AI automation
AI automation for businesses: from manual work to flows that run themselves
We build language models into your systems so documents get read, tickets get sorted, replies get drafted and data gets moved without anyone doing it by hand. As an AI consultancy in Stockholm we help you find the processes where it actually pays off, then build the solution at a fixed price.
- Built into your existing systems
- A person approves where it matters
- Your data stays under your control
What AI automation means in practice
AI automation is letting a language model do the interpretation work that used to need a person: read an email and understand what the customer wants, pull amounts and dates out of an invoice, write a first draft of a reply. What sets it apart from ordinary automation is that the input does not have to be structured. What sets it apart from a chatbot is that it happens inside your systems, not in a window on the website.
Documents that read themselves
Invoices, contracts, orders and applications are interpreted, the data is extracted and lands in the right field in the business system. A person reviews the exceptions, not every document.
Tickets that sort themselves
Incoming email and forms are classified, prioritised and routed to the right person with a suggested reply. Support starts from a draft instead of an empty field.
Replies and documents that get written
Quote drafts, order confirmations, summaries of long threads and reports are generated from your own data. Staff edit and approve.
Search across your own knowledge
Questions against your contracts, manuals, procedures and case history are answered with citations, instead of someone digging through ten systems.
Checks that never get tired
Every order, invoice or report is reviewed against your rules before it moves on. Anything that looks wrong is flagged for a person.
AI agents for whole flows
When several steps hang together, such as receiving an enquiry, checking stock, creating a quote and booking delivery, we build an agent that drives the flow and stops at the decisions you want to make yourselves.
When AI automation pays off, and when it does not
We turn down about as many AI projects as we take on. The technology is impressive, but it only pays for itself on the right kind of task. This is how we decide.
Pays off when
- The task is done many times a week by several people
- The input is text, documents or email that varies in form
- An error can be caught and fixed before it has consequences
- The rules are clear enough to write down
- The data lives in systems you control
Does not pay off when
- The task is rare or already takes a few minutes
- A single error is costly and hard to detect
- The input is already structured, then ordinary integration is enough
- Nobody can describe what a correct result looks like
- The customer expects a person at the other end
How we build AI automation that holds up in production
The hard part is not getting a model to solve the example in the demo. The hard part is having it solve the thousand variants in reality, and knowing when it does not.
Map one process
We pick one bounded task, measure how long it takes today and collect fifty real examples. Not a hundred processes, one.
Prototype against real data
Within one to two weeks we test the model against your examples and measure accuracy. If it is not high enough we say so and stop there.
Build with a human in the loop
The solution is built into your system with clear points where a person reviews and approves. Everything the model does is logged with its inputs.
Operate, measure and adjust
We track accuracy and cost per run in production, adjust when models are updated, and remove review steps once you trust the results.
Your data and your models
The most common question we get is where the data goes. The answer depends on what you need, and we build so that you can switch.
Models from several providers
We work with models from OpenAI, Anthropic and Google through their business APIs, where your data is contractually not used for training. We build so the model can be swapped without rewriting the solution.
Data inside the EU where required
For sensitive data we use the providers' EU regions or models that run in your own environment. Which one fits is decided with you before we build.
Traceability
Every decision the model makes is stored with input, output and the model version used. You will need that the day someone asks why.
Cost you can see
Every run costs a known amount. We measure it from day one so you know what the automation costs per document, ticket or reply.
What does AI automation cost?
The price is set per project and fixed before we start. What drives it is how many variants of the task have to be handled, how much of your systems is already available as an API, and how strict your requirements for human review are.
Variation in the task
One invoice type from ten suppliers is easy. Free-text email about anything is hard. The prototype shows where you are.
How open your systems are
If the business system has an API, reading and writing is quick. If not, we build the route first.
Review flow
An interface where staff approve, correct and teach the model takes time to get right, but it is what makes the solution get used.
Ongoing model cost
Runs cost per call. We quote that cost separately so you see the whole picture, not just the project price.
The prototype against your real data is the first thing we deliver, and it is deliberately small. You know within two weeks whether it holds, before you commit to the rest.
ContextAI automation is part of our custom software development.
RelatedIf the input is already structured, no language model is needed. Read about system and API integration.
Common questions about AI automation
What does AI automation cost?
It depends on how many variants of the task have to be handled, how open your systems are and how much human review you want. We always start with a small prototype against your real data, so you know within two weeks whether it holds. Then you get a fixed price for the full solution, with the ongoing model cost per run quoted separately.
What is the difference between AI automation and a chatbot?
A chatbot is a window where a person types questions. AI automation is the model working inside your systems without anyone asking: reading the invoice that arrived, sorting the email, writing the draft. The chatbot needs someone sitting at it. The automation saves time precisely because nobody has to.
How do we know the model is not making things up?
By never letting it make decisions that cannot be checked. Every answer is stored with its inputs, the model is forced to cite a source when answering questions, and at first a person reviews everything. We measure accuracy in the prototype against your own examples before building anything, and in production we track it week by week.
Where does our data end up?
With the model provider we choose together, through their business API where data is contractually not used for training. For sensitive data we choose EU regions or a model that runs in your own environment. You get a clear picture of the data flow before we build, and we build so the provider can be swapped.
Which processes are best to start with?
Things done often, by several people, where the input is text or documents and where an error can be caught before it costs anything. Invoices, incoming tickets, quote drafts and summaries are common first steps. We help you pick one task, not ten, and measure how long it takes today.
How long does it take?
The prototype against your data takes one to two weeks. A finished solution built into your system with a review flow normally takes a few weeks to a couple of months depending on the integrations. You see results every week and can stop after the prototype if the numbers do not hold.
Do we need a new system for this?
Usually not. AI automation works best built into the systems you already use, connected through their APIs. If a system lacks an API we build the route first. If the problem is that none of your systems does what you need, that is a custom software project, and we say so.
Can you help us with AI strategy before we build anything?
Yes. A common first engagement is to go through the business, list the tasks that take the most time and assess which suit AI, which are solved by ordinary integration and which should be left alone. You get a prioritised list with estimated savings, not a presentation about AI in general.
Tell us which task takes the most time
Describe a process that is done by hand today and roughly how often, and we will get back within one working day on whether AI can take it and how.