very calm voice.
So the work is a list of things going wrong. We'd design it so a malformed answer gets caught by validation before any other code reads it, a provider outage sends the request to a second model or to a person, and a customer who pastes a novel into the box hits a limit that stops the meter. When someone asks what the tool told a client last March, the log should say. That's API integration work with a stranger on the other end, and it runs on the same full stack as the rest of your application.
Two products, two design decisions.
MEP AI Pro takes a plain-language job description from an HVAC, plumbing or electrical pro and writes a line-item quote in seconds. The decision that matters is where the prices come from: the contractor's own rates. Ask a model to guess a price and it will guess. Hand it a rate book and it has less room to embarrass anyone.
MacroExits has AI read every marketplace listing two ways, the case for and the case against. One confident paragraph from a machine is a sales pitch. Two that disagree give a buyer something to think with. We'd build that kind of structure into any tool whose reader has money on the line.
Not every job calls for a pipeline of our own. Bleuwave.com, in custom PHP and MySQL, carries a Broccoli AI integration. When a finished product already does the job, we'd expect connecting it to be the cheaper build.
Three questions before we quote.
Does the task come up often? Can a person check the answer faster than they could write it? And what does a calm, wrong answer cost you? Drafting a quote passes all three, since an estimator can scan line items in the time it takes to pour coffee. So does pulling fields out of a messy PDF, or sorting inbound requests by urgency.
A task fails the third question when one bad answer would wipe out a season of saved labor. We leave AI out of those and say so before you spend anything. Sometimes the better build is a plain web application or an instant estimate tool running on rules you wrote.
What leaves your building.
An API call sends your text to someone else's servers. That deserves a decision and not a default. Before we build, we'd list what the task needs, strip out what it doesn't, propose a provider whose published terms you and your adviser can live with, and write down what goes where. The aim is for your records to stay in your own systems and the model to get only the slice the task requires.
We don't decide whether that satisfies your obligations. A law firm, a clinic or a financial adviser should put that written data flow in front of their own counsel or compliance adviser before launch. We'll hand it over. The interpreting is theirs.
The meter and the exit.
Model usage is billed by the provider, per use, and it climbs when your customers use the feature. We work out an estimate with you ahead of launch, set limits, and log what each feature spends. A cheap model can do the sorting while a costlier one handles the hard reading.
Models improve, prices change and providers retire things. We try to keep the model behind one seam in the code, so changing vendors is closer to an edit than a rebuild. The build itself starts from $575, as published. Pricing depends on complexity.
How it works
From one task to a logged feature.
Collect real samples
You hand us real examples of the task: old quotes, messy emails, the documents nobody likes. A model tested on tidy samples passes and proves nothing.
Write the checks first
We agree on what a good answer looks like before choosing a model. We'd turn your samples into a test set and rerun it whenever a prompt or model changes.
Wire it in
The feature goes into your application with validation, fallbacks, usage limits and a log. Anything you can't take back, such as a sent quote, waits for a person.
Watch the first real runs
We read the output and compare the bill with the estimate, then tighten the prompt. The log shows you the same thing it shows us.
They made the technical side feel clear.
We talked about goals first — then platforms, content, search, and automation.
They helped us turn a loose idea into a working digital product — asked sharp questions, found the hidden workflow problems, and built a system our staff could use without a long training curve.
Price it in a minute
Price the wiring.
Answer a few questions and a starting price shows on screen. It covers the build. Model usage goes on the provider's bill, and we'll estimate that with you. The developer who would do the work is the one who follows up.
- Every price published on the pricing page
- 0% financing with 25% down. Financed projects are hosted on the DataCram platform for the length of the plan. Shopify stores can't be financed.
- One developer from first call to launch
What do you need built?
Pick what fits (pick any)
Rather talk? (480) 525-7582. 0% financing on any project, 25% down. See all pricing
AI & LLM Integration: questions we hear a lot.
Which models do you use?
Claude, GPT and their peers. Our AICiteBeam build checks ChatGPT, Claude, Gemini and Perplexity every day, so the differences among them aren't news to us. We choose per task, weighing quality against speed and price, and we keep the choice changeable.
Do you train a custom model on our data?
Rarely. The usual approach hands the model your catalog, rates or documents at the moment it answers. Updating a price then means editing a record. In our view training is slower and costlier, and we'd want a specific reason before recommending it.
What happens to our business data?
We settle that with you before building. We'd send only what the task needs, propose a provider whose published terms you're comfortable with, and give you a written list of what leaves your systems and why. If your business has rules about client data, your own counsel should review that list.
How do you handle a wrong answer from the model?
Plan on it happening. Answers get checked against your data and format before they act, and a person owns anything final. That's why we like drafting work: a wrong draft costs a minute of somebody's time. When the task needs steps taken on its own, see how we limit agents.
How much does it cost to build and run?
Building starts from $575. Pricing depends on complexity. Running costs are usage-based and billed by the provider. We put limits and logging in place and estimate the monthly use with you before launch. See pricing.
Can it connect to the software we already use?
If that software has an API, usually yes. We check what each system allows before quoting, and ServiceTitan's API has its own page here. Its API Terms restrict AI use of data from that API, so that case gets checked with ServiceTitan first. If the job is moving data around with no judgment involved, plain automation is cheaper and you should buy that.
Bring one task and a folder of examples.
We'll tell you whether a model belongs in it. If it does, we'll quote the wiring. If it doesn't, you've lost one conversation.