AI implementations
A chatbot that responds with your knowledge (rag) rather than making it up. Classification of emails and leads. Reading invoices and documents. We build on your data, host where you want, show you a working prototype before we start full implementation.
If you recognize at least two, it makes sense to talk about implementing AI.
The salesperson doesn't know if this product fits this machine, so he calls the technologist. Onboarding a new person takes months because the knowledge is not in one place. The RAG assistant responds with your documents in seconds, with a link to the source, not with fabrications.
The contact@ mailbox is a bag: inquiry, complaint, invoice, spam. Someone reads it and sends it around. AI classification identifies and directs the report to the right person or to a machine immediately, without waiting for the morning inbox check.
Invoices, orders, specifications, contracts. Someone opens the PDF and copies the data into the system. AI reads the document, extracts fields (Tax Identification Number, amounts, items), and gives it to a human for validation only when it is not certain. Less rewriting, fewer typos.
A ready-made chatbot does not know your price list, procedures or customer history, so it gives untruths in a confident tone. RAG connects the model to your knowledge base: it responds only from what you have, and when it doesn't know, it says it doesn't know.
No declarations or slides. Everything below is working at __ JSONCREW_1__ right now. We can show this on the screen when we arrive at your first stage.
Computer programming
All new code is created together with AI. The program proposes a solution, the person checks and accepts it. We know where AI really accelerates, and where it only pretends to help.
Sale
Contact box read automatically. Each query marked (industry, customer temperature) goes to the right person, without an injured browsing.
Content
AI turns a short note into a finished article, a person checks the publication directly on the page with one click. Instead of a working day, two hours.
Social media
Automatically generated carousels, infographics and comics. Consistent faces of the characters in each slide, Polish text exactly where it should be, without mistakes.
Google Analytics
Connected to Google Search Console. Daily reports of positions and clicks, we know which pages require cutting without clicking on the panel.
Maintenance
A dozen or so customer pages under one panel. Updates, monitoring and backups run from one place instead of clicking on each page separately.
Advertising
Connected advertising account on Facebook and Instagram. Daily report: which ads eat up the budget and which are worth increasing. AI hints, the decision is made by a human.
Safety
Automatic monitoring on customer pages. After a recent attack, three types of viruses were recognized and removed in one day, without losing any page.
Six hard changes for the company. Click to show an example with numbers.
Klient dostaje wycenę tego samego dnia i zamawia u Ciebie zamiast szukać dalej.
Salesman gets an inquiry, asystent czyta specyfikację klienta i cennik, generuje wycenę do potwierdzenia przez człowieka. Zamiast 3 dni oczekiwania na wolne okienko technologa, 60 minut i klient ma ofertę.
Nowy handlowiec pyta asystenta zamiast trzymać na etacie 3 seniorów w rezerwie.
Cennik, procedury, wyjątki, historia klientów, wszystko w bazie, nie w głowach. Nowy pracownik pierwsze tygodnie nie wisi nad ramieniem doświadczonego kolegi, tylko pyta asystenta i dowozi.
Skalujesz sprzedaż i obsługę bez zatrudniania kolejnych osób do BOK.
Skrzynka kontakt@ czytana automatycznie, każde zapytanie oznaczone i skierowane do właściwej osoby. Powtarzalne pytania dostają automatyczną odpowiedź od razu, tylko trudne trafiają do człowieka. Ta sama ekipa obsłuży 3× więcej zapytań zanim zatrudnicie kogoś nowego.
Klient pyta w niedzielę o 23:00, dostaje merytoryczną odpowiedź w 5 minut zamiast „skontaktujemy się w poniedziałek".
Asystent odpowiada z Twojej bazy wiedzy o każdej porze. Klient który zadał pytanie wieczorem albo w weekend nie musi czekać do rana, dostaje wycenę, dostępność albo instrukcję od razu. Rano handlowiec ma gotowe podsumowanie i lead cieplejszy niż w poniedziałek.
Odejście seniora nie unieruchamia firmy, wiedza siedzi w bazie, nie w jednej głowie.
