Artificial intelligence for the processes where the business loses time

We bring AI in where it helps the team work faster with documents, requests, knowledge and repetitive knowledge work.

Discuss your task See the Aivex approach

Why this task comes up

AI should not begin with the question “which model do we pick”. The right question is where the team searches for information every day, classifies enquiries, answers the same questions, checks data or prepares decisions from a template. If the process is not described, AI stays an experiment. If the process is clear, AI can become a working tool.

When to get in touch

  • Staff spend a long time looking for answers in documents, PDFs, policies and knowledge bases.
  • Support repeats the same answers over and over.
  • New hires take a long time to get up to speed.
  • Requests have to be classified or routed by hand.
  • AI has already been trialled but never built into the process.
  • You need quality control, sources behind the answers and a handover of hard cases to a person.

What Aivex does

Aivex designs AI as part of a digital process. We define the data source, the use case, the limits, the oversight, the role of the human and the quality criteria. That approach keeps the “magic” out and makes AI useful in real work.

  • AI assistants for staff and support
  • RAG systems and search across documents
  • AI agents for tasks, checks and actions through APIs
  • classification of enquiries, texts and documents
  • human-in-the-loop for the doubtful cases
  • logs, quality assessment and control over sources
  • integrations with CRM, knowledge bases and internal systems

How we work

  1. We find the scenario where AI can be genuinely useful.

  2. We check the data: quality, access, structure, limitations.

  3. We design the MVP: what the AI does on its own, where a person has to check.

  4. We integrate the AI into the interface, the CRM, support or a backend process.

  5. We measure quality and develop the scenario after launch.

What the client gets

  • the team gets answers and prompts faster
  • support carries less of the repeat-question load
  • the AI works with the company’s own data, not from thin air
  • the business keeps oversight, sources and logs
  • the AI scenario can grow without becoming unmanageable

What it looks like in practice

A support team was hunting for answers across manuals, PDFs and internal policies. Aivex designed a RAG assistant: the documents became the sources, the operator sees a prompt together with the passage it rests on, and the doubtful cases go to a person. AI made the work faster without taking control away from the team.

Frequently asked questions

Can we start with a single AI scenario?

Yes, and that is the best format: one process, data you understand, a limited MVP and clear quality criteria.

Will the AI answer clients on its own?

Not necessarily. It is often safer to start with AI prompts for the operator rather than a fully automatic reply.

What should we prepare before we start?

Documents, knowledge bases, FAQs, examples of real enquiries, the limits, the roles and the rules for handing hard cases to a person.

Show us the task you need solved

Describe where you are now: what you have today, where the team loses time, which systems you use and what result you need. We will suggest the next step — discovery, an MVP, an improvement or a roadmap.

Discuss your task