AI where it does real work, not demos.

We use AI for the tasks that used to need a person reading, sorting and deciding: documents, emails, requests and unstructured data — turned into structured actions inside your workflows, with people reviewing only the exceptions.

Book a discovery call See typical scenarios

Where we apply AI

Not everywhere. Only on tasks where rules cannot be written down but a person “just knows” — and the volume makes it worth it.

Document processing & data extraction Invoices, contracts, delivery notes, forms — read and turned into structured fields.
Email & request classification Incoming messages sorted by type, urgency and owner, and routed to the right queue.
Knowledge search & answers Your procedures, manuals and past cases searchable in plain language — with sources shown.
Summaries & drafts Long threads and reports summarised; replies drafted for a person to approve and send.
Data cleaning & matching Duplicates merged, product names matched across catalogues, messy inputs normalised.
Quality checks & anomaly flags Orders, invoices or entries that look wrong get flagged for a person before they cause damage.

Three AI workflows we build most often

Each one has a human in the loop by design. AI does the reading; people make the calls that matter.

01

Document intake that reads itself

  1. Invoices, delivery notes or contracts arrive by email or upload
  2. AI identifies the document type and extracts the key fields
  3. Values are validated against your systems — supplier, PO, totals
  4. Clean documents are booked; uncertain ones go to a review queue
Before

Someone opens every PDF and types the numbers into accounting. Errors surface at month end.

After

Most documents are booked untouched. People spend minutes on exceptions, not hours on typing.

02

Inbox triage and drafted replies

  1. Requests arrive in a shared inbox or web form
  2. AI classifies each one: order, complaint, quote request, invoice question…
  3. It is routed to the right person with a suggested reply drafted from your knowledge base
  4. The person edits, approves and sends — urgent cases are flagged first
Before

One inbox, everyone reads everything, urgent messages wait behind newsletters.

After

Every request has an owner within minutes and a reply that is 80% written.

03

Internal knowledge assistant

  1. Procedures, manuals, contracts and past tickets are indexed
  2. Staff ask questions in plain language — in chat, Slack or your portal
  3. The assistant answers from your documents and shows where the answer came from
  4. Gaps in the documentation become visible and get filled
Before

“Ask Marko” is the knowledge base. When Marko is on holiday, work waits.

After

Answers in seconds, with sources — and new colleagues stop interrupting senior ones.

Is AI the right move for you?

Often the honest answer is “plain automation first”. Here is how we tell the difference.

A good fit when…

  • High volume of documents, emails or requests handled by people
  • The rules cannot be written down — a person “just knows” by reading
  • The input is unstructured: PDFs, scans, free text, photos
  • A review step for uncertain cases is acceptable
  • You have historical examples we can measure accuracy on

Probably not yet when…

  • The rules are fully deterministic — plain automation is cheaper and more reliable
  • The volume is a few items a week
  • Zero errors are required and no human review step is possible
  • There is no data yet to test against — we would be guessing, and we do not ship guesses

How an AI workflow project runs

Accuracy is measured on your real documents before you commit to a build — not promised in a slide.

  1. Week 0

    Discovery call

    What arrives, in what form, how often, who handles it today and what a wrong answer would cost.

  2. Weeks 1–2

    Feasibility on your samples

    We run a set of your real documents or emails through candidate models and report measured accuracy — then propose a fixed scope, or tell you it is not worth it yet.

  3. Weeks 3–6

    Build with a human in the loop

    The workflow, the validation against your systems and the review queue for low-confidence cases — tested on live volume.

  4. Go-live

    Monitor accuracy, shrink the exceptions

    Every decision is logged. Reviewed exceptions feed back into the rules and prompts so the share handled automatically keeps growing.

The right model for the task — not one model for everything

We choose per task and keep it swappable. Your data is processed under no-training terms or on private deployments, and lives in your systems, not the model’s.

  • OpenAI
  • Anthropic Claude
  • Google Gemini
  • Azure OpenAI
  • Open-source models
  • OCR
  • Vector search
  • Structured outputs
  • n8n
  • Make
  • Custom pipelines

Questions we hear on every first call

Is our data used to train public models?

No. We use providers under business terms that exclude training on your data, or private deployments where the model runs in an environment you control. Nothing is stored by the model; results are written back into your own systems.

How accurate is it?

We do not answer that in general — we measure it on your documents in the feasibility phase and give you the number before you commit. In production, low-confidence cases go to a person, so the accuracy of what goes through untouched stays high.

What about hallucinations?

Three guards: outputs are structured (fields, not free text), every extracted value is validated against your own data where possible, and anything below a confidence threshold is reviewed by a person. The AI reads; it does not get the final word on anything important.

Which models do you use?

Whichever fits the task best on your samples — commercial or open-source. The workflow is built so the model can be swapped when a better or cheaper one appears, without rebuilding anything else.

What does it cost to run?

Model usage is typically priced per document or per message and usually comes to cents, not euros. We estimate the monthly running cost in the proposal based on your actual volume.

Do we need a data team or AI expertise in-house?

No. The workflow runs like any other system: it has logs, a review queue and someone responsible for exceptions. We build it, monitor it and hand you a tool your team already knows how to use.

Which pile of documents would you hand to AI first?

Bring a few real examples to the call. We will tell you what AI can reliably do with them — and what it cannot, yet.

Free · 45 min

What you get in the discovery call

No slides, no pitch. We talk about how your work actually flows.

  • A written map of where time is being lost in your workflow
  • 2–3 quick wins you can act on immediately
  • A clear recommendation: integrate, automate or build

Book your discovery call

  • Free discovery call, no commitment
  • Measured accuracy on your samples before you decide
  • Fixed-scope proposal before any work starts
Book a discovery call