ZekaLab MMC
Problem
Modern enterprises and government institutions sit on massive volumes of relational databases and unstructured policy documents. However, extracting actionable insights requires dedicated data engineering teams, writing manual SQL queries, or sifting through hundreds of PDF pages. Furthermore, strict data privacy regulations, national data sovereignty laws, and confidential security protocols strictly prohibit sending internal data to public third-party cloud LLMs. Regional institutions also suffer from poor support for regional languages like Azerbaijani in off-the-shelf Western AI tools.
Solution
ZekaLab solves this by deploying sovereign, air-gapped, client-neutral Generative AI engines directly within the client’s private cloud or on-premise data center. Our hybrid router architecture dynamically delegates queries between specialized SQL Agents (for structured databases) and semantic RAG Agents (for policies and documents). The system operates strictly behind existing enterprise authentication (SSO), ensuring employees only access data they are authorized to see. All computations run on private infrastructure using high-efficiency open-source models (e.g. Llama 3, Qwen) with zero external data leaks and first-class native language comprehension.
Want to connect with ZekaLab MMC?
Sign up on StartupDeal to see the full profile, compare startups with AI and reach the team.