Custom AI Chatbots

Chatbots Built Around Your Business

Off-the-shelf chatbots handle off-the-shelf questions. Your customers deserve better. Our custom AI chatbots are trained on your specific knowledge base — your products, your processes, your terminology, your tone of voice. The result is a bot that sounds like your best customer service agent, available 24/7 across every channel.

Architecture Options

We build on the foundation that best fits your needs. For most businesses, we recommend a RAG (Retrieval-Augmented Generation) architecture: your knowledge base is indexed and searchable, and the LLM generates natural responses grounded in your actual data. This approach eliminates hallucination while maintaining conversational fluency.

GPT-4o Integration

OpenAI GPT-4o for maximum language understanding, multi-modal capability (text + images), and the broadest general knowledge. Ideal for customer-facing chatbots where natural conversation quality is the priority. We implement guardrails, content filtering, and response validation to ensure the bot stays on-topic and on-brand.

Claude Integration

Anthropic Claude for applications requiring careful, nuanced responses — healthcare, legal, financial services. Claude excels at following complex instructions and maintaining consistency across long conversations. Constitutional AI training reduces harmful outputs.

Open-Source Models

LLaMA, Mistral, or Phi for data-sensitive deployments where you need full control. Models run on your infrastructure — no data leaves your servers. Higher setup cost but zero per-query API fees and complete data sovereignty.

Knowledge Base Management

We build and maintain the knowledge base your bot learns from: product documentation, FAQ databases, support ticket history, policy documents, and training manuals. Vector database indexing (Pinecone, Weaviate, or pgvector) ensures fast, accurate retrieval. Monthly retraining keeps the bot current as your business evolves.

Investment

Custom chatbot development starts from 149,70 EUR. Includes: requirements analysis, architecture design, NLP training, platform integration, testing, and 30-day post-launch support. Typical timeline: 3-6 weeks depending on complexity.