Ugitech Services · Offering
AI agents built for your teams, connected to your tools
Conversational assistants, agents that carry out tasks in your tools, process automation: we design reliable AI agents, connected to your data and governed by clear rules.
What we build
AI agents that work alongside your teams
From the assistant that answers your customers to the agent that processes your files, we build agents that are useful, measurable and under control.
Customer assistants
Answers for your customers on WhatsApp, Telegram, the web or email, at any hour.
Business agents
Agents that carry out tasks in your tools: CRM, ERP, ticketing.
Search across your documents
Sourced answers drawn from your documents and knowledge bases (RAG).
Process automation
Automated sorting, extraction and data entry, with human review where it matters.
Hand-off to your teams
A human takes over on sensitive or complex topics.
Security & governance
Scoped access, hosting in Europe and a log of every agent action.
Process
Six steps, one team, a deadline met
01
Discovery & scoping
Needs, scope, estimate
02
UX/UI design
Journeys, mockups, prototype
03
Development
Sprints and regular demos
04
Testing & UAT
Automated and business testing
05
Go-live
Deployment and app stores
06
Hosting & enhancements
Run, maintenance, new features
Work
Recent projects
- ServicesAccountingWhatsApp AI assistantAn AI assistant on WhatsApp that answers an accounting firm's clients from their own data, and hands over to the team on sensitive topics.Read the case study
- ServicesCoachingAI coach on TelegramAn AI assistant on Telegram that tracks each client's progress and answers their day-to-day questions, in place of the coach.Read the case study
What is the difference between a chatbot and an AI agent?
A chatbot answers questions. An AI agent can also act: look things up in your tools, open a ticket, update a record, within the limits you set.
Does our data stay confidential?
Each agent only accesses the data it needs. Hosting is in the European Union by default, and we choose models and terms of use that do not train the models on your data.
How do you prevent wrong answers?
The agent relies on your sources (documents, databases, tools) rather than general knowledge, cites its sources when useful and hands over to your team according to the rules you define.
How long does it take to put an agent into production?
It depends on the tools to connect and the scope. Our AI assistant for an accounting firm went into production in 6 weeks.
How do you measure results?
We set the metrics before we start (questions handled, time saved, share of requests passed to your team), then track them after launch.
What do you need?
Three questions to point you to the right expertise, or a direct conversation with a consultant.