BioFord AgentEmbodied IntelligenceAutonomous Research Platform
Let AI act as scientists, experimenters, and analysts. BioFord integrates multiple agents—including literature retrieval, experiment design, experiment orchestration, and data analysis—to connect the full life-science workflow, boosting research efficiency by multiples to orders of magnitude.
Fragmented research workflows with severe disconnects
In traditional research, literature review, experiment design, execution, and data analysis remain siloed—creating natural barriers that drag down efficiency.
Gaps lead to: difficult cross-stage collaboration • scattered information and data • limited room to improve overall research efficiency
80%+ time spent
on repetitive and administrative work
Scientists spend enormous time on manual operations, literature searches, and data format conversions—rather than focusing on core hypothesis development and validation.
Severe data silos
information scattered everywhere
Paper records, spreadsheets, and standalone instrument data use incompatible formats, making cross-stage collaboration extremely difficult and causing valuable research data to be lost.
Difficult human-machine collaboration
low utilization of automated equipment
Expensive lab equipment often sits idle due to the lack of intelligent orchestration and scheduling—averaging only around 30% utilization.
BioFord Multi-Agent Collaborative System
With a foundation model as the core brain, five agents work together to close the loop from literature to design, execution, and analysis.
Foundation Model Core Brain
System Orchestrator
Built on biomedical and scientific large language models with deep life-science domain expertise.
- Cross-disciplinary scientific knowledge and logical reasoning
- Multi-agent task decomposition and dispatch
- Long-context memory and iterative learning from experimental feedback
From Digital Hypothesis to Physical Verification
Click the tabs below to learn how each agent empowers your biopharma R&D workflow.
Literature Retrieval Agent
Literature Agent
“Extract massive literature in seconds and converge research directions automatically”
AI Solutions & Efficiency
- Automatically aggregate the latest research in biopharma, bioinformatics, and related fields worldwide
- Intelligently extract key experimental routes, protocols, material ratios, and critical parameters
- Generate multi-dimensional research summaries and identify limitations and breakthrough opportunities
Core Technical Capabilities
- Deep integration with Semantic Scholar, CORE, and other authoritative global academic databases
- Cross-disciplinary semantic search, academic lineage mapping, and research interpretation
- Intelligent research-direction convergence analysis with automatic extraction of high-frequency keywords and hotspots
Revolutionary efficiency gains
Compared with traditional manual workflows
Research direction convergence and literature review time reduced by over 95%
Flexible deployment, secure and compliant
Meet the differentiated needs of research institutions and pharmaceutical enterprises with highly available cloud and on-premise private deployment.
Cloud Version
Cloud-hosted SaaS Version
Ideal for lightweight research teams and academic institutions. Instant access without maintaining complex local compute environments.
- Instant provisioning with multi-user remote real-time collaboration
- Continuous seamless model algorithm updates and iteration
- Highly available cloud compute resources with elastic scaling
On-Premise Private Deployment
On-Premise Private Deployment
For pharmaceutical companies and national key laboratories with strict requirements for data privacy, security compliance, and customization.
- Absolute data security with fully closed intranet operation
- Support for local private databases and deep customization of bioinformatics pipelines
- Compliant with national and industry data privacy standards
Research in Action: Notable Outcomes
Ai4SLAB: AI-Assisted Directed Evolution of Glucose Isomerase
For customized development of next-generation glucose isomerase formulations (requiring better heat resistance and broader pH range), BioFord adopted a performance-first, reverse-design approach for full closed-loop optimization:
PredAgent: Assists mutation design and performance prediction to pinpoint advantageous mutants
ProAgent & OperAgent: Intelligently generate protocols and automatically drive robotic synthesis and characterization
ComAgent: Assists intelligent data analysis for rapid entry into the next optimization iteration
Disrupting traditional bioengineering
Compressed directed evolution from 18 weeks to 5 weeks, expanded throughput 5x, and reduced false positive/negative and cross-contamination rates to extremely low levels—marking a new era of fully automated agent-driven life science research.