Customer gets an answer in 5 seconds
Regardless of the time of day or week. The chatbot works 24/7 without breaks.
Chatbot AI — intelligent customer service on the website and in Teams
We deploy chatbot AI based on OpenAI or Claude. Natural conversation in Polish, integration with your company knowledge base, escalation to an agent. Works on websites, in Microsoft Teams and Messenger. Go-live in 3–6 weeks.
01
Customers waiting hours/days for an answer
02
Support handling the same questions hundreds of times
03
Evenings and weekends = lost sales opportunities
Customer problem
Customers ask the same questions, support drowns, costs grow
Customer visits the site, has a product question. Sends an email or calls. Evening — nobody answers. Next day support responds — but the customer has already bought from the competition. Half the questions are repetitive: prices, availability, hours, how to order. Support answers them for the hundredth time.
Chatbot AI solves this completely. The customer asks on the site in natural language, AI answers in 5 seconds 24/7. If it's complex — the chatbot escalates to an agent with the full conversation history. Result: 70% of questions handled without humans, support on strategic work.
Why it matters
Customers waiting hours/days for an answer
Support handling the same questions hundreds of times
Evenings and weekends = lost sales opportunities
Expensive support doesn't scale with the business
No coverage in multiple languages
What we deliver
What we deliver in the implementation
A chatbot AI rollout is a project: conversation design, knowledge base integration, agent escalation, full launch with monitoring.
01
Audit of current customer service
Analysis of emails, tickets, chats. Identifying top customer questions. Picking chatbot use cases.
02
Chatbot persona design
Name, tone, communication style. Matched to the brand. First greeting, way of welcoming the customer.
03
Knowledge base for the chatbot
Knowledge source configuration: FAQs, documentation, prices, return policy. The chatbot answers based on these.
04
System integrations
CRM for customer data, order system for statuses, calendar for bookings, payment system for invoice status.
05
Escalation to an agent
When the chatbot isn't enough — handoff to an agent in Microsoft Teams with the conversation transcript. The customer sees they're now with a human.
06
Deployment on website and Teams
Chatbot widget on the site (WordPress, custom), integration with Microsoft Teams (Copilot Studio), optionally Messenger and WhatsApp.
07
Analytics dashboard
Number of conversations, top questions, % handled by AI, post-chat NPS, identifying knowledge gaps.
08
Training and 30 days of support
Sessions for support (escalation handling), admins (knowledge base management). 30 days of support with answer calibration.
Technology stack
Technologies we use
OpenAI or Claude as the chatbot brain, Azure AI Search for the knowledge base, integrations with company systems.
OpenAI GPT-4 / Claude (conversation brain)Azure AI Search (knowledge base)Microsoft Copilot Studio (Teams chatbot)Power Automate (escalation workflow)Monday CRM (customer data integration)Microsoft Teams (escalations to agent)
Your solution
Typical chatbot AI scenarios
Chatbot on the website
Widget in the bottom-right corner. Customer asks about products, prices, availability, policies. AI answers based on the knowledge base.
Internal chatbot in Microsoft Teams
Chatbot for employees — HR Bot, IT Helpdesk Bot. Questions about leave, procedures, technical issues. Fewer tickets to HR/IT.
Chatbot in Messenger / WhatsApp
Customers contact via Messenger or WhatsApp — chatbot answers on these channels. Same brain, same answers.
Sales / lead qualification chatbot
Chatbot qualifies website leads: industry, company size, budget. Qualified ones routed to a salesperson in CRM.
Solution fit
Sprawdźmy, które elementy rozwiązania najszybciej ograniczą pracę manualną i uporządkują procesy w Twojej organizacji.
Impact and metrics
Effects of a chatbot AI rollout
Clients we have implemented chatbots for report similar effects in the first 2 months.
70%
of questions fully handled by AI
5 sec
average first response time
24/7
availability for customers
-60%
fewer support tickets
Business benefits
Support reclaims time
70% of questions handled by AI. Support focuses on harder cases, sales, VIPs.
Full auditability
Every conversation recorded, transcribed, available for analysis. Statistics on top questions and knowledge gaps.
