Implementing AI for businesses Poznań.
Implementing AI for Businesses and Artificial Intelligencethat actually pays for itself in 12 months. We build AI chatbots with a knowledge base (RAG), AI agents that perform tasks across multiple systems, and business process automation—with measurable ROI calculated before the project even starts.
working MVP
it really pays off
from AI investments
on your property
Problem
It sounds familiar?
Before we calculate ROI and choose the right technology, we start with one question: where exactly are you wasting time and money? If you recognize even two situations here, implementing AI will likely pay off sooner than you think.
The team is drowning in the same questions
60–80% of customer service inquiries involve repetitive topics: pricing, order status, availability, and hours of operation. People respond with the same answers over and over again, rather than addressing the difficult issues.
All knowledge is in people's heads
When an expert goes on vacation or leaves the company, their knowledge disappears with them. New employees spend weeks getting up to speed because no one has written down "how we do it."
The customer waits for hours for a response
The inquiry comes in after hours or on the weekend and sits there until the morning. Meanwhile, the client asks the competition—and the first one to answer usually wins.
Handmade offers and documents
Every offer, contract, or report involves hours of copying data between files. It's easy to make a typo, get an outdated price, or send the wrong version to the client.
Data scattered across five systems
CRM, ERP, spreadsheets, email, disk. To answer a single customer question, you have to look in several places—and hope that everywhere is up to date.
Have you tried ChatGPT and it hallucinated?
Naked ChatGPT doesn't know your prices, procedures, or customer history. It makes things up in a confident tone and doesn't cite sources. That's why we build RAG systems that respond solely based on your data.
Do you recognize at least two of them? Get started with a free audit — we will calculate what can be recovered and in what time.
For whom?
Implementing AI in your company makes sense forthree types of organizations
Not every company needs AI implementation right away. In ~30% of cases, we recommend postponing and first streamlining processes. The remaining 70% are companies where AI agents and AI process automation will actually pay for themselves faster than hiring another person.
Companies with overloaded customer service desks
The customer service team receives 60-80% of the same questions: pricing, order status, opening hours, FAQs. AI Chatbot with Knowledge Base takes over repetitive inquiries, consultants deal with difficult cases.
Companies with a large knowledge base
Hundreds of documents, procedures, regulations, legal opinions, technical offers – knowledge that cannot be searched with Ctrl+F. RAG System allows you to ask questions to the company database and receive answers with source citations.
Companies with repeatable processes
Email classification, offer generation, invoicing, document OCR, reporting. AI Agents and Automation perform these tasks across multiple systems (CRM, ERP, email) without involving the team.
What does the implementation include?
Implementation of artificial intelligence from the audit to production
We don't sell ChatGPT embedded in a website. Each AI implementation for businesses is a specific module solving a specific problem—with a calculated ROI, integrations with your systems, and documentation left in your hands.
Industries
AI implementations tailored toYours industry
The challenges faced by e-commerce, law firms, and B2B manufacturers differ. Below are the areas where we most frequently implement AI—along with industry-specific application examples.
E-commerce
The bot advises on product selection, checks order status, and handles returns and complaints 24/7. It relieves the Customer Service Desk during peak sales periods, when inquiries are most numerous.
Law firms
The RAG system searches contracts, case law, and internal opinions, responding with source citations. It accelerates research and document preparation—while maintaining data confidentiality.
Medicine & Health
Sensitive data doesn't leave your infrastructure—we implement self-hosted models (Llama, Mistral) compliant with GDPR. Registration management, patient FAQs, and query triage.
Production & B2B
Automated request for quotation (RFQ) processing, OCR of technical documents, and an agent generating preliminary quotes. Reduces response time from days to minutes.
Finance & Accounting
OCR and invoice classification, automatic document entry into the system, and detection of unusual items. Less manual retyping, fewer month-end errors.
Education & Training
An assistant responds to students based on the materials, generates quizzes and summaries, and handles organizational questions. A tutor is available to every participant at any time.
AI implementation process
From audit to production in 5 steps
AI implementation in your company takes 4–12 weeks, depending on scale. We begin with an AI readiness audit and ROI calculation, culminating in a monitored production rollout with hypercare. You confirm each stage before the next one begins.
Audit & ROI
90-minute discovery, process map, 2–3 use cases with calculated return on investment. Free.
Architecture
Model selection (OpenAI, Claude, Llama), integration scheme, compliance plan, sprint schedule.
MVP in 14 days
First working version in two weeks. Real data, real questions, real quality assessment.
Pilot
2–4 weeks of testing with a selected team or customer segment. Prompt and knowledge base iterations.
Production & Hypercare
Full roll-out, monitoring, 4 weeks of intensive support, team training, documentation.
ROI calculator
Count how much AI can save in your company
Move the sliders to suit your situation and see your approximate return on investment. This is a simplified simulation—we'll calculate the real numbers during a free audit based on your processes.
Your situation
// This simulation is for illustrative purposes only. Assumes a one-time implementation cost of PLN 5,000 and excludes API and hosting costs (approx. PLN 200–600/month). We prepare a detailed ROI model individually as part of an audit.
