01
The assistant answers, but nobody does the task
A person asks, copies the answer and pastes it into the system. The model did the thinking, but a human still did the work, moving data from one window to another.
Custom software with artificial intelligence
Put artificial intelligence to work on what your team does by hand today
We do not sell AI licenses or a chatbot bolted onto your website. We write software that uses language models to do one concrete task in your operation —read, classify, draft, decide— and we leave it connected to the systems where your information actually lives.
We write the application ourselves, we do not resell it
The AI goes inside your process, not in a separate tab
Our own day-to-day operation runs on agents we built
The breaking point
You already tried artificial intelligence and the work still gets done the same way
Almost every company we talk to has already used an AI assistant. They wrote better emails and summarized a meeting or two. And that is where it stopped: the task that actually eats hours is still done by hand.
02
Generic AI knows nothing about your company
It does not know your prices, your customers, your approval rules or your history. Without access to that data it answers in general terms, and general terms are no basis for a decision.
03
It worked in the demo and not in the process
The proof of concept went fine with three hand-picked examples. When the odd case arrived —the wrong one, the incomplete one— nothing had been planned for it.
What we do
Three ways to put AI inside the operation, not beside it
We build custom applications where the language model is one component of the software, not the product. These are the three most requested by companies that already have systems running.
Agents that carry out the task
Software that takes in a case, decides what to do using your business rules and leaves it done in the system: the record created, the reply sent, anything doubtful escalated to a person.
Reading documents and emails
Invoices, purchase orders, delivery notes, requests that arrive by email. The model extracts the fields, the software validates them against what you already have, and whatever does not add up gets flagged, not invented.
Assistants over your own information
Ask in plain language and get the answer with its source: which document, which record, which date. And with permissions, so each person only reaches what they should.
This is a branch of custom software development: same team, same way of working. If what's missing is connecting the systems to each other before automating anything, that's systems integration.
How it's built
What's inside an AI application that holds up day to day
The difference between a demo and something that runs is in the four pieces around the model. They are what makes the system reviewable when someone asks why it answered that.
The model is a replaceable part
We pick whichever fits the case, and it can be swapped without rebuilding the application. We do not tie your operation to a single model provider.
Controlled access to your data
The agent queries your systems through the same doors an authorized person uses, with permissions and a log of what it read.
Written rules and limits
What it can do on its own, what it has to confirm and what it must never touch. That is defined before any code is written and it stays in the scope.
Human review where it hurts
Where a mistake costs money or credibility, a person approves the final step. AI reduces the work, not the accountability.
The example we know best is our own: DSC's day-to-day operation runs on a fleet of agents we wrote and maintain ourselves. See the systems we run.
Scope
Four answers define the size of your AI project
We do not publish a price because the range is enormous: there are single-task automations and there are complete systems. These four things are what move the number, and they are reviewed with you before a proposal exists.
Which concrete task it will do
"Use AI" is not a scope. "Classify the requests that come in by email and create the case" is, and it can be quoted.
Where the information comes from
If the data is already in a system we can talk to, it is a connection. If it lives in folders and in someone's head, it has to be organized first.
What happens when it gets it wrong
A wording mistake can be fixed. A mistake in a price or in inventory cannot. That is what defines how much validation and how much human review has to be built in.
Who runs it afterwards
We can hand it over documented to your team, or keep running and tuning it. Those are two different scopes and it is worth deciding up front.
In the diagnostic we go through these four points, and out of that comes a proposal with scope, deliverables and commitments in writing. Book a free assessment.
Fit
When we will tell you not yet
You want a chatbot on the website and nothing else
If the goal is answering visitors' common questions, there are off-the-shelf tools that do it for a fraction of what custom development costs. That is the route for you.
The data is not organized yet
If the information lives in personal spreadsheets and every department has its own version, no model fixes that. The foundation has to be sorted out first; we say so before selling anything.
Questions
What people ask us before starting with AI
Which AI model do you use?
Whichever fits the task, and it is written into the proposal. We choose based on what the case needs —reasoning quality, language, cost per use, whether the data may leave your infrastructure— and the application is written so the model can be swapped later without rebuilding it.
Will my data be used to train a public model?
That is an architecture decision, not a detail. The diagnostic defines what information may go out to a model provider and what may not, and that boundary is put in writing in the proposal along with the technical alternative for sensitive data.
How much does it cost and how long does it take?
It depends on the four scope points above. We do not give a figure on this page because it would be made up: it comes out of the diagnostic and is put in writing, with deliverables and timelines, in the proposal.
Is this worth it if my company is not big?
It is worth it when there is a repetitive task with enough volume for automating it to pay for itself. The size of the company matters less than the size of the task, and that gets measured before we propose anything.
And what if the AI gets it wrong?
We assume it will happen and design for it: validation against your own data, a log of every decision so it can be reviewed, and human approval at the steps where a mistake costs. What we do not do is hand over something that decides on its own and leaves no trace.
Next step
Tell us which task is eating your hours
With that we will tell you whether it can be automated with AI today, whether ordinary software is the better fix, or whether it is not the moment yet. We would rather say so than sell you a project that will not pay for itself.
Prefer to write to us? Tell us what you need.