What data does the AI chatbot learn from?

Your chatbot answers from your own business data: documents, FAQs, knowledge base articles, CMS content and product catalogues. We use RAG (retrieval-augmented generation) with a vector database hosted in the EU and LangChain orchestration, so the AI retrieves the relevant source before it generates any response. Answers come with citations traceable to the original document, and when the sources don't cover a question, the chatbot is instructed to say so and hand over instead of guessing. We test that behaviour before launch.

Does the chatbot support Croatian and other languages?

Yes. Current OpenAI and Anthropic models handle Croatian, English, German and many other languages well, so one chatbot can serve several languages without a separate build per language. The chatbot detects the visitor's language from their first message and responds in kind. This is particularly valuable for tourism, real estate, and e-commerce businesses serving international clients across multiple EU markets from a single deployment.

Where can the chatbot be deployed?

On your website, mobile app, WhatsApp, Viber, Facebook Messenger, or as an internal Slack or Microsoft Teams bot. We build one AI core powered by LangChain and Pinecone, then deploy it across multiple channels through n8n workflow automation. This means every channel shares the same knowledge base and conversation quality. Adding a new channel is a much smaller job than the first build, because the AI logic is decoupled from the delivery interface.

How do you measure chatbot performance?

We track four core metrics through a real-time analytics dashboard: automated resolution rate (percentage of queries resolved without human handoff), average conversation length, user satisfaction ratings collected post-interaction, and conversions directly attributed to chatbot interactions. The resolution rate is tracked from the first week, so you see what the agent handles on its own before relying on it. The dashboard also surfaces unanswered questions, so your knowledge base improves continuously based on real visitor behaviour.

Will the chatbot make answers up?

It can, if you let it, which is why we do not build it that way. A language model naturally produces text that sounds convincing, so without constraints it will answer things it does not know. The fix is to have it answer only from your own documents and pages, retrieving the source before generating a reply, and to explicitly allow it to say it does not have the information. Before launch we test it against real customer questions, including ones with no answer on the site.

Does a chatbot replace our support team?

No, and it should not. A chatbot handles repeat questions well, and those are most of the volume: opening hours, availability, pricing, order status, how something works. Anything requiring judgement, an exception, or a person taking responsibility stays with people. The realistic benefit is that your support team stops spending the day on the same three questions, not that the team disappears.

What happens when the chatbot does not know the answer?

It says so and hands the conversation on. That behaviour is deliberate: a clear handover to a contact form, email or a real person beats a guess that sends the customer in the wrong direction. Every handover is logged, so you can see which questions keep coming back unanswered. That list is usually the most useful by-product of the whole project, because it shows what your pages are missing.

What does a chatbot cost to run once it is built?

The running cost is not our fee but model usage and hosting, and it scales with the number of conversations, the length of answers and the model chosen. Using a smaller model for simple questions and a larger one only for complex queries keeps that low, which is how we build it. Before launch we estimate a range from your expected traffic, and after launch you see actual usage instead of an estimate. What we do not do is a flat monthly figure that stays the same whether the bot gets used or not.

Is a chatbot GDPR compliant?

It is when it is built that way, and that is part of the work rather than an afterthought. In practice: visitors are told they are talking to a bot, conversation content is not used to train models, only data with a purpose is retained and it has a deletion period, and processing runs with a provider covered by a data processing agreement. If the bot touches customer records in your systems, the scope of that access is defined before anything is connected.

How much content do we need for the bot to be useful?

Less than most people expect, but it has to be accurate. The bot does not invent knowledge; it searches what you give it, so a handful of well-written documents on services, pricing, process and common questions usually beats a hundred pages of general marketing copy. If your answers contradict each other from document to document, the bot will expose that. Uncomfortable, but useful: the contradictions it surfaces were there before the bot existed.

Can the bot complete an action, not just answer?

Yes, when it is connected to the system that performs the action. It can look up an order status, check an open slot, send a quote or open a ticket in your system, provided that system exposes an interface for it. Without that connection the bot stays at the level of information. That is why, before estimating, we ask what exactly you want it to do and which system it has to talk to.

How do you prevent the chatbot from being abused?

By limiting scope, capping messages and monitoring usage. The bot answers only within your subject and refuses to be steered into unrelated requests, message counts per visitor are capped so cost and abuse cannot grow unchecked, and conversations are logged so unusual patterns are visible. None of this is perfect protection, but it covers the realistic cases: automated resource burning and attempts to push the bot into a different role.

AI Chatbots

A scripted chatbot breaks the moment a customer phrases something it didn't expect. We build custom AI agents grounded in your own data with retrieval-augmented generation. They answer accurately, qualify leads in real time, and book appointments around the clock, in any language. They resolve routine inquiries on their own and escalate the rest with full context intact. And if AI is the wrong tool for your case, we'll tell you that before you spend a euro.

