AI assistants
LLM-based assistants that answer on your content and documents.
AI, chatbots & automation
AI, chatbots and automation applied to real problems.
We integrate artificial intelligence, assistants and chatbots and automate repetitive workflows: concrete LLM-based solutions designed to save time and reduce errors, not to chase hype.
AI is valuable when it solves a problem, not when it makes headlines.
Assistants that answer on your content, chatbots that qualify requests, automation that removes repetitive manual work: we start from the process and use AI only where it adds measurable value.
Companies that want to apply AI to support, operations and repetitive workflows in a concrete way.
- Less repetitive manual work
- Faster answers for customers and teams
- Automated, monitorable processes
- AI integrated into existing systems
AI, chatbots & automation
What it includes
Chatbots
Chatbots for support and qualification, integrated into your channels.
Process automation
Automated flows that connect systems and remove manual steps.
LLM integrations
Integration of language models into products and internal tools.
AI interfaces
AI features inside applications and platforms, with a well-crafted UX.
Reliability
Checks, fallbacks and monitoring for sustainable use of AI.
How we work
01
Discovery
We identify where AI adds real, measurable value.
02
Design
Definition of the flow, data sources and controls.
03
Development
Model integration, automation and interfaces.
04
Testing & QA
Verification of answer quality, edge cases and fallbacks.
05
Launch & evolution
Go-live, monitoring and refinement.
Technologies & expertise
LLMRAGAI APIsWorkflow automationIntegrationsVector databases
Products that prove the expertise
View all products
Frequently asked questions
Does the AI only answer on our content?
Yes, with a RAG approach the assistant answers on your documents and data, reducing made-up responses.
Does it integrate with the systems we use?
Yes, we integrate AI and automation into your existing tools and channels.
How do you handle reliability?
With checks, fallbacks and monitoring, and by using AI only where it is genuinely useful.