An AI agent reads messy information, like emails, documents and call notes, decides what it means, and takes the next step inside your systems. We build them for one job at a time, train them on your rules and your real examples, and manage them after launch, because switching one on is the easy part. Anything we build, you own.
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The best uses are steps a person does the same way every time, but software alone can't, because the input is unstructured: a supplier's email, a scanned document, the notes from a call.
Agents draft, sort and suggest. They shouldn't make clinical, legal or financial decisions on their own, and they shouldn't send anything under your name without approval. Most of a workflow is still ordinary automation, moving records and sending reminders on rules you can check. The agent handles the judgment-heavy reading, and hands anything unclear to a person with the evidence attached.
Emails, documents, photos and call notes, in whatever layout they arrive.
Which job this belongs to, what changed, what's missing. Below a confidence line, it asks instead of guessing.
Updates the record, drafts the reply, flags the gap, inside the systems you already run.
Anything that spends money, speaks for you or changes a commitment waits for a tap in the app or the inbox.
Every agent sits inside a workflow with approval steps, an exception queue and a fallback, and where it needs a person, that step lives in a phone app or inside Outlook or Gmail. These are the agents we're asked for most. Yours may be one of them, or something we haven't listed.
Reads supplier confirmations, permit notices, client replies and records from other offices, works out which job, matter or patient each one belongs to, and says how sure it is. Anything uncertain goes to a review queue instead of a guess.
See the build →Reads authorized email and meeting notes for changes agreed on a call and promises made in a meeting, proposes each one to the person who made it, and checks the confirmed ones against the scope, the tasks and the billing.
See the build →Reads what a crew or a client uploaded, confirms the required items are there, and flags anything unreadable or out of date. It confirms something is present, not that the work was done well.
See the build →Reads what a new inquiry is asking for, fills in who they are, scores and assigns the lead by your rules, and drafts a personal reply by text or email while the rep gets an alert.
See the solution →Pulls the budget, decision makers, pain points and next steps out of a transcribed call, lands the notes on the right deal, and drafts the follow-up for the rep to review and send.
See the solution →Reads every campaign on Google, Microsoft and Meta, sorts visitors into higher and lower intent audiences, and moves a limited budget toward what is producing leads. A person approves anything that spends beyond the limits.
See the case study →Training an agent doesn't mean building a model. The models come from Anthropic and others. Training means teaching one your job, with five things, and it isn't finished until the agent's answers match your team's on real work.
What to match, what to flag, when to stop and who decides. We sit with the people who do the work, because the real rules are rarely the ones in the manual.
The agent is tested against real emails, documents and calls from your business, not made-up ones. Every piece has to pass before it goes near live work.
Templates, rate cards, checklists and procedures are given to the agent to read from, so a scope comes from your rate card and a checklist from your firm, not from whatever the model remembers.
Spend limits, confidence thresholds, and the things it may never do: send under your name without approval, make a clinical, legal or financial call, or write to a live system when a run can’t be trusted.
A person approves the output, and every correction goes back into the rules and the examples. The agent runs beside the old way until its answers match, and only then does it take over.
Agents draft, sort and suggest. A person approves anything that matters.
Models change, inboxes change, and the exceptions an agent meets in month six aren't the ones it was tested on. Managing an agent is the work that keeps it trustworthy. It comes two ways: ongoing support for one agent, or a fractional AI Automation Director for your whole AI program.
Alerts on the agent and the systems around it email you and us when something breaks or stops.
API, token and subscription costs tracked against a budget, with unusual spikes flagged.
Providers retire and replace models. Each change is retested against your examples before the agent moves.
Exceptions are reviewed, the rules and examples updated, and models fine-tuned where that earns its place. That’s maintenance, not a new project.
Hosting help, patches, staff changes and the incremental improvements an agent needs after launch.
Written rules for how your staff use AI, kept current as the tools change, on the Director retainer.
Optional, and most projects sit at the lower end. Monitoring, fixes, model changes, tuning and a set amount of improvement for an agent we built. Support is optional. Take the build and run it yourself if you’d rather, and you still own the code, the repository and the documentation.
See what support covers →Someone to run every agent and AI tool you have: vendors and contracts, the roadmap and the AI use policy, spend, maintenance of completed projects and a monthly report. Support for our builds is included.
See the tiers →Each of these is part of a system we built for a client, written up in full with its baseline, results and running cost.
AI agents sign in to each store’s CMS, create the pages and posts, build their on-page SEO and set the schedule. The system refuses to write when a run can’t be trusted. The monthly work per dealership went from about 14 hours to about 2, estimated.
See the case study →Claude reads every campaign on Google, Microsoft and Meta, sorts visitors by intent and moves the budget toward what produces leads. Dow Family Automotive got 43% more leads on the same ad budget.
See the case study →Every patient moves through staged checklists, calls are recorded and summarized, and records go for a compliance review a person verifies. Intake and reorder needed 30% fewer offshore staff, and the order error rate fell below 1%.
See the case study →The models an agent thinks with, and the frameworks that give it tools, memory and limits. They run in accounts you own, and where patient data is involved, only platforms under a business associate agreement are used. The rest of what we build with, and the systems we connect to, are on What we build.
Repetitive admin, missed follow-ups and disconnected systems take time away from running your business. Bring us one process that slows your team down. We'll help you understand what could improve, how much time you could recover, and what it might cost to implement.
Get My Free Workflow ReviewFree 30-minute conversation about how your business runs. Practical recommendations. No obligation.
Not sure where to start? That's what the free call is for. We'll walk through how your business runs and find the easiest wins, the places where automation and AI agents can take work off your team first.
Prefer to talk? Call (435) 243-5808 about your workflow, or .
