The honest question first
Not every process gets better with AI. So an engagement with us starts with whether it will actually pay off for you. If the answer is no, we'll say so — cheaper for you, and more credible for us.
Where it does work, it's usually the same pattern: work that comes up often, takes a lot of time, and where the answer sits in documents or data you already have.
Approach
How an engagement works
- 01
Mapping the work
Together with your staff we walk through an ordinary working week, task by task: what comes in, what happens to it, where the time goes. What really happens, not what the procedure says.
- 02
An honest label per task
Each task gets one of three labels: AI takes it over, AI helps a person, or AI adds nothing. That last one is more common than you'd think — and it's useful to know.
- 03
Capturing what recurs
What repeats, we capture: workflows that do the same thing every time, reusable instructions with your templates, house style and wording, and agents that complete sub-steps on their own.
- 04
Coaching until it runs
We work on your own documents and systems, alongside your team, until the new way of working runs without us. Only then is it done.
What it delivers
Workflows that deliver the same result every week
No dependence on whoever happens to be good at prompting. The process is captured and produces predictable output.
Consistent output
Quotes, emails and reports in your house style, with your templates and wording — regardless of who starts the task.
Agents that finish routine work
Looking up information, combining data, preparing a first draft. Decisions and exceptions go to a person, with the context attached.
A team that runs it themselves
Your staff understand what's happening and can adjust it. And along the way, steps that were never really needed disappear.
Data and privacy under control
Where your data lives, who can reach it, and what goes to which model. For many companies this is the deciding question — rightly so. We choose the setup that fits, up to and including models inside your own cloud environment.
Typical examples
- Sorting incoming emails, gathering the right information and preparing a reply
- Drafting quotes based on earlier quotes, price lists and the customer's request
- Compiling reports from multiple sources, in a fixed format
- Summarising case files and flagging missing documents
- Making internal knowledge searchable — with sources cited in every answer
What we care about
- Start small. One process that really works is worth more than a broad platform nobody uses.
- No answer beats a made-up answer. An assistant should say when it doesn't know, and show where its answer comes from.
- Clear boundaries for agents. What an agent may do, what it can access and when it must involve a person belongs in the design — not as an afterthought.
- No lock-in. You should be able to switch vendor or model without starting over.
- Bring people along. AI that works against the way people work gets quietly ignored.
Contact
Which process should we tackle first?
Describe one task that comes back every week and who does it today. You'll get an honest view of where the first gains are — even if AI isn't the answer.
You'll get a reply within one working day.
- Emailinfo@leart.be
- Phone+32 89 24 33 50
- LinkedInFollow us on LinkedIn
- RegionBilzen-Hoeselt · Limburg, Belgium
- CompanyLeart · BE 0769.426.863