AI vs. Current State
What is really changing after implementation
It's not about replacing people, but rather about removing the repetitive, tedious workload. This is the most common difference between how a company operates before and after implementing AI in its operations and processes.
You don't know what for to start?
That's why we offer a free audit. In 90 minutes, we'll show you where AI will deliver the fastest return for your business—and we'll be honest if implementing it doesn't make sense right now.
AI implementation packages
Three levels - fromchatbot to the full AI system
Implementing AI for businesses isn't a single product, but a spectrum: from a simple AI chatbot costing a few thousand to a dedicated system with AI agents costing six figures. The following are approximate ranges—each project is individually priced after an audit.
Chatbot AI & FAQ
- RAG chatbot based on your documents
- up to 100 pages of knowledge base (FAQ, price list, regulations)
- website widget + optional Messenger
- OpenAI or Claude API
- transferring the call to a consultant
- GDPR compliance + cookies
- delivery time: 2–3 weeks
RAG + integrations
- RAG system over the full company database
- CRM integrations (HubSpot, Pipedrive, Salesforce)
- integration with ERP (Subiekt, Optima, Comarch)
- multi-channel: web, e-mail, Slack, Teams
- dashboard with response quality metrics
- GDPR audit + self-hosting option
- 4 weeks of hypercare after launch
- delivery time 4–6 weeks
AI & custom agents
- multi-step AI agents in business processes
- self-hosted models (Llama, Mistral) for sensitive data
- fine-tuning models for your industry
- computer vision / OCR / document classification
- dedicated infrastructure (AWS, Hetzner, on-premise)
- AI Act + DPIA compliance
- 3 months of development and optimization included
- 8–14 weeks delivery
Example implementations
What does it look like in practice
Three representative scenarios show how we match a solution to a problem and the level of impact we can achieve. These are anonymized examples illustrating typical workloads—not specific client data.
RAG Chatbot Relieves Customer Service Workload
ChallengeThe Customer Service team was drowning in repetitive questions about order status, availability, and returns—especially during the sales season.
SolutionThe RAG chatbot, based on terms and conditions, FAQs, and statuses, with store integration, hands over difficult issues to a consultant with the context of the conversation.
RAG system over the database of contracts and opinions
ChallengeKnowledge was scattered across hundreds of documents and lawyers' minds. Research for the letter took hours of searching through files.
SolutionSelf-hosted model and semantic search engine over an internal database. Source-cited responses; data never leaves the infrastructure.
in minutesresearch time for the letter
AI agent for initial RFQ handling
ChallengeRequests for proposals were arriving via email in various formats. Manual reading and preliminary pricing delayed response by days.
SolutionThe agent reads the query, extracts parameters (OCR), classifies and prepares a preliminary quote for the salesperson's approval.
in minutesresponse time to an inquiry
// Illustrative and anonymized scenarios – show typical scope and order of effects, not actual data for specific customers.
Technologie
On what we are building
We match tools to the problem and compliance requirements—from commercial APIs where speed matters, to self-hosted models where data cannot leave the company. Without being tied to a single vendor.
- OpenAI GPT
- Anthropic Claude
- Google Gemini
- Llama
- Mistral
- self-hosted
- Pinecone
- Qdrant
- Weaviate
- pgvector
- LangChain
- LlamaIndex
- n8n
- Make
- Zapier
- Python
- FastAPI
- webhooks
- REST / API
- AWS
- Hetzner
- Docker
- on-premise
- SSO
Why us
We implement AI that stays Your
We don't sell hype or subscriptions to someone else's black box. We build solutions that solve a specific problem, deliver measurable returns, and stay in your hands—with code, data, and documentation.
Audit, not forced sales
We start by calculating ROI. In ~30% of cases, we recommend do not implement — because the processes must first be sorted out or the scale is too small to pay off.
The code and data stay with you
No vendor lock-in. You get the source code, access, and documentation. You can develop the solution alone or with any team—you're not tied to us.
RAG instead of hallucinations
We don't plug in bare-bones ChatGPT. We build systems that respond solely based on your documents, citing sources, and an honest "I don't know" when there's no answer.
GDPR and AI Act from day one
For sensitive data, self-hosted models are available on your infrastructure. Every implementation begins with a compliance audit: DPIA, risk classification, and right of human intervention.
First MVP in 14 days
You don't wait six months for results. In two weeks, you have a working version based on real data—and you decide on further steps based on facts, not promises.
Polish team and local systems
You speak Polish, regardless of time zone differences. We understand the realities of the market and integrate with what companies actually use—Subiekt, Comarch Optima, Asari.
Opinions
What do they say? customers
Brief comments from people we worked with on AI implementations – about what actually changed in their companies after the launch.
The chatbot has taken over most order status inquiries. The team finally has time for more difficult matters instead of answering the same questions over and over again.
What I most appreciate is the honesty at the outset—we were given a concrete profit statement, not promises. The implementation paid for itself faster than we expected.
