AI implementation for business
AI deployed where it actually saves time
Most corporate AI projects end at the demo. We start by asking which repetitive work you want gone — and whether its disappearance can be measured.
Who it's for
- Teams drowning in documents to read, classify and retype
- Support teams answering the same question for the thousandth time
- Companies with a lot of internal knowledge nobody can find
What you get
Document classification and extraction
Invoices, contracts, orders and correspondence read automatically, with fields extracted into your system and a queue of uncertain cases for a human to decide.
Search over company knowledge
A question asked in plain language, an answer with a link to the source document.
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No making things up — if the answer isn't in the knowledge base, the system says so.
Customer support assistants
Answers to repetitive questions, handed off to a human the moment confidence drops.
Automation with a model in the loop
The model as one step in a process, not the whole process — with the output validated before it's written to the system.
Narrower scopes within this service
If you already know exactly what you need, the pages below describe it directly.
- AI chatbots and support assistantsAn assistant that answers from your own knowledge, not a general model. With stated limits, a hand-off to a person, and a measurable effect.
- Semantic search over internal knowledgeSearch that understands the question rather than matching keywords. Documentation, procedures and project history you can finally find things in.
- Document classification and data extractionInvoices, orders and contracts read automatically instead of retyped. With a confidence threshold and a review path for anything unclear.
How we work
- 01
Choosing the use case
We review the candidates and pick the one with the best ratio of time saved to risk. We define the success metric before starting.
- 02
Prototype on your data
A working prototype on real documents, with measured accuracy. This is the decision point on whether to continue.
- 03
Production rollout
Integration with your systems, handling of uncertain cases, permissions and a model decision log.
- 04
Measurement and tuning
Accuracy measured continuously, because data changes — a model that worked in March may not work in November.
Pricing models
Fixed-price prototype
Quoted separately and deliberately small.
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If accuracy comes in below the threshold, we stop — and that's a good outcome too, just a cheaper one than finding out a year in.
Fixed-price rollout
Once the prototype exists the scope is known, so we can give an amount and a deadline.
Maintenance retainer
Accuracy monitoring, tuning and model updates.
See the case study
DSS by Caredo — a product we build together with the Caredo team
Frequently asked questions
Where do we start with AI in our company?
With one process that's repetitive, has a measurable time cost, and tolerates the occasional mistake. Classifying incoming documents usually meets all three. Starting with "an assistant that knows everything" is the most common way to get a project with no end.
Will my data go to a public model?
Not unless you want it to. We match the solution to your requirements — from hosted models under a no-training-on-data agreement, to models run on your own infrastructure.
What if the model gets it wrong?
We design assuming it will. Every rollout has a confidence threshold — above it, the automation runs; below it, the case goes to a human. Every model decision is logged and traceable.
What does it cost?
A prototype is usually PLN 15–30k net. A production rollout is PLN 60–200k, depending on the number of integrations. On top of that, the cost of running the models — typically hundreds to a few thousand zloty a month.
Got an idea? Let's talk.
The first call is free. We reply within one business day.