SOLUTION

Autonomous AI for Molecular Biology

Purpose-built, domain-trained, multi-agent system running entirely on your infrastructure. VigyanLLM is not a wrapper around existing APIs — it is a sovereign biomedical AI platform with a proprietary inference engine, 46 batches of curated training data, and a dedicated verification agent.

📊 320,000+ Purpose-Built Records

46 specialized training batches — each targeting a precise biological subdomain. Batch 19: 50,000 clinical reasoning records. Batch 20: 50,000 genomic design records. Batch 46: 110,025 CRISPR structural anomaly records processed on dedicated AWS GPU infrastructure. Not scraped. Not generic.

Proprietary JSONL pipeline · 85% confidence

🔐 No External API. Ever.

VigyanLLM runs on the VigyanInferenceEngine — a native GGUF inference engine that replaced all external proxy processes. Every inference request is processed on secure AWS infrastructure or dedicated on-premises hardware. No OpenAI. No Anthropic. No Cohere. Your data stays sovereign.

VigyanInferenceEngine · Bare-metal · Sovereign

✅ ChinhAI: The Validation Layer

A single LLM cannot check its own hallucinations. VigyanLLM's ChinhAI agent is a dedicated verification layer — cross-checking primer off-target binding, validating molecular docking energetics, and flagging sgRNA instability before any output reaches you.

3-agent architecture · Sequential validation

Three Agents. One Research Pipeline.

01

Core — Orchestration Layer

Parses research intent, decomposes complex biological queries, and routes sub-tasks. Synthesizes outputs into a coherent final report.

02

SubBrain — Domain Reasoning

The specialist. Handles biological knowledge retrieval, sequence analysis, protein prediction, and pathway mapping.

03

ChinhAI — Verification Gate

The validation layer. Cross-checks results against off-target databases and physics-based benchmarks before release. No single model has the final word.

The Hallucination Defense: In biomedical AI, a wrong answer is not an inconvenience — it corrupts an experiment, wastes weeks of lab work, or in a clinical context, causes harm. VigyanLLM's architecture is built around this constraint. Every output passes through Core's orchestration, SubBrain's domain reasoning, and ChinhAI's validation before the researcher sees it.