Ecosystem

A connected AI ecosystem for biotech R&D

Benchling AI connects instruments, external data, partner models, and enterprise systems through open standards — so insights reach scientists wherever they work.

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Your data, without walls

Benchling embeds AI models into your R&D workflows, so in silico insights live right next to your wet lab data. Stay flexible as AI evolves by using the right tools, without vendor lock-in. Benchling makes it easy to run open source and proprietary models and bring insights back into your workflows.

Enrich Benchling workflows with predictions from in-silico models
Export structured data to feed your own ML pipelines
Switch model providers without rebuilding your infrastructure
Keep your data secure while using the models you want

MCP-native by design

Benchling uses MCP (Model Context Protocol) as a first-class integration layer to connect your AI tools and data systems through standard connectors — no custom APIs required. As an MCP client, Benchling AI can connect to external models, data sources, and partner tools. As an MCP server, Benchling can securely expose structured scientific context — experiments, entities, results, and relationships — to approved AI assistants and chat applications.

Connect partner tools without custom APIs
Let scientists access Benchling context from their external AI interfaces
Maintain governance, permissions, and IP controls

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Best-in-class AI, built into Benchling

From leading model providers to biotech innovators, partners are building with Benchling to bring intelligence directly into experimental workflows.

Run predictions from NVIDIA BioNeMo, Anthropic Claude and OpenAI in your existing workflows
Participate in federated learning through Lilly TuneLab
Skip procurement cycles and infrastructure setup

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Designed for an expanding partner ecosystem

Benchling is building an ecosystem where best-in-class tools don’t live in silos — they operate directly within experimental workflows. Using open standards like MCP, partners can bring their capabilities into Benchling while maintaining ownership of their technology, infrastructure, and IP.

The result: partner tools that are more powerful because they understand experimental context — and scientists who get answers without leaving their work.