Practical AI implementation for your business

From the first conversation to a solution that works. AI assistants, automation, integrations and data analysis.

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What we do

AI that works inside your business, not next to it.

We help businesses use artificial intelligence where it brings a real benefit: in customer service, work with documents, data analysis and the team's everyday tasks. We don't start with a ready-made product but with your problem, and we take the solution to the point where it simply works.

What does implementing AI mean in practice?

  1. The right task

    We look for work that takes a lot of time, keeps repeating or means searching through many sources. That's where AI makes the most sense.

  2. A solution that fits

    Sometimes a well-configured tool is enough; sometimes an application built from scratch is needed. We choose what solves the problem, not what happens to be in fashion.

  3. Connected to your business

    The solution uses your data and works where your team already works: in email, the sales system or company documents.

  4. Everyday use

    An implementation is finished when the team uses the solution every day and knows when it can trust it and when it needs checking.

How we help

Not just chatbots.

Artificial intelligence is much more than a chat window on a website. Here are the areas where we can help. In practice, one implementation often combines several of them.

Not sure which area applies to your business? Describe the problem and we will help you choose a solution.

  • Process automation

    Repetitive tasks that someone does by hand today: retyping data between systems, filling in spreadsheets, passing matters on, sending reminders. We connect the tools you already use so that data flows by itself and a person approves only what needs a decision.

    • moving data between systems
    • order and document workflows
    • notifications and reminders

  • AI agents and customer service

    An assistant that knows your offer and your rules: it answers recurring questions, keeps the inbox in order and prepares draft replies. An agent can also carry out an agreed task on its own, such as checking an order's status. It always works within clearly defined limits and passes difficult cases to a person.

    • replies to emails
    • website chat
    • sorting incoming requests

  • Custom applications

    When off-the-shelf software doesn't fit the way the business works, we build tools of its own: a dashboard for the team, a quote calculator or configurator, a simple app for customers. We connect them with the systems you already use and add AI only where it is genuinely useful.

    • team dashboards
    • quote configurators
    • apps for customers

  • Company data and documents

    A company's knowledge lives in contracts, procedures, price lists and inboxes. An assistant answers questions on the basis of these documents and shows where the information came from, and the system reads data from invoices, orders or contracts and enters it where it is needed.

    • company knowledge assistant
    • reading invoices and contracts
    • searching documents

  • Analytics and reporting

    We bring data from different places together into one clear picture: reports that update themselves, summaries in plain language and signals that something is out of the ordinary. The decision stays with you, and AI helps you prepare it better.

    • automatic reports
    • questions to your data in plain language
    • spotting unusual changes

  • Content and visuals made with AI

    Product descriptions, image variants, visualisations or campaign materials, wherever AI genuinely shortens the work or makes it possible to prepare more variants. A person approves the final version.

    • product descriptions
    • images and visualisations
    • marketing materials

Example uses

What it can look like in practice

The scenarios below illustrate typical situations in businesses; they are not descriptions of completed projects. Each one shows what can happen automatically and what stays in people's hands.

Example 01

Questions from customers

Situation
Many similar emails arrive every day: about delivery dates, product availability, the returns policy. The same people keep answering them.
Solution
An assistant reads the messages, checks the data in the order system and prepares a draft reply. A member of staff reviews and sends it, and unusual cases go straight to the right person.
What could change
Replies go out faster, and the team has more time for matters that need a conversation.
Automatically
Recognising the topic, checking the order status, drafting a reply and passing unusual cases to the right person.
A person decides
Reviewing and sending the reply, as well as complaints and disputes.
  • customer service
  • AI assistant
  • order system integration

Example 02

Knowledge scattered across documents

Situation
Procedures, price lists and arrangements with customers sit in folders, emails and notes. New people ask about the same things, and experienced staff lose time searching.
Solution
An internal assistant answers questions on the basis of company documents and shows where the information came from. Everyone sees only what they have access to.
What could change
Answering a typical question takes a moment, and bringing new people on board is simpler.
Automatically
Finding the relevant passages, answering with the source and respecting access rights.
A person decides
Keeping documents up to date, and checking the answer against the source when prices or contract terms are involved.
  • knowledge assistant
  • company documents
  • access rights

Example 03

Documents retyped by hand

Situation
Invoices and orders arrive as PDF files, scans or photos. Someone retypes the data into the system, and mistakes come to light only later.
Solution
The system reads the document, extracts the data it needs, compares it with the order and enters it into the software. When something doesn't match, it flags the document for a person to check.
What could change
Less retyping by hand, and discrepancies are caught sooner.
Automatically
Reading the data, comparing it with the order and entering matching documents into the system.
A person decides
Resolving discrepancies and approving payments.
  • document automation
  • accounting system integration

Example 04

Data that is hard to draw conclusions from

Situation
Sales data sits in the system, in spreadsheets and in the online shop. A report is put together by hand once a month, so decisions are made on out-of-date numbers.
Solution
Data from different sources flows into one automatically updated report. AI describes the changes in plain language and points out unusual signals, such as a drop in orders in one region.
What could change
An up-to-date picture without building reports by hand, and a faster response to change.
Automatically
Collecting data, calculating figures, describing changes and flagging an unusual drop.
A person decides
Looking for the causes and deciding what to do about them.
  • data analysis
  • reporting
  • integrations