Request a support-bot audit

5.0· 29 Google reviewsSenior EU engineersFull code ownershipGDPR-compliant by default

AI Chatbot Development

We build an AI agent that answers from your own content, not a rigid script that breaks the moment someone phrases things differently.

What is a RAG system and when is it worth it?

A RAG system (retrieval-augmented generation) answers questions from your own documents: it first finds the matching passages in a vector database and only then lets the language model write the answer, based on exactly those passages. The chatbot answers with your knowledge instead of the model's general training data.

A RAG system is worth it when:

the same questions keep coming in and the answer is already written down somewhere, such as manuals, product data, policies or past support replies,
your team spends time today looking those answers up and typing them out,
every answer has to be traceable, because it rests on a specific document.

It is not worth it when the answer is not documented anywhere or changes by the minute, such as stock levels in your ERP. Then a direct query to the API is the right approach, and we tell you so before the project.

The assistant on this website works on the same principle: it answers questions from our own content, indexed in a Pinecone vector database, and takes the language of the page into account.

What Separates AI Agents from Rule-Based Bots?

A generative AI agent is a conversational system powered by large language models (LLMs) that understands natural language, maintains context across a conversation, and generates precise answers from a verified knowledge base. Unlike rule-based chatbots that follow decision trees, AI agents interpret intent, even when users phrase questions in unexpected ways.

Rule-based chatbots fail when a user's input does not match a predefined keyword or flow. Gartner predicts that by 2027, chatbots will become the primary customer service channel for roughly a quarter of organisations. The shift is driven by LLMs' ability to handle ambiguous queries, multi-turn conversations, and multilingual support natively.

We build agents using LangChain orchestration with OpenAI and Anthropic models, selected per use case for the optimal balance of accuracy, latency, and cost. Each agent is configured with strict guardrails: topic boundaries, escalation triggers, and hallucination prevention through retrieval-augmented generation.

The result is a digital team member that conducts fluid conversations, resolves routine inquiries without human involvement, and hands off complex cases with full context preserved.

How RAG Prevents AI Hallucination

Retrieval-augmented generation (RAG) is an AI architecture pattern that grounds LLM responses in verified, domain-specific documents rather than the model's general training data. This greatly reduces hallucination, where AI generates plausible but factually incorrect answers, by restricting the response source to your approved content.

We structure your business documents (product catalogues, service manuals, pricing sheets, FAQs, past support transcripts) into a vector database hosted in the EU (pgvector on Supabase, or Pinecone). When a customer asks a question, the system retrieves the most relevant document chunks, injects them into the LLM's context window, and generates an answer grounded in your verified facts.

Every response includes source attribution. If the knowledge base does not contain a sufficient answer, the agent is instructed to say so and escalate instead of guessing, and we test that behaviour before launch.

Data sovereignty is non-negotiable. Your documents remain on your infrastructure or in an EU-hosted environment. They are never used to train public models and are never shared across clients. GDPR requirements are built into the data pipeline from ingestion to deletion.

What Can an AI Agent Actually Do?

An AI agent integration is a system connection that allows the agent to perform actions, not just answer questions, by interacting with your existing business software through APIs. The difference between a chatbot and an agent is that an agent acts on information rather than merely retrieving it.

We connect AI agents to the tools your team already uses:

Order and shipment status: The agent queries your ERP (for example Pantheon, Luceed or SAP) in real time and returns tracking information directly in the chat
CRM updates: New leads are automatically created in your CRM (for example HubSpot, Salesforce or Pipedrive) with conversation context, qualification score, and source attribution attached
Appointment booking: The agent checks calendar availability and books sales calls or service appointments directly, including timezone handling for international clients
Helpdesk ticketing: Unresolved issues create structured tickets in Zendesk, Freshdesk, or your internal system with full conversation history

Inbound scheduling is a typical first candidate: the agent books, confirms and reschedules without staff involvement. The sales team stops losing time to back-and-forth email scheduling.

How Does AI Lead Qualification Work?

AI-powered lead qualification is the automated process of scoring, categorising, and routing inbound inquiries based on predefined criteria (budget, timeline, authority and need) before a human sales representative is involved. Speed of first response matters: according to the MIT and InsideSales lead response study, leads contacted within 5 minutes are 21 times more likely to enter the sales pipeline than those contacted after 30 minutes.

Our AI agents respond instantly at any hour, in any language. They ask qualifying questions conversationally, score responses against your ideal customer profile, and route high-intent leads directly to the right team member via Slack, email, or CRM notification.

Low-quality inquiries (spam, irrelevant requests, tire-kickers) are filtered automatically. Your sales team spends its time on conversations with a realistic probability of closing.