Cennik, procedury, wyjątki, historia dziwnych zamówień, wszystko udokumentowane w miejscu z którego asystent czerpie. Gdy senior idzie na emeryturę albo urlop, jego wiedza zostaje. Nowy handlowiec pyta tak samo jak przed 15 laty pytał Pan Krzysiek, dostaje tę samą odpowiedź, z tego samego dokumentu.
The salesperson calls first the one who is almost buying, not the alphabetical list.
The assistant reads all customer interactions (emails, history in CRM, website traffic), assesses the maturity and temperature of the lead, sets the queue for the salesperson. The priority is given to the person who was on the listing page 3 times this week, not the person who left the inquiry 6 months ago.
First stage of implementation
No slides and no generalities. We come to you, talk to the team, watch how you work, and a week later you get a map of processes and a recommendation where AI makes sense for you, and where it's a waste of money.
What this day looks like
What you get the week after
The report and map will stay with you. Even if after the first stage we decide together that this is not the time for further implementation, you come out with a specificity that you did not have before.
Who's coming
A developer with manufacturing experience and process supervisor who understands sales and operations. No salesperson and no PowerPoint consultant.
If we move forward with the implementation, the amount of the first stage is deducted from the project budget.
Start now!A short online conversation. You will tell us where you are with AI, we will tell you if the first stage makes sense for you.
AI Implementation makes sense when solving a specific business problem, not when the management wants to „have AI". That's why we start with the question that AI is supposed to answer: what will accelerate, what will relieve the burden, where you will actually earn or save. Only then do we choose the technology.
The approach is challenging: we will tell you where AI pays off and where it is an expensive toy. Not every process needs a language model, sometimes ordinary automation It achieves the same result at a lower cost.
RAG (Retrieval-Augmented Generation) connects the language model to your knowledge base. Instead of responding with general knowledge from the Internet, the model first searches your documents and then responds only based on them, with a link to the source. Thanks to this, he knows your price list and procedures, and when there is no basis in data, he says he doesn't know, instead of making things up.
Hallucinations cannot be eliminated, but they can be limited and detected. We use RAG (source-based responses), force document citations, measure accuracy on your real questions and set a threshold below which the model says it doesn't know instead of guessing. You assess the quality on the prototype before you pay for the full construction.
Depends on requirements. When the data is sensitive (GDPR, personal data, finances), we place the model on your server and the data does not go outside. When there are no such restrictions, we use cloud models, which are cheaper and faster. We make the decision in the first stage, the data entrustment agreement is signed before the start.
We match the task, budget and GDPR requirements, not the other way around. Cloud models (e.g. Claude, GPT) when quality counts and there are no data limits. Open models, placed on your server when the data must stay with you or the volume is large. The architecture is built in such a way that it is possible to change the model when prices or the supplier's policy change.
We start with a one-day first stage (PLN 3,000 net, payable in advance). After that, we know the scope and give a fixed price for implementation. A simple FAQ assistant on ready-made documents is priced separately, cheaper than full implementation with integration into the system. In addition, there is the cost of queries to the model, which we show in advance and monitor after implementation.
This is a normal situation, not an obstacle. The first step is to review the data: what you have, in what condition, what needs to be sorted before indexing. Sometimes a sample of key documents is enough to start, and the rest is added later. If there is not enough data to make sense of AI, we will say it directly instead of taking the project by force.
The biggest mistake is connecting a model that responds "off the top of your head." In business, what matters is that the AI responds with Yours data, documents, price lists, procedures. This is what rag does: the model first finds the right piece of your knowledge, only then responds. Less fiction, more verifiable concrete.
AI without rules is a risk: data leaks, wrong decisions, lack of control. That’s why, for more significant implementations, we establish governance (what the model sees, what it can do, how we log it) BEFORE launch, not after an incident.
We start with the pilot on a narrow, measurable case to see the real effect before you enter the scale with the budget. If the pilot delivers, we expand. If not, you know it after a few weeks, not after a year and a big bill.
Are you wondering where AI will really help in your company? Arrange a free conversation, we will indicate the cases that will come back and those that are better to let go.