Data centers consume massive energy and expose sensitive data. We build local on-premise LLMs and edge AI agents so anyone can conduct scientific research and run compliance audits entirely on their own machine.
Our Vision
Centralized cloud data centers drain immense energy and compromise privacy. Convai Innovations focuses on building on-premise local LLMs and edge AI models that process data right where it lives — on your workstation or private server.
By eliminating reliance on energy-heavy cloud data centers, on-premise AI cuts carbon footprints while giving you 100% data ownership and air-gapped security.
Anyone with a curiosity or question can become a scientist. Our AI co-scientist (Nadhi) runs on consumer hardware to fetch literature, execute code, and draft findings locally.
No hallucinated facts or unverified claims. Powered by our peer-reviewed confidence-routing research, every output is traceable to real downloaded documents and local files.
On-Premise Architecture
From your local files to verified results — zero cloud data center dependencies.
Point the local agent to your research directory, papers, clinical scans, or policies.
On-premise model evaluates confidence signals to eliminate pre-generation hallucinations.
Indexes local documents and fetches open literature directly without cloud storage.
Outputs cited manuscript edits with .bak undo, offline DPDP audits, & clinical reports.
Our Products
Built to run locally on your device with zero data center transmission.
A desktop multi-agent co-scientist that empowers anyone to conduct rigorous research. Nadhi reads open-access literature, connects findings across local files, executes Python code, and edits manuscripts right on your PC.
The air-gapped DPDP compliance auditor for India's Digital Personal Data Protection Act. Runs 100% offline on your hardware or local Ollama server with zero cloud dependencies and zero telemetry.
Air-gapped, zero telemetry
Pre-configured audit checklists
Run on your private server
Single-machine bound key
An offline cardiac AI diagnostic tool designed in collaboration with medical doctors. Uses LoRA-tuned multimodal AI to interpret 12-lead ECG waveforms and blood reports directly on clinic workstations.
Parameter-efficient LoRA fine-tuning for multimodal ECG analysis without cloud patient data transfer.
Our Researches
Our methods are backed by our own published research in confidence routing, multimodal medical AI, custom RAG embeddings, and reinforcement learning.
Multi-signal confidence routing for pre-generation hallucination suppression, guaranteeing that every AI output in Nadhi is grounded in verifiable source literature.
Read paper on arXiv →Parameter-efficient LoRA fine-tuning for multimodal LLaMA 3.2, achieving high-accuracy 12-lead ECG waveform interpretation for on-device medical decision support in AI4Cardio.
Read paper on arXiv →Building custom embedding architectures for local document Retrieval-Augmented Generation, powering high-accuracy local file synthesis without third-party cloud vector stores.
Read paper on arXiv →Personalized agent frameworks powered by Advantage Actor-Critic (A2C) reinforcement learning, laying the foundation for continuous, on-device agent adaptation.
Read paper on arXiv →