Nodko offers an on‑premise AI platform that runs large language, transcription, vision, and embedding models directly on a company’s own hardware, ensuring that no data ever leaves the organization. The solution supports open‑weight models up to 70 B parameters, provides full logging, explainability, and audit trails, and includes an optional human‑validation step to reduce errors in regulated environments.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises handling sensitive or regulated data often cannot use cloud-based AI services because of confidentiality, compliance, and data residency requirements. This limits their ability to automate repetitive tasks and leverage advanced AI models without risking data leakage or dependence on external providers.
Solution
Nodko provides an on‑premise AI platform that runs large language, transcription, vision and embedding models directly on a company’s own hardware. By eliminating outbound API calls and cloud dependencies, all prompts, documents, and AI outputs remain within the organization’s infrastructure, ensuring confidentiality and auditability. The platform supports a range of open‑weight models (e.g., Llama, Mistral, Qwen) up to 70 B parameters, delivering state‑of‑the‑art performance without compromising security. Users can access the AI via a web interface hosted on a shared server or install it on individual workstations, allowing flexible deployment while maintaining zero‑internet operation. An optional human‑validation step lets teams review AI‑generated results before they are committed, reducing errors and rework while keeping workflows traceable and auditable.
Target Audience
Primary customers are enterprises, government agencies, and regulated industries (e.g., finance, healthcare, legal) that require secure, on‑premise AI capabilities for internal workflows.
Features
- Fully on‑premise inference with zero outbound API calls, guaranteeing that no data leaves the organization
- Support for open‑weight LLMs, transcription, vision and embedding models up to 70 B parameters, selectable per hardware capacity
- Dual deployment options: centralized server appliance accessed via LAN or per‑device installation for complete offline operation
- Comprehensive logging, explainability and audit trails for every AI action, meeting regulatory traceability requirements
- Optional human‑validation workflow that inserts a review step before AI outputs are finalized
- Compatibility with existing tools through agile AI agents that can be integrated into current processes without custom code