Processing quote requests has been reduced from days to minutes. Salespeople receive a ready-made quote and simply approve it.
// Sample reviews (placeholder) - replace with real customer quotes before publishing.
Integrations
We connect AI with what you are already using
CRM, ERP, store, inbox, messaging, spreadsheets. We plug into your existing stack—and if something isn't listed, it can usually be connected via API anyway.
Our rules
What can you do? to count
Four things that apply to every implementation – regardless of the scale of the project or the package chosen.
Free audit
90-minute session with calculated ROI. No strings attached, no hidden costs.
MVP in 14 days
First working version in two weeks. You evaluate the effect on real data.
GDPR Compliance
Self-hosted models for sensitive data, DPA with vendors, AI Act compliance.
Code for your own
You get the source code, access, and documentation. Zero vendor lock-in.
FAQ
Frequently asked questions aboutAI implementation
Questions most frequently asked by CEOs and CTOs during initial interviews about implementing artificial intelligence in their company. Is your question not listed here? We'll respond within 24 hours.
Depends on the scale. Simple AI chatbot with knowledge base it's the cost from 5,000 to 12,000 PLN net one-time + PLN 200–600/month for API and hosting. Medium implementation with RAG and CRM/ERP integrations - from 18,000 to 60,000 PLN. Enterprise with AI agents, fine-tuning and self-hosting — from 50,000 PLN and above, most often in the range of PLN 80,000–250,000.
As part of a free audit, we calculate specific ROI: how many hours of work the process currently consumes, how many hours it will consume after implementation, and in how many months the investment will pay off.
We deliver the first working MVP in14 daysWe launch a full AI chatbot with a knowledge base in2–3 weeks. Implementation with deep integrations (CRM, ERP, email, Slack) takes 4–6 weeksEnterprise projects with AI agents and fine-tuning — 8–14 weeks. Production deployment is followed by a 4-week hypercare period.
Chatbot AI answers questions — reads your knowledge base (RAG) and provides answers in natural language. AI agent performs tasks — decides what step to take, uses multiple tools (CRM, calendar, email, database) and is able to carry out a multi-stage process, e.g. qualifying a lead, sending an offer and arranging a meeting.
Most Polish companies need a RAG chatbot to get started in 2026. AI agents are the next step—we introduce them once the chatbot has already generated its first ROI.
For sensitive data (medical, financial, office, HR) we do not use commercial models — We implement self-hosted open source models (Llama, Mistral) on customer infrastructure or in a private EU cloud. For standard projects, we use commercial APIs (OpenAI, Anthropic) with data retention and enterprise DPA disabled.
We start each implementation with a compliance auditGDPR and EU AI Act (DPIA, risk classification, transparency towards the user, right to human intervention).
The RAG (Retrieval-Augmented Generation) architecture keeps hallucinations to a minimum. The bot responds only based on your documents and cites sources. When the answer isn't in the database, he says "I don't know" and passes the matter on to a human—instead of making things up.
Each implementation includes response quality monitoring, a dashboard with metrics, and a mechanism for users to flag incorrect responses.
We are checking this as part of free AI readiness auditWe map processes, identify areas with the highest savings potential, and calculate the ROI of a potential project. In approximately 30% of cases, we recommend do not implement AI — because the processes first need to be organized, the knowledge base does not exist or the scale is too small to make the implementation profitable.
For the remaining 70%, we write a specific proposal with a timeline, milestones, and calculated return on investment. You make your decision fully informed.
NO. We add AI on top Your existing stack, not instead of it. We connect via APIs and integrations to what you're already using—HubSpot, Pipedrive, Salesforce, Subiekt, Comarch Optima, WooCommerce, Shopify, and Asari.
If a system doesn't have a ready-made API, it can usually be connected anyway (webhooks, exports, middleware). We check this during the audit phase.
We design systems to mitigate risk at the source: RAG architecture responds only from your documents, on sensitive topics the bot says "I don't know" and hands the matter over to a human, and high-stakes decisions are always approved by an employee (human-in-the-loop).
We also include quality monitoring, a metrics dashboard, and an error flagging mechanism. We collaboratively define and document the scope of responsibility and boundaries for AI operations—in line with the AI Act's requirements regarding human oversight.
No – and this is a conscious principle. You get the source code, access, and documentation. You can develop the solution yourself or with any team; we don't lock you into a subscription to someone else's black box.
We are also not tied to a single model – if necessary, we switch projects between OpenAI, Anthropic, Google, or the self-hosted model, depending on cost, quality, and compliance requirements.
It's practically our default mode. We start with a single, quantifiable process and deliver an MVP in 14 days. We then test it on real data with a selected team or customer segment for 2–4 weeks.
The decision to expand your rollout is based on pilot results, not promises. If the results aren't convincing, we don't proceed.
Free AI Readiness Audit
Check if AI will return in your company
90-minute session: We map processes, select 2-3 areas with the highest potential, and calculate specific ROI. No strings attached—we recommend 30% of the time. do not implement.