On-Device Intelligence for Healthcare and Scientific Discovery
Beyond autonomous security auditing, Convai Innovations develops parameter-efficient, privacy-preserving AI architectures for clinical diagnostics and automated scientific research.
AI4Cardio: On-Device Multimodal Cardiac AI
AI4Cardio is an on-premise clinical diagnostic system developed in collaboration with cardiology specialists. By fine-tuning multimodal vision-language models with LoRA on 12-lead ECG waveforms and patient clinical parameters, AI4Cardio provides rapid, high-accuracy arrhythmia detection and cardiac risk stratification directly on local clinic workstations.
Because diagnostic inference runs 100% locally on workstation GPUs, patient Protected Health Information (PHI) and raw physiological telemetry never leave hospital premises, satisfying HIPAA and DPDP statutory data residency requirements.
Nadhi: Autonomous Desktop AI Co-Scientist
Nadhi Co-Scientist is an on-device multi-agent research co-pilot. It coordinates specialized sub-agents (Planner, Critic, Director, and Synthesizer) to formulate hypotheses, automatically download and index hundreds of open-access scientific papers (arXiv, PMC, PubMed, bioRxiv), execute local Python experiments, and write cited manuscript drafts in .docx and .pdf formats with full revision history.
Powered by our confidence-routing research, every generated paragraph is strictly grounded in verified literature citations, eliminating factual hallucinations.
Published Papers and Scientific Preprints
Confidence-Aware Routing for Hallucination Mitigation
Multi-signal confidence routing for pre-generation hallucination suppression, ensuring every AI output is grounded in verifiable literature.
Read paper on arXiv →Multimodal ECG Interpretation via LoRA Fine-Tuning
Parameter-efficient LoRA fine-tuning for multimodal vision-language models, achieving high-accuracy 12-lead ECG waveform interpretation on edge devices.
Read paper on arXiv →DeepRAG: Custom Embedding Models for RAG from Scratch
Building custom embedding architectures for local document Retrieval-Augmented Generation without third-party vector cloud dependencies.
Read paper on arXiv →Continual Learning Agents via A2C Reinforcement Learning
Personalized agent frameworks powered by Advantage Actor-Critic (A2C) reinforcement learning for continuous on-device adaptation.
Read paper on arXiv →