Generative AI that runs on your own infrastructure
We build document assistants, AI agents and analytics copilots that run on servers you control. Your documents, prompts and answers stay inside your network, which keeps you in line with KVKK and your own security policies.
Since 2023 we have delivered RAG chatbots, document translation, summarisation, multimodal document search and agent-based dashboards for aviation, energy, transport and public sector organisations.
Why organisations keep AI in-house
Your data stays with you
Models run on your hardware or in your private cloud. Nothing is sent to an external AI service, so sensitive and personal data never leaves your control.
Predictable cost
No per-token bills that grow with every user. One GPU server can serve a whole department, and the cost is known in advance.
Models you choose
We use open-weight models such as Llama and Qwen and pick the right size for each task. You can switch or upgrade models without changing your applications.
What we build
Document Q&A (RAG)
Chat with your policies, contracts, audit reports and training material. Every answer links back to the source document, so users can check it.
Scanned document understanding
Vision-language models read scanned pages directly, including tables and images, without a separate OCR step. Compare documents and find which file contains a fact.
Document translation
Translate large volumes of internal documents on your own servers, integrated with your document management system, instead of paying per page.
Summarisation
Turn long reports and document sets into short summaries, so teams can find what matters faster.
Natural-language analytics
Ask a question in plain language and get a chart or dashboard. Agents query your data warehouse, analytical cubes and APIs, and respect existing access rules.
Custom AI agents
Multi-step agents that retrieve data, run Python, produce reports and connect to your systems, built to your own workflow.
Ask a question, get a chart
Ask a question in plain language and get a chart or dashboard. Agents query your data warehouse, analytical cubes and APIs, and respect existing access rules.
- Plain language in. No SQL or report request needed.
- Transparent. The generated query is shown, so analysts can check it.
- On your servers. The model runs locally, next to your data.
The demo uses sample data. We have built agentic dashboards like this for customer projects.
Example: a user asks “Show revenue by region for this year”. The assistant finds the right tables, writes and runs a SQL query on the data warehouse and answers with a chart. Sample data.
From first use case to production
Discovery
We choose one use case with clear value, review your documents and data, and agree how success will be measured.
Pilot
We build a working version on a limited document set, test answer quality with your users and size the hardware.
Production
We add single sign-on, access control, logging and monitoring, and connect the solution to your systems.
Operate and improve
We measure answer quality, update models and documents, and add new use cases on the same platform.
Proven, open technology
- vLLM inference
- Llama and Qwen models
- Vision-language models
- LangChain and LangGraph
- LlamaIndex and Docling
- PostgreSQL with pgvector
- Kubernetes
- Oracle, SQL Server, S3
Where it runs
- InfoTelica AI Tower, a GPU workstation for a team or department. See configurations
- Your existing servers in your data centre or private cloud.
- Shared GPU platform for many teams, with self-service and quotas, built with our partner Rafay.
Start with one use case
Tell us which documents or questions take your team the most time. We will suggest a pilot and the hardware it needs.