Example 05

Quotes built in a spreadsheet

Situation
Sales staff prepare quotes in a complex spreadsheet that only one person really knows. Every quote means copying prices, discounts and descriptions, and mistakes surface only at the customer's end.
Solution
A simple application guides the salesperson step by step: products, variants and discounts according to agreed rules. At the end it produces a finished quote document, and AI drafts the message to the customer.
What could change
Quotes are ready sooner and follow the same rules, whoever prepares them.
Automatically
Calculating the price by the rules, the quote document and a draft message to the customer.
A person decides
Discounts outside the rules and approving the final quote.
  • custom application
  • price list integration
  • AI-assisted writing

Example 06

Hundreds of products without descriptions

Situation
An online shop sells hundreds of products, and many of them have no description or one copied from the manufacturer. Writing everything from scratch would take many weeks.
Solution
From the product data, such as specifications, material and intended use, AI drafts descriptions in the shop's agreed style, in several lengths.
What could change
The team edits and approves texts instead of writing them from scratch.
Automatically
Draft descriptions in several versions and a list of facts to confirm.
A person decides
Checking specifications, polishing the style and publishing.
  • AI-assisted content
  • online shop

AI Lab

See how it works before you implement anything.

Four short demonstrations on sample data from Jasnota, a fictional shop. Choose a task, start with a ready-made example and see what an AI model does with it.

Input

What the model knows about the shop

    What the AI did

    1. 01 · Understanding the case

    2. 02 · Key information

    3. 03 · To check

    4. 04 · Draft reply

    What it gives you: a member of staff gets an organised case and a draft to check instead of a blank reply. A person decides whether to send it.

    How we work

    Problem first. Technology second.

    Every implementation goes through five stages: from getting to know the process, through choosing the application, building and testing, to handing the solution over to the team.

    AI isn't always the best answer. If a simpler automation or a change to the process would solve the problem better, we'll say so plainly.

    1. 01

      We get to know the process

      We talk to the people who do the work every day and look at how it really runs: how long it takes, where mistakes happen, which tools and data it uses. We write down how things are today, so there is something to compare with later.

      Outcome a description of the process and of where time is lost.

    2. 02

      We choose the application

      We point out what is worth automating, where AI will really help and what is better left to people. We agree on the scope, the order of work and how we will know the solution works.

      Outcome a specific proposal: what we build and how we will check the result.

    3. 03

      We build and integrate

      We create or configure the solution and connect it with the tools the business uses: email, systems, spreadsheets and documents. Access to data is limited to what is necessary.

      Outcome a working solution in your environment.

    4. 04

      We test and train

      We check the solution on real cases and compare it with how things were before. We agree what happens automatically and what a person checks, and show the team how to use it.

      Outcome a solution proven in practice and a team that knows how to use it.

    5. 05

      We hand over and improve

      We hand the solution over to the team with instructions: how to use it, whom to ask and what to do when something doesn't work. After launch we check whether it really saves time and fix whatever needs changing in practice.

      Outcome a solution the team uses every day.

    About us

    Time is money. So we check whether we save it.

    We don't implement technology for its own sake. We start with the problem in the business, build solutions around how the team really works, and check whether they actually save time or remove everyday friction.

    1. Problem before technology

      The first conversation is about what takes up time and where work gets stuck, not about AI models. We choose the tools last, and sometimes a simpler automation turns out to be enough.

    2. Real work, not a demo

      The solution has to work in the email, systems and documents the team uses every day. That's why we build it around the existing way of working and test it with the people who will use it.

    3. A result you can check

      Before we start, we agree on what should improve: the time it takes to handle a case, the number of manual steps or mistakes. After implementation we compare it with how things were. If there is no visible difference, we say so openly.

    4. People where it matters

      We clearly agree what the system does on its own and what a person checks. AI prepares and suggests, but decisions that matter for customers and money stay with people.

    Who is behind it

    You talk directly to the person who analyses your process and carries out the implementation, with no intermediaries and no passing your case along.

    Oliwier Bernatowicz

    Oliwier Bernatowicz

    Implementation Specialist and Business Analyst

    I have been working with programming and technology for 7 years. I implemented my first automations in my own e-commerce business, from sales and customer service to analysing results, shipping and on-time delivery. That is why I know these challenges not only from the technical side, but also from the perspective of an entrepreneur who has to run a business every day.

    Today I help other companies find the processes worth improving and choose solutions for them, from simple automations to AI tools. I care about implementations that solve a specific problem, are secure and make people's work easier, instead of adding yet another complicated system.

    Send a message

    The same AI tools are available all over the world. Implementation makes the difference.

    Access to AI alone doesn't change anything yet. We help fit it to your processes, data and team.

    Contact

    Tell us what you want to improve.

    You don't need to know what technology you need. Describe the process or the problem in a few sentences, and we will reply, ask about the details and suggest where to start.

    What to write about

    • which task or process you want to improve,
    • who handles it today and roughly how long it takes,
    • which tools and data you use.

    Prefer email?

    Write directly to kontakt@czastopieniadz.com.

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