How Do We Ensure GDPR Compliance in AI?

GDPR-compliant AI deployment is the practice of building conversational agents that collect, process, and store personal data in accordance with EU General Data Protection Regulation requirements. For any business operating in the EU or serving EU citizens, this is a legal obligation, not a feature.

Our AI agents implement compliance at the architecture level:

Data minimisation: Agents collect only the information required for the specific interaction
Consent management: Users are informed about data processing before the conversation begins, with clear opt-in mechanisms
Right to erasure: Conversation logs and personal data can be deleted on request through automated workflows
Access controls: Role-based permissions determine who can view conversation history, export data, or modify agent behaviour
EU data residency: Data is stored in the EU, and when conversations must stay in the EU we use EU-hosted models

We implement audit logging, incident response procedures, and supply chain security documentation as part of every deployment.

From Kickoff to Live Agent, Step by Step

AI agent deployment is a structured process that transforms raw business knowledge into a production-ready conversational system. Deploying without a defined methodology leads to agents that hallucinate, frustrate users, and damage brand trust. Our four-step process reduces those risks through validation at every stage.

Step 1. Knowledge Audit: We audit every content source your AI needs to reference: product catalogues, FAQs, pricing sheets, service manuals, and past support transcripts. We identify knowledge gaps, content that must be created before the agent can answer accurately. Output: a verified content checklist your team reviews and approves before development begins.

Step 2. RAG Architecture and Configuration: Your verified content is structured into an EU-hosted vector database using optimised chunking strategies. The LLM is configured with tone-of-voice parameters, escalation rules, topic boundaries, and fallback behaviours that match your brand. We select the optimal model (OpenAI, Anthropic Claude, or a mix) based on your accuracy and latency requirements.

Step 3. System Integration: The agent connects to your existing tools: CRM for lead capture, booking software for appointments, helpdesk for ticket creation, and messaging platforms (WhatsApp, Messenger, website widget) for multichannel presence. Every integration is tested with real data.

Step 4. Adversarial QA and Launch: We run the agent through a test set of real and adversarial conversations covering edge cases, ambiguous inputs, out-of-scope questions, and adversarial prompts. Only when the agent passes our quality threshold on a set of real test queries does it go live.

Every deployment's complexity is different, so the Knowledge Audit is what confirms your real timeline, not a guess upfront.

What Happens After Launch?

A chatbot is not finished at launch: your offer changes, your documents change, and customers ask questions nobody planned for. After launch, we can look after it on a monthly plan: we review real conversations and unanswered questions, update the knowledge base, rerun the tests after model updates, watch the cost per conversation and fix what breaks. New features are separate projects with their own fixed price, so the monthly plan stays predictable.

AI Chatbot vs Rule-Based vs Human Support

AI chatbots, rule-based chatbots, and human support each have distinct strengths. AI chatbots combine 24/7 availability with natural language understanding and system integration. Rule-based bots offer predictability but break on unexpected inputs. Human support provides the highest empathy but is constrained by cost, availability, and scalability.

Criterion	AI chatbot (Neviox)	Rule-based chatbot	Human support
Availability	24/7 uninterrupted	24/7 uninterrupted	Business hours
Natural language understanding	High (LLM)	Limited (keywords)	Full
Simultaneous conversations	Unlimited	Unlimited	Team-limited
Cost per conversation	Low (API calls)	Low	High (hourly wage)
Answer personalisation	High (RAG + context)	Low	High
CRM / ERP integration	Native API	Complex	Manual
Multilingual support	Native (100+ languages)	Translation required	Team-dependent
Training & maintenance	Knowledge base updates	Manual tree editing	Employee onboarding

Quick Fix

Don't need a new chatbot? We fix a specific bug in the one you have.

If your existing chatbot isn't working correctly, you don't need a new project. We take on the exact fix with a price estimate before we start.

Fix a bug in your existing chatbot
Widget won't load, loses conversation context, or drops responses.

We also work remotely with clients in:

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Frequently Asked Questions

Why Neviox Digital

AI you stay in control of
It answers from your documents and cites the source, and we test it on your real questions before launch.
Your code, your accounts
The code, the domain and every account are yours from day one, with no annual licence and no lock-in to us.
Price agreed up front
AI chatbots start from €2,000. We set the exact price after reviewing the scope and agree it in writing, fixed per phase, before work starts.

Send Us Your Site, Repo or Idea. Our Engineers Reply in Writing.

We reply as soon as we can, usually within a few days, and tell you what we would change first.

Request an audit

What we shipped, what broke and what we would do differently. No news roundups.

What is a RAG system and when is it worth it?

