Companies with heavy customer support
Teams that need an assistant to handle frequent questions and requests around the clock.
Artificial Intelligence
AI solutions built into your business
LLM-based assistants, search across internal documents, automatic translation and process automation. We build AI into the systems your team already uses every day.
Who is it for?
Teams with knowledge spread across documents
People looking for fast, sourced answers in procedures, contracts and technical documents.
Units with repetitive workloads
Departments that want to automate data entry, classification, summarisation and translation.
What we do
AI assistants
Assistants that work on your website, on WhatsApp or inside internal systems.
- Customer support assistant
- Internal knowledge assistant
- Handover to a human agent
Document search (RAG)
Search that answers from your own documents and cites its sources.
- PDF, Word and web pages
- Answers with sources
- Permission-based access
Automatic translation
The translation stack we use in Dynamic Translator.
- 110+ languages
- Quality control with human editing
- Translation in your brand voice
Process automation
Automating repetitive work with AI.
- Classification and summarisation
- Form and document reading
- Reporting
Image and signal processing
Models that extract meaning from camera, sensor and measurement data.
- Image processing
- Signal processing
- Data analytics
Why AntArge?
We use AI in our own product: Dynamic Translator combines AI translation with human editing to translate websites into more than 110 languages. The same team builds every layer, from data preparation and the model to the API and the user interface.
Technologies we use
How we work
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First meeting
We listen to your needs, goals and constraints in a free first meeting.
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Scope and quote
We put the scope in writing and prepare an itemised quote.
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Design and development
We work in short stages and share a working version with you at the end of each one.
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Launch and support
We launch, then stay with you for maintenance, monitoring and new features.
Related projects
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We decide together at the start: whether to use a cloud-based model or a setup where data never leaves your company depends on your needs and how sensitive the data is.
What if the assistant gives a wrong answer?
We ground the assistant in your own documents and make it cite its sources; when it isn’t confident, it hands the conversation over to a human agent.
How do we measure results?
We agree on the metric at the start, such as the share of requests answered, time saved or error rate.