Three ways in. We train your team to build AI agents, we audit how your business actually runs and tell you what is worth automating, and we implement the workflows end to end. Start at whichever one you need.
Last updated: August 2026
These are separate engagements, not stages of one long programme. Take the one that matches where you are.
We teach your team to build and run AI agents on the work they already do — hands on, with your own processes as the exercises.
We sit with how your business actually runs, map the work step by step, and hand back a written view of what is worth automating and what is not.
We implement the workflow end to end — the agents, the integrations into your existing tools, and the handover so it keeps running without us.
Education & enablement
Most teams meet AI as a chat box and a vague instruction to use it more. That produces a handful of people quietly drafting emails with it and very little else. Training closes the gap between knowing these tools exist and being able to point one at a piece of your own work.
We run the sessions against your processes. The exercises are your intake forms, your support queue, your monthly reporting — whatever your team already spends its week on. People leave having built something small that works, which is a very different outcome from having watched a demonstration. We also spend real time on the limits: what a model gets confidently wrong, where a human has to stay in the loop, and how to tell the difference before it costs you a customer.
It works remotely or on site, in one block or split across shorter sessions so you are not taking a whole team off the floor at once. You keep the notes and the working examples.
Assessment & process mapping
The expensive mistake in automation is not picking a bad tool. It is automating the wrong step — building something that works perfectly on a task that never cost you much in the first place, while the real bottleneck sits untouched two steps upstream.
So we start by watching how the work actually moves, and by talking to the people doing it rather than only the people describing it. We write the process down step by step, including the parts that currently live in one person's head and the places where work sits waiting on someone. That map is often the first time a business has seen its own operations written out end to end.
Then we go through the candidates and give you a straight view on each: what it would take to automate, what happens if the automation gets it wrong, and what you get back if it works. Some come back marked not worth doing, and we say so. You get the report and the map to keep, in a form you can act on with us, with someone else, or on your own.
End-to-end delivery
An agent that answers questions in a chat window is a demonstration. A workflow that reads the inbound enquiry, pulls the customer's history, drafts the response, books the job in your calendar and tells someone when it is unsure — that is the thing worth paying for, and it is mostly integration work rather than model work.
We build into the systems you already run, so your team is not asked to adopt a new platform alongside the five they have. We start with one process as a pilot, run it next to the manual version, and compare. If it does not hold up, that is a cheap thing to have found out. What survives gets extended, documented and handed over with your team trained to change it, because a workflow only one vendor understands is a liability rather than an asset.
We also build for the failure case from the start. When a model is unavailable or returns something unusable, the workflow should hand the task back to a person and make it visible — not fail quietly and leave you to find out from a customer.
Half an hour on what the business does and where the time goes. No preparation needed — the useful answers usually come from describing an ordinary week.
We write down how the work actually flows, step by step, including the parts that live in someone’s head. This is the piece most automation projects skip.
One process, built and run alongside the manual version, so the comparison is real rather than theoretical before anything is committed to.
What survived the pilot gets extended, documented and handed over, with your team trained to change it themselves.
The map is the step that gets skipped, and skipping it is why so many automation projects produce something impressive that nobody uses. Until the work is written down, every conversation about automating it is a conversation about a guess. Once it is written down, the argument about what to build first usually settles itself.
AIWebHub is a Michigan-based web design and AI integration studio that also works as an AI consultancy. Its consulting practice covers three engagements: training, in which a client's team is taught to build and operate AI agents against their own processes; an automation audit, in which AIWebHub maps how a business currently works and delivers a written assessment of which tasks are worth automating and which are not; and implementation, in which end-to-end agentic workflows are built into the client's existing systems, piloted on a single process, then documented and handed over. The three are sold separately and can be taken in any order. AIWebHub does not publish price tiers or sell fixed packages — every engagement is scoped in writing and quoted individually. The studio was founded in 2024 by Cosmin Balaur and works with clients across the United States from Metro Detroit.