A RAG system is worth it when:

the same questions keep coming in and the answer is already written down somewhere, such as manuals, product data, policies or past support replies,
your team spends time today looking those answers up and typing them out,
every answer has to be traceable, because it rests on a specific document.

The assistant on this website works on the same principle: it answers questions from our own content, indexed in a Pinecone vector database, and takes the language of the page into account.

What Separates AI Agents from Rule-Based Bots?

The result is a digital team member that conducts fluid conversations, resolves routine inquiries without human involvement, and hands off complex cases with full context preserved.

How RAG Prevents AI Hallucination

What Can an AI Agent Actually Do?

We connect AI agents to the tools your team already uses:

Order and shipment status: The agent queries your ERP (for example Pantheon, Luceed or SAP) in real time and returns tracking information directly in the chat
CRM updates: New leads are automatically created in your CRM (for example HubSpot, Salesforce or Pipedrive) with conversation context, qualification score, and source attribution attached
Appointment booking: The agent checks calendar availability and books sales calls or service appointments directly, including timezone handling for international clients
Helpdesk ticketing: Unresolved issues create structured tickets in Zendesk, Freshdesk, or your internal system with full conversation history

Inbound scheduling is a typical first candidate: the agent books, confirms and reschedules without staff involvement. The sales team stops losing time to back-and-forth email scheduling.

How Does AI Lead Qualification Work?

Low-quality inquiries (spam, irrelevant requests, tire-kickers) are filtered automatically. Your sales team spends its time on conversations with a realistic probability of closing.

How Do We Ensure GDPR Compliance in AI?

Our AI agents implement compliance at the architecture level:

Data minimisation: Agents collect only the information required for the specific interaction
Consent management: Users are informed about data processing before the conversation begins, with clear opt-in mechanisms
Right to erasure: Conversation logs and personal data can be deleted on request through automated workflows
Access controls: Role-based permissions determine who can view conversation history, export data, or modify agent behaviour
EU data residency: Data is stored in the EU, and when conversations must stay in the EU we use EU-hosted models

We implement audit logging, incident response procedures, and supply chain security documentation as part of every deployment.

From Kickoff to Live Agent, Step by Step

Every deployment's complexity is different, so the Knowledge Audit is what confirms your real timeline, not a guess upfront.

What Happens After Launch?

AI Chatbot vs Rule-Based vs Human Support

Criterion	AI chatbot (Neviox)	Rule-based chatbot	Human support
Availability	24/7 uninterrupted	24/7 uninterrupted	Business hours
Natural language understanding	High (LLM)	Limited (keywords)	Full
Simultaneous conversations	Unlimited	Unlimited	Team-limited
Cost per conversation	Low (API calls)	Low	High (hourly wage)
Answer personalisation	High (RAG + context)	Low	High
CRM / ERP integration	Native API	Complex	Manual
Multilingual support	Native (100+ languages)	Translation required	Team-dependent
Training & maintenance	Knowledge base updates	Manual tree editing	Employee onboarding

Quick Fix

Don't need a new chatbot? We fix a specific bug in the one you have.

If your existing chatbot isn't working correctly, you don't need a new project. We take on the exact fix with a price estimate before we start.

Fix a bug in your existing chatbot
Widget won't load, loses conversation context, or drops responses.

What is a RAG system and when is it worth it?

What Separates AI Agents from Rule-Based Bots?

How RAG Prevents AI Hallucination

What Can an AI Agent Actually Do?

How Does AI Lead Qualification Work?

How Do We Ensure GDPR Compliance in AI?

From Kickoff to Live Agent, Step by Step

What Happens After Launch?

AI Chatbot vs Rule-Based vs Human Support

Quick Fix

We also work remotely with clients in:

Related Services

Latest Projects

What will your project cost?

Frequently Asked Questions

What data does the AI chatbot learn from?

Does the chatbot support Croatian and other languages?

Where can the chatbot be deployed?

How do you measure chatbot performance?

Will the chatbot make answers up?

Does a chatbot replace our support team?

What happens when the chatbot does not know the answer?

What does a chatbot cost to run once it is built?

Is a chatbot GDPR compliant?

How much content do we need for the bot to be useful?

Can the bot complete an action, not just answer?

How do you prevent the chatbot from being abused?

Why Neviox Digital

Send Us Your Site, Repo or Idea. Our Engineers Reply in Writing.

What is a RAG system and when is it worth it?

What Separates AI Agents from Rule-Based Bots?

How RAG Prevents AI Hallucination

What Can an AI Agent Actually Do?

How Does AI Lead Qualification Work?

How Do We Ensure GDPR Compliance in AI?

From Kickoff to Live Agent, Step by Step

What Happens After Launch?

AI Chatbot vs Rule-Based vs Human Support

Quick Fix

We also work remotely with clients in: