Uncovers disease mechanisms, biological pathways, and target validation evidence.
Built differently because
scientists deserve it
EMET isn’t a wrapper around a general-purpose LLM. It's a decade of proprietary data, scientific reasoning, domain-specific models, software packages, skills, tools, code execution, and biopharma-specific architecture — assembled to think and act like a team of PhD scientists.
From complex research question to trusted novel insight in minutes
A scientist asks a complex biological question. EMET doesn’t search — it reasons.
Here’s what happens under the hood.
- 01Input
Research question
- 02Planning
Intent recognition, workflow initiation
- 03Execution
Skills, tools, models, code execution
- 04Synthesis
Knowledge graph verification and grounding
- 05Output
Verified and cited insight, reports, visualization
Five layers.One unified environment.
EMET unifies the scientific tech stack — data, models, software packages, skills, tools, and reasoning — into a single environment. Each layer is purpose-built for the complexity of biopharma R&D.
Interfaces
EMET
Programmatic Access
Lab in the Loop
Scientific Agents
BenchSci Agents
Custom Agents/Workflows per User
Ground Truth & Reasoning
Science Led Quality Assurance, Evaluations and Optimizations
Scientific Skills, Tools, Capabilities, Workflows, Coding, Models, (large and small) and Reasoning
Closed Access Data
BenchSci Proprietary Data
Internal Org Data
Enterprise Security & Governance
The data took a decadeto build. It shows.
Eight years of publisher partnerships, 60 PhD scientists curating every data point, and a knowledge graph that no competitor can replicate. This is the ground truth of disease biology.
- All scientific publications
- Preprints
- Patents
- Omics
- Clinical trials
- Ontologies and facts
- Reagents and methods
- Internal biopharma data
2.2B
Relationship edges
60
PhD Scientists
38M
Scientific publications
16M
Closed-access papers only found in EMET
100M
Ontological entities and relationships
80+
Omics databases
85M
Reagent and model system data
Scaling AI without losing scientific accuracy
Generative AI at scale is not a modeling problem or an orchestration problem. It’s an evaluation problem. We solved it with a neuro-symbolic approach that no general-purpose platform can match.
Specialized workflows.Unified reasoning.
EMET’s agentic research architecture deployed hundreds of purpose-built workflows for every dimension of pre-clinical R&D.
Accelerates hit identification, SAR analysis, and compound optimization.
Guides antibody design, epitope mapping, and biologics developability profiling.
Interprets analytical assay readouts to verify molecular structure and purity.
Integrates computational models, bioinformatics pipelines, and internal data.
Models target engagement and dose-response kinetics to predict efficacy.
Identifies biomarker candidates and companion diagnostics to derisk translation.
Predicts ADME and pharmacokinetic properties to optimize drug-like behavior.
Identifies off-target risks and cross-species toxicities before in vivo studies.
Tracks cross-program milestones and evidence to support pipeline decisions.
Assembles preclinical evidence packages and submission-ready documentation.
Uncovers disease mechanisms, biological pathways, and target validation evidence.
Accelerates hit identification, SAR analysis, and compound optimization.
Guides antibody design, epitope mapping, and biologics developability profiling.
Interprets analytical assay readouts to verify molecular structure and purity.
Integrates computational models, bioinformatics pipelines, and internal data.
Models target engagement and dose-response kinetics to predict efficacy.
Identifies biomarker candidates and companion diagnostics to derisk translation.
Predicts ADME and pharmacokinetic properties to optimize drug-like behavior.
Identifies off-target risks and cross-species toxicities before in vivo studies.
Tracks cross-program milestones and evidence to support pipeline decisions.
Assembles preclinical evidence packages and submission-ready documentation.
Uncovers disease mechanisms, biological pathways, and target validation evidence.
Accelerates hit identification, SAR analysis, and compound optimization.
Guides antibody design, epitope mapping, and biologics developability profiling.
Interprets analytical assay readouts to verify molecular structure and purity.
Integrates computational models, bioinformatics pipelines, and internal data.
Models target engagement and dose-response kinetics to predict efficacy.
Identifies biomarker candidates and companion diagnostics to derisk translation.
Predicts ADME and pharmacokinetic properties to optimize drug-like behavior.
Identifies off-target risks and cross-species toxicities before in vivo studies.
Tracks cross-program milestones and evidence to support pipeline decisions.
Assembles preclinical evidence packages and submission-ready documentation.
Uncovers disease mechanisms, biological pathways, and target validation evidence.
Accelerates hit identification, SAR analysis, and compound optimization.
Guides antibody design, epitope mapping, and biologics developability profiling.
Interprets analytical assay readouts to verify molecular structure and purity.
Integrates computational models, bioinformatics pipelines, and internal data.
Models target engagement and dose-response kinetics to predict efficacy.
Identifies biomarker candidates and companion diagnostics to derisk translation.
Predicts ADME and pharmacokinetic properties to optimize drug-like behavior.
Identifies off-target risks and cross-species toxicities before in vivo studies.
Tracks cross-program milestones and evidence to support pipeline decisions.
Assembles preclinical evidence packages and submission-ready documentation.
The numbers againstfrontier models
Zero-shot queries to commercial LLMs, without EMET’s proprietary prompting and knowledge layer. This is what a decade of scientific infrastructure does to raw model performance.
Top Frontier Model
~75
EMET with BEKG
93.0
Disease Biology
Genetic Evidence
Target Profile
Safety Signals
Program Rationale
Biomarkers
Toxicology
Safety Pharmacology
ADME / PK
Translation
Q01
Q02
Q03
Q04
Q05
Q06
Q07
Q08
Q09
Q10
Q11
Q12
Q13
Q14
Q15
Q16
Q17
Q18
Q19
Q20
0
20
40
60
80
100
Built for regulated biopharmafrom day one.
Enterprise readiness isn’t a feature we added. It’s how EMET was designed — because the scientists who need it most work in environments that can’t compromise on security, compliance, or reproducibility.
Security & Compliance
Designed for sensitive and regulated environments. Enterprise security and governance built for biopharma data — not retrofitted from a general-purpose platform.
Internal Data Integration
Proprietary data, dark data, and internal datasets integrated directly into EMET’s reasoning layer. Your data doesn’t sit alongside the platform — it powers it.
Customizable Workflows
Pre-defined research methodologies encoding your team’s logic, tool sequences, and standards — standardizing repeatable objectives across the organization.
Forward deployed scientists
Dedicated PhD scientists embedded in your deployment. They understand your biology, your programs, and your org — not just the software.
Self-Driving Lab Connectivity
EMET connects to automated lab infrastructure — closing the loop between computational hypothesis and physical experiment. Lab in the loop, not lab as an afterthought.
ROI Engineering
A dedicated value engineering team tracks, measures, and reports impact continuously — so the business case for EMET gets stronger over time, not weaker.
Drug programs fail when biology is misunderstood. EMET exists to close that gap — before it closes your pipeline.
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