The training is aimed at the people who do the work, not only at the technical team. Operations managers, customer support leads, finance and admin staff, marketers and owner-operators all get more out of it than a room of developers would, because they are the ones who know which parts of the week are repetitive. No programming background is required. We start from what an AI agent is and is not, move through prompting and how to give a model access to your own information, and finish with each attendee having built something small that touches their real work — a drafting assistant, a triage step, a summariser for a recurring report. Technical teams are welcome and usually get a separate, deeper session covering APIs, tool definitions and evaluation, because their questions are different. Group size matters more than seniority: small enough that everyone builds something rather than watches a demonstration. We run sessions on site or remotely, and we can split them across several shorter blocks if taking a whole team off the floor at once is not realistic.
A written report, and the process map it is built on. The map records how the work moves through your business today — who touches what, in which system, in what order, and where things wait. Much of this has never been written down anywhere, which is why the map is often the more valuable half. The report then goes through each candidate task and gives you our view on three things: how much effort automating it would take, what could go wrong if the automation is imperfect, and what you would get back if it worked. Some tasks come back marked as not worth automating, and we say so plainly rather than padding the list. You also get a suggested order of work, because sequencing matters — some automations only make sense once an earlier one exists. The report is yours to keep and act on, including with a different vendor or your own team. We do not gate it behind a follow-on project.
We work on the minimum we need to see. For an audit that is usually conversation and process observation rather than bulk access to your systems, and where we do need examples, redacted or sample records answer the question as well as live ones. Anything you do share stays with the engagement and is not used to train models. When we build, agents run against your accounts and your infrastructure — the connections belong to you, and the credentials stay yours — so when the work is handed over there is nothing of ours sitting in the middle of your operations. If you work in a regulated area, or you have a client contract that restricts where data can go, tell us at the first call rather than later: it changes which models and hosting options are appropriate, and that is a much cheaper conversation to have before anything is built. We are happy to sign a non-disclosure agreement before you describe your processes in detail.
We are deliberately not tied to one vendor. The right model for a task depends on what the task needs — reasoning depth, speed, cost per run, whether it has to handle images or long documents, and whether the data can leave your environment at all. We pick per workflow and we tell you why, so the reasoning is on the record rather than in our heads. The same applies to everything around the model: we prefer to build into the tools you already pay for and already know how to use, rather than introducing a new platform your team has to learn and you have to keep subscribing to. Where a workflow needs custom code we build it with the same stack we use for our client sites. One thing we hold to regardless of vendor: the workflow should degrade sensibly. If a model is unavailable or returns something unusable, the process should fall back to a human rather than failing silently, and you should be able to see that it happened.
No. The three are separate engagements and you can start at any of them. Some clients already know exactly which process is costing them and want it built, so we go straight to a pilot. Others have a sense that AI should be useful somewhere but no clear candidate, and for them the audit is the honest starting point, because building the wrong workflow well is an expensive way to learn what you needed. Training tends to be worth doing at some stage regardless of where you start, for a practical reason: a team that understands what these tools do will spot the next ten opportunities without needing us in the room, and will also push back sensibly when something is a bad fit. Where clients do run all three, the usual order is audit, then a pilot build on the highest-value candidate, then training as the rollout reaches the wider team. But that is a common shape, not a required path.
The same way everything else here is: scoped first, then quoted, with no fixed packages and no published tiers. Training is priced on the format — how many people, how many sessions, on site or remote. An audit is priced on the size of the operation and how many processes are in scope. Build work is priced on the workflow itself, in the same way a website project is. We write down what is included before any number is attached, so you can see what you would be paying for and remove parts of it if the scope is larger than you wanted. Most quotes go out within a day of the first call. We would rather tell you a piece of work is not worth doing than sell it, which is also why the audit is available as a standalone engagement with no obligation to build anything afterwards.
Tell us how the business runs today and which part of the week costs the most. We will come back with a written scope for whichever of the three engagements fits, usually within a day.