context connected
Make Benchling + AWS Data Gen AI-ready
Close the manual gap between computational data in AWS and experimental context in Benchling. Turn days into minutes - and prove meaningful AI impact.
INFINITE POTENTIAL: THE DATA REVOLUTION
Today, however, the industry is changing. AI and Machine Learning are creating new opportunities to answer much broader questions than lab data was originally intended for. In fact, they require this data to train models that will rise above the pack. So leveraging data beyond its original scope is no longer just a nice to have - To be competitive in biotech, it’s increasingly an imperative.
A PARADIGM SHIFT
Biotechs of the past treated data as a resource that they used once and then threw away. They were effectively burning it like oil. The successful biotechs of the future will treat data as an asset that continues to compound value the longer they keep it. But like any asset, this can only happen if they invest in maintaining it.
DATA: REIMAGINED
Data bundled with metadata is doubly as valuable, allowing teams to trust, find and reuse data to create multiple successful outcomes. With metadata, organizations can make better, faster decisions, and rely on their data assets to lead the way.
If you can't find the data, you can't reproduce the analysis.
See how Quilt connects Benchling to AWS-native scientific data
The solution
From Days to Minutes
Your Benchling workflows are solid. Your AWS data lake is massive. But the gap between them is blocking GenAI progress.
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Pipeline Automation
Replace manual exports and brittle scripts with automated pipelines that move Benchling↔AWS data reliably and repeatably.
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Contextual Analysis
Turn siloed AWS objects into Benchling-contextualized, queryable datasets your teams can find and use fast.
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Proven Lineage
Close gaps in lineage with end-to-end traceability that keeps results auditable from sample to analysis.
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AI Readiness
Unblock AI pilots with model-ready, governed data that’s secure, permissioned, and actually usable for GenAI.
Unfortunately,
Data without context is not reusable
LINKED DATA IS REUSABLE DATA
The final step in moving from data as an expendable resource to data as a reusable asset is to begin linking data to other datasets and resources that will allow teams to recreate and understand the original context, and understand how the data can be leveraged in new ways.
Quilt is an Advanced AWS Technology Partner
Quilt Data is an AWS Advanced Technology Partner. Quilt brings seamless collaboration to Amazon S3 by connecting people, pipelines, and machines using visual, verifiable, versioned data packages. Amazon Web Services provides secure, cost-effective, and scalable big data services that can help you build a Data Lake to collect, store, and analyze massive volumes of heterogeneous data.lets get started
stop wrangling data. start discovering.
Get a personalized demo
See Quilt in Action
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NextFlow Summit 2025
Live demo featuring Benchling + AWS with MCP, Sail BioMedicines runs cross-system analysis in minutes, and reviews Quilt + MCP
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CEO BLOG POST
Bridging Benchling and AWS: How GenAI and MCP Accelerate Scientific Workflows by Kevin Moore, CEO Quilt
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case study
Entact Bio builds a strong data foundation, reducing lookup time by 90%
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Benchling Demo
Closing the loop on RNA-SEQ: See live demo of data flow between Benchling and AWS
COMMON QUESTIONS
Primarily S3-backed scientific datasets—the stuff that’s too large or too unstructured to live comfortably inside an ELN/LIMS. That typically includes omics (FASTQ/BAM/CRAM/VCF, count matrices), imaging (microscopy and high-content outputs), instrument outputs, analysis results, and ML-ready feature sets—at 10TB+ scale.
Benchling stays the system of record for experimental work; Quilt adds the missing bridge to AWS-scale data. In practice, Quilt attaches AWS datasets to Benchling entities (projects/samples/assays/runs) so scientists keep using Benchling normally, while large files remain in AWS but become discoverable and usable in Benchling context.
Quilt is typically deployed to align with AWS-native security controls (identity, encryption, network boundaries) while adding a layer of data access governance and traceability across datasets and derived outputs. The goal is: who accessed what, when, and why, plus lineage from source → transformation → result, so sharing is controlled, and audits don’t devolve into spreadsheet archaeology.
It means the data is not just stored—it’s contextualized, queryable, permissioned, and traceable, so it can be reliably used in AI workflows. Concretely: Benchling context + rich metadata + consistent identifiers + lineage + governed access, so you can power RAG/deep research, automate cross-assay questions, and avoid “AI pilots” that stall because no one trusts the inputs.
A common pattern is a fast initial integration to prove value on one or two high-impact workflows, then expansion. The exact timeline depends on security approvals, data scope, and how standardized your metadata is—but the intent is to show meaningful value quickly (not a months-long platform migration).
Typically, no—AWS remains the home for large datasets. The focus is on connecting, organizing, and governing what’s already in S3 (and making it usable in Benchling context), not copying everything into a new system.
Usually, it’s R&D IT/Platform Engineering as the technical owner (security, AWS alignment), with comp bio/data engineering as the workflow owner (schemas, pipelines, “what good looks like”). Bench scientists and scientific ops are key stakeholders for validation, adoption, and ensuring the Benchling experience stays clean.
Anthropic (Claude) is the GenAI layer that can sit on top once the data bridge exists—but the architecture can be provider-agnostic (Claude, other LLMs, internal models). Quilt’s role is to make the underlying Benchling + AWS data accessible and governable enough that GenAI outputs are credible, reproducible, and secure.
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