Artificial Intelligence for Businesses
Integrate AI into your applications and processes: search, analyse, assist, automate — without replacing human judgement.
Information hard to find, documents long to process
In many companies:
- finding information means digging through folders or bothering a colleague;
- reading and summarising a document takes hours that could be better spent;
- the same answers are written again and again from the same information;
- existing tools are hard to use or require long training.
AI can accelerate these situations, provided it is integrated where it brings real value.
Generative AI, assistant, agent: what we are talking about
Generative AI produces content — text, answers, summaries — from a request and provided information.
An AI assistant helps a person: it answers questions, writes, summarises, translates. The person stays in control.
An AI agent goes further: it can chain actions — search for information, call a tool, perform an authorised task — to reach a defined goal.
RAG (searching in company documents) allows these systems to answer from your information, not general knowledge: this is what makes AI useful and reliable in a professional context.
AI integrated into your processes, not demos
Technea integrates AI into concrete cases:
- AI assistants: answering internal questions, writing assistance, document summaries;
- AI agents: information retrieval, chained task execution, tool control;
- RAG: AI answering from company documents, with sources;
- document search: finding the right document, passage, answer;
- document analysis: extracting, classifying, summarising business documents;
- integration into applications: AI in your tools, not in a separate tool;
- automation with AI: AI decides or prepares, the workflow executes.
Concrete examples
- Document analysis: understanding and classifying technical or administrative documents.
- Document search: finding information in thousands of pages.
- Classification: automatically sorting incoming documents.
- Knowledge base: an assistant answering from internal procedures.
How an AI project starts
1. Need identification: which task, which gain, which data. 2. Proof of feasibility: trial on a real case with your documents. 3. Integration: AI in the process, application or tool. 4. Evaluation: measuring answer quality and gains. 5. Continuous improvement: adjustments based on real usage.
What it changes
- faster answers: information found in seconds;
- documents processed at scale: analysis, classification, extraction;
- controlled quality: answers rely on your documents, not assumptions;
- teams focused on decisions, not searching;
- a reusable foundation: the same infrastructure serves several use cases.
Frequently asked questions
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An AI use case for your business?
Let's identify together the first task AI can accelerate.