BenchSci
Free tierThe agentic AI workbench that reasons through disease biology for preclinical R&D
Enterprise·Technical·Powered by Multiple (frontier LLMs + ESM-2, AbLang2, RDKit)·API available
Key strengths
Access to 16M closed-access papers via exclusive publisher partnershipsProprietary knowledge graph with 858M nodes and 2.2B relationship edges95%+ accuracy via neuro-symbolic evaluation, 2–4x better than frontier LLMs100+ proprietary scientific skills covering preclinical R&D workflowsCurated reagent & biology data: 16M antibodies, 22M RNAi entries, 18M CRISPR records
Enterprise pricing
Toronto, Canada
Founded 2015
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Technical Integration & Setup
Architecture Overview
EMET uses a neuro-symbolic orchestration layer that:
- Decomposes natural language research queries into sub-tasks
- Routes each sub-task to one or more of 100+ proprietary scientific skills
- Chains results intelligently across internal and external data sources
- Applies a neuro-symbolic evaluation loop for factual grounding (95%+ accuracy)
Model Stack
EMET orchestrates multiple specialized models:
Frontier LLMs → General reasoning & language understanding
ESM-2 → Protein language modeling
AbLang2 → Antibody sequence modeling
RDKit → Cheminformatics & small molecule analysis
Data Layer
| Asset | Scale |
|---|---|
| Scientific publications | 38M+ (incl. 16M closed-access) |
| Knowledge graph nodes | 858M |
| Relationship edges | 2.2B |
| Ontological nodes | 100M |
| Antibody records | 16M |
| RNAi entries | 22M |
| CRISPR records | 18M |
| Cell lines | 500K |
| Animal models | 700K |
| Patents | 780K |
Enterprise Integration
- Ingest proprietary/dark data (internal reports, spreadsheets, ELN data)
- Connect to self-driving labs via dedicated APIs
- Support for customizable agentic workflows per therapeutic area
- Compliance-ready for regulated biopharma environments
- Bespoke connectors built by BenchSci's scientific team per deployment