Service
AI agentic systems for complex processes.
Clait develops LLM-based agentic systems: specialised AI agents that work together to orchestrate complex workflows, make decisions, correct themselves and work with the data and tools a company already uses.
Published
01
More than automation
We design systems in which specialised AI agents work together to orchestrate complex workflows, make autonomous decisions and handle multi-step processes from start to finish. This is not simple automation: these systems reason, correct themselves and plan, coordinating tasks in parallel.
Starting from pre-trained language models, we build systems that slot easily into a company’s existing processes.
02
What the agents do
Query databases
They turn natural-language questions into queries on company data.
Extract information
They draw insights from documents.
Produce structured output
Results ready for the next steps of the process.
Integrate
They work natively with the systems a company already uses.
03
Applications
With multi-agent architectures, persistent memory and advanced reasoning, applications range from conversational business intelligence to smart customer support, and from decision automation to large-scale company profiling.
FAQ
Frequently asked questions.
How is an AI agent different from ordinary automation?
Traditional automation follows fixed steps. Clait’s agentic systems reason, plan and correct themselves, coordinating several tasks in parallel within complex workflows.
Do the agents integrate with existing systems?
Yes: they query databases, extract information from documents and generate structured output that integrates with the systems a company already uses.
Can a database be queried in natural language?
Yes. Clait has built a conversational business intelligence platform that turns natural-language questions into SQL queries and interactive charts.
Keep reading.
- Case study · 01A RAG-based AI assistant for an e-learning platformClait built a RAG-based AI assistant for a Moodle e-learning platform: it reads and indexes the courses’ PDF documents and answers learners’ questions only from the courses they are enrolled in, citing document and page.RAG · LLM · E-learning
- Case study · 02Querying a database in natural languageClait built a conversational business intelligence platform: staff type a question in plain language, the system turns it into an optimised SQL query and returns an interactive chart, with no SQL knowledge needed.Agentic systems · LLM · SQL
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