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AI Chatbots for your business

We design conversational chatbots based on LLMs (GPT, Claude, Gemini, open-source models) integrated with your business systems. They handle complex requests, qualify leads in real-time, and take repetitive requests off your support team while maintaining a tone of voice consistent with your brand.

Intelligent virtual assistants that manage 24/7 customer support, qualify leads, and increase conversions.

Use cases

  • 24/7 multilingual customer service for e-commerce
  • Lead qualification for B2B agencies
  • Automated bookings for clinics and professional firms
  • User onboarding for SaaS
  • Internal HR assistant and corporate knowledge base

Measurable benefits

  • Fewer repetitive requests handled by an agent
  • Immediate response 24/7/365
  • Leads qualified automatically, around the clock
  • Infinite scalability without hiring

Technical details

Language models

  • GPT-4o, Claude 3.5, Gemini 1.5 Pro
  • Self-hosted open-source models (Llama, Mistral)
  • Fine-tuning on corporate data
  • RAG with proprietary knowledge base

Channels and integrations

  • WhatsApp Business API
  • Customizable web widget
  • Mobile App (iOS, Android, React Native)
  • Messenger, Instagram DM, Telegram

CRM/ERP Integrations

  • Salesforce, HubSpot, Pipedrive
  • Zendesk, Freshdesk, Intercom
  • Custom webhooks to ERP
  • Real-time synchronization

Security and compliance

  • GDPR and privacy by design
  • End-to-end encryption
  • Full audit logs
  • EU data residency

How we build a conversational assistant

  1. Knowledge ingestion — We collect the sources the assistant is allowed to cite — FAQs, documentation, product data, internal procedures — and normalise them: version, content owner, last update.
  2. Retrieval — We index the content and configure semantic retrieval with filters for channel, language and permissions, so every answer traces back to an identifiable document.
  3. Prompting and evaluation — We define the answer scope and build a test set with expected outcomes. Every prompt change is re-measured on the same set, so regressions surface before release.
  4. Integrations — We connect the assistant to the channels people already use and to the systems it must read or update: tickets, orders, customer records, calendars.
  5. Human handoff — We set the conditions for escalation: low retrieval confidence, topics outside scope, explicit user request, dissatisfaction signals.
  6. Monitoring — After go-live we review unresolved conversations, out-of-scope questions, cost per conversation and source quality; that is what drives the next iteration.

We hand over prompts, retrieval configuration, evaluation sets and integration documentation, so the assistant can be maintained without us.

Working with us

The team that analyses the process is the team that builds and maintains it: product, engineering, integration with your existing systems, governance of automated decisions and post-release support.

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FAQ

How long does it take to launch an AI chatbot?

An MVP is online in 2-4 weeks. Enterprise versions with complex integrations require 6-10 weeks.

Can I use my company's data?

Yes. We implement RAG (Retrieval Augmented Generation) on your documents, FAQ, and knowledge base, keeping everything private.

Can the chatbot transfer the conversation to a human operator?

Yes, handoff is automatic when the bot detects complex requests or explicit user dissatisfaction.

How much does an AI chatbot cost?

Pricing starts at approximately €4,000 for a complete custom chatbot. The price varies based on channels, integrations, and conversation volume.