Data Integration Platform

Advanced data engineering solution for your Digital Lab

Centralize the silos. Harmonize disparate formats. Spend your time looking for insights, not manually transcribing or agonizingly searching for your data.

How it works

Automatically collect experimental data

Acquire data from any source without manual intervention. Yes, we really mean any source. In the cloud or on premise. Any lab instrument - connected or disconnected. CROs/CDMOs. Data stores, ELNs, and LIMS. Unless it is written on a piece of paper, we can extract the data, and enrich it with metadata in the process.

Software

Native data collectors pull or receive data from popular instruments

File

A directory watcher uploads files from your chosen folders

IoT

Our patented device connects typically disconnected instruments

Centralize all your data in a data lake

Easily find and access all your experimental data in one central cloud repository. Don't spend hours manually adding metadata or searching for files. No need to re-run an experiment because you couldn't find the data. Discover what experiments have already been run across your R&D ecosystem.

  • Raw data, processed data, and custom metadata
  • Versioning + traceability
  • Advanced filters, previews, and batch actions
  • Elastic search + SQL/tabular search
  • Batch + stream data
  • Built on AWS S3

Harmonize and move data using pipelines

Transform your experimental data, regardless of source, into a standard format. Enrich your data by merging with metadata. Push data to ELN/LIMS. Generate reports and perform calculations. Validate and QA results. Use our pre-configured pipelines or configure your own to process your data and move it throughout your R&D ecosystem.

  • Control pipelines using triggers
  • View status within the TetraScience User Interface
  • Built on AWS Lambda and Docker
  • Developer friendly

Prepare your data for analysis and exploration

Understand your data, derive insights from it, build models on it, or simply query it. Our hierarchical schema defines relationships, hierarchies, and requirements across your data. This schema enables knowledge/RDF graphs and semantics, ultimately providing meaningful organization and a strong foundation for data science + AI.

Data sources + targets
Platform architecture
Connectors for data acquisition
Data lake for management
Pipelines for data processing
Data harmonization for action
"After evaluating several vendors, we selected TetraScience because of their innovative technology platform, their instrument vendor agnostic approach, their team, and their vision for a Digital Lab. We felt that they had the most mature and scalable platform in this market.”
- Bill Goode, VP of IT, Informatics, and Operations at Jounce Therapeutics
"We believe that proper and simplified contextualization of data being produced by automation coupled with industry leading data capture and pipelining will bring significant value to our customers. This partnership should enable any HighRes lab automation user to acquire their data with experimental context and serve as the foundation for automated data capture and analysis. These are fundamental elements to building closed loop automation processes.”
- Ira Hoffman, CEO, HighRes Biosolutions
"Working with TetraScience enables us to not only expand our growing base of strategic partners but also help customers save valuable time, enabling them to focus more on scientific discovery.”
- Stephen Gallagher, Ph.D., CEO, Dotmatics

Your data