Who this is for
Who this is for
Companies with an active website and traffic
Organizations with 1,000+ unique monthly users where a chatbot is a real contact channel.
Companies with active support
Organizations handling 50+ daily customer questions — where chatbot AI truly offloads the team.
Companies with a knowledge base and documentation
Organizations with documented FAQs, procedures and instructions — the chatbot has something to work on.
Companies planning to scale
Organizations for whom growing site traffic shouldn't grow service costs proportionally.
Implementation process
Chatbot AI implementation process
We implement the solution in a structured model that clarifies project stages, integration with the current environment and further development across the organization.
Stage01
Audit of current customer service (1 week)
Analysis of emails, tickets, chats. Identifying top questions. Picking chatbot use cases.
Stage02
Persona design and knowledge preparation (1 week)
Persona, tone, scenarios. Preparing and structuring the knowledge base (FAQs, documentation, prices).
Stage03
Configuration and integrations (1–2 weeks)
OpenAI/Claude, Azure AI Search, CRM and order-system integrations. Website widget, Teams integration.
Stage04
Pilot with monitoring (1 week)
Pilot with 10% of traffic, answer-quality monitoring, knowledge base tweaks. Agent escalation test.
Stage05
Go-live and 30 days of support
Full launch. Support training. 30 days of quality monitoring and knowledge base completion.
Stage 1 of 5
Audit of current service (emails, chats, tickets)
Estimate of support time savings
Chatbot rollout plan with concrete ROI
FAQ
FAQ about chatbot AI
How long does chatbot AI take to implement?
Typically 3–6 weeks. Shorter (3 weeks) for simple FAQ chatbots. Longer (5–6 weeks) for chatbots with CRM, order system, and lead qualification integrations.
Does the chatbot make up answers?
Not when configured properly. The chatbot uses the company knowledge base (RAG — Retrieval Augmented Generation). If the answer isn't in the base — it says so directly and escalates to an agent.
Which languages does the chatbot support?
OpenAI GPT-4 and Claude support 95+ languages. The chatbot detects the customer's language automatically and answers in that language, regardless of the knowledge base language.
Can I embed the chatbot on WordPress?
Yes. We have widgets ready for WordPress, custom HTML sites, Shopify, custom solutions. Widget deployment — hours.
What about GDPR and conversation logging?
The chatbot informs about conversation logging (GDPR-compliant). Transcripts saved in the CRM on the customer. Configurable retention policy. GDPR consent handled in conversation.
How does chatbot maintenance work?
OpenAI/Claude is billed per token, Azure AI Search per query — so the cost model scales smoothly with traffic. On our side it's typically a few hours of consultant time per month for answer quality monitoring and knowledge base tuning.
Are trainings part of the package?
Yes. Sessions for support (escalation handling in Teams), admins (knowledge base management, monitoring), leadership (how to read metrics).
Related materials
Related solutions
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In-depth analysis
Chatbot AI — what to know
The difference between a chatbot and a voicebot isn't just the channel. Text is asynchronous — the customer reads, thinks, sometimes pauses and comes back an hour later. They type complex questions, paste screenshots, expect links and clear next steps. A voicebot operates in real time, where every second of silence works against it. Chatbot AI is the right tool when the customer needs to reference documentation, compare offers or check specifications — situations that require precision and memory, not speed.
The strongest property of a modern chatbot AI isn't the language model itself, but the quality of the knowledge base it pulls from — RAG (Retrieval-Augmented Generation). A generic ChatGPT answers in generalities. A chatbot grounded in your refund policy, service pricing and product descriptions answers with specifics. That's the difference between „probably yes” and „under our policy we accept returns within 14 days, here's the form link”. The whole rollout revolves around that layer — the quality of your documents and how precisely they're indexed in Azure AI Search.
Chatbot vs. live chat vs. hybrid is a scale-and-question-type decision. A shop with 30 inquiries a day and complete documentation — a standalone chatbot will pick up 80% of the traffic. B2B support with 200 tickets and highly individual cases — a hybrid: chatbot qualifies, classifies and hands off to a consultant with full context. Pure live chat is a fit only for low-volume and heavily regulated industries. Each of the three variants has a different dashboard, different metrics and a different maintenance model.