Primer design. PCR validation. Protein docking. CRISPR analysis. Clinical reasoning. VigyanLLM automates the full biological research pipeline — on your hardware, with no external API, no data leaving your institution, and a dedicated verification agent checking every output before it reaches you.
Live counts of validated primer designs processed through VigyanLLM's 22-step pipeline.
Purpose-built, domain-trained, multi-agent system running entirely on your infrastructure.
22-step validated primer and probe design with thermodynamic profiling, BLAST specificity checking, SNP screening, repeat masking, and multiplex scoring — producing lab-ready results in under 30 seconds.
Primer3 · SantaLucia 1998 · BLAST · IDT/Twist Export320,000+ purpose-built biomedical records across 46 specialized training batches. Proprietary VigyanInferenceEngine with zero external API dependency — every inference processed locally or on dedicated GPU.
VigyanInferenceEngine · GGUF · 85% ConfidenceVigyanLLM is incubated at IIT Delhi's FITT (Foundation for Innovation and Technology Transfer), progressing through structured milestones toward global deployment.
Core AI pipeline validated on 46 domain-specific training batches with proprietary inference engine and multi-agent verification.
Deployment with institutional research partners for real-world validation across genomic, clinical, and pharmaceutical workflows.
Multi-tenant enterprise platform with role-based access, compliance certifications, and global researcher onboarding.
VigyanLLM supports primer and assay design across the full spectrum of molecular biology applications — from basic PCR and qPCR gene expression analysis to advanced NGS library preparation, CRISPR-Cas9 genotyping, clinical diagnostics, and pharmaceutical drug discovery workflows.
Gene expression, miRNA, viral load
Amplicon panels, library prep, targeted seq
Allele-specific PCR, ARMS, TaqMan assays
Pathogen detection, infectious disease, oncology
Drug discovery, biomarker validation, QC
DNA profiling, GMO testing, breeding
A full platform tour plus three autonomous pipelines. Each one shows the product working inside real research workflows.
Complete VigyanLLM web features walkthrough
Watch the full website and product experience in one walkthrough: the core interface, research workflows, project creation, outputs, and the practical features researchers use across the platform.
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Design lab-ready primer pairs in under 30 seconds. SantaLucia 1998 thermodynamics, NCBI/Ensembl fetch, hairpin and dimer checks — free to try.
Today's molecular biology researcher runs six or more disconnected software tools in a single experiment: a primer design tool that doesn't talk to the PCR optimizer, a docking platform that can't read the structure predictor's output, and a clinical database with no connection to the genomic pipeline.
Every handoff is a manual reformatting job. Every tool charges separately, and every one of them sends your data to a cloud server you don't control.
This is not a productivity problem. It is a structural failure of the biotech software industry — and it costs researchers 60%–70% of their time.
Every major biotech AI solves one problem. AlphaFold folds proteins. Schrödinger runs docking. Benchling logs lab data. The researcher manually stitches outputs together — introducing error and wasting hours at every handoff.
Sending patient genomic sequences, clinical trial data, or proprietary compound structures to third-party APIs is a legal and ethical exposure. HIPAA, GDPR, and institutional data governance policies are incompatible with most current biotech AI platforms.
Schrödinger's platform costs tens of thousands of dollars annually. The majority of research labs worldwide — and virtually every academic lab in India — cannot access state-of-the-art computational biology tools.
From gene sequence to validated experimental protocol, a researcher touches at minimum six systems. No platform today automates primer design, structure prediction, molecular docking, and clinical reasoning under one sovereign roof.
"ChatGPT for biotech" is not a company. VigyanLLM is a purpose-built, domain-trained, multi-agent system with a proprietary inference engine, 46 batches of curated biomedical training data, and a dedicated verification agent that no off-the-shelf AI product has.
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 T4 GPU. Not scraped. Not generic.
VigyanLLM runs on the VigyanInferenceEngine — a native GGUF inference engine that replaced all external proxy processes. Every inference request is processed locally or on a dedicated T4 GPU. No OpenAI. No Anthropic. No Cohere. Your data stays on-premises.
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 (e.g., −8.5 kcal/mol), and flagging sgRNA instability.
Parses research intent, decomposes complex biological queries, and routes sub-tasks. Synthesizes outputs into a coherent final report.
The specialist. Handles biological knowledge retrieval, sequence analysis, protein prediction, and pathway mapping.
The validation layer. Cross-checks results against off-target databases and physics-based benchmarks before release.
Predict accurate 3D protein conformations and identify structural anomalies for drug target discovery.
AlphaFold · RosettaFold · ESMFoldGPU-accelerated docking with realistic binding energy calculations and interaction surface mapping.
Vina-GPU · −8.5 kcal/mol benchmarksRNA-Seq analysis and pathway integration for complete systems biology understanding.
DESeq2 · edgeR · KEGG · ReactomeAutomated design with thermodynamics validation and off-target screening for laboratory protocols.
Off-target screening · Thermo validationsgRNA-DNA Heteroduplex instability analysis and PAM variation mapping for precise editing.
HNH Domain · sgRNA Instability · PAM MappingDifferential diagnosis and symptom mapping trained on 50,000 dedicated clinical reasoning records.
Trained on 50k Clinical Records| Capability | AlphaFold | Schrödinger | Benchling | Recursion | VigyanLLM |
|---|---|---|---|---|---|
| No External API Required | ✗ | ✗ | ✗ | ✗ | ✓ |
| Full Pipeline (Single System) | ✗ | ✗ | ✗ | ✗ | ✓ |
| Primer & PCR Automation | ✗ | ✗ | ◐ | ✗ | ✓ |
| Clinical Reasoning | ✗ | ✗ | ✗ | ✗ | ✓ |
| Multi-Agent Orchestration | ✗ | ✗ | ✗ | ◐ | ✓ |
| On-Premise Deployment | ✗ | ◐ | ✗ | ✗ | ✓ |
AlphaFold serves protein research. Schrödinger serves drug discovery. Recursion serves pharmaceutical pipelines. VigyanLLM is built for every molecular biology researcher — academic labs, biotech startups, clinical institutions, and agricultural biotech — making advanced computational biology accessible without enterprise pricing or cloud dependency.
VigyanLLM Primer keeps entry pricing simple while giving labs higher monthly design capacity, audit-ready reports, and a 22-step validation workflow that goes beyond basic primer picking.
46 specialized training batches. 320,000+ medical and genomic records ingested. Three-agent pipeline (Core, SubBrain, ChinhAI) producing research-grade outputs. Sovereign Cold Core architecture live.
Replacing simulated layers with TensorFlow.js. Adding advanced search (TF-IDF, BM25) and semantic embeddings directly in the interface for offline-capable inference.
Institutional deployments with role-based access, collaborative workspaces, and enterprise API controls for universities and biotech startups.
Full implementation of HIPAA, GDPR, and FDA 21 CFR Part 11 architectures. Hardened audit trails for hospital deployments.
VigyanLLM runs entirely on your hardware. No cloud dependency. No third-party API calls. Sovereign by design.
Deploy the full VigyanLLM stack on any Linux or macOS machine with a single Docker Compose command. No Kubernetes required. No cloud subscription. Your genomic data never touches an external network.
ChinhAI, our dedicated verification agent, cross-checks every output against physics-based benchmarks and off-target databases before release. Three sequential agents ensure no single model has the final word.
For research collaborations, beta access, or general inquiries — reach out to our team.
We are offering 60-day free access to PhD researchers and lab PIs for real-world validation. In return: honest feedback and early access to every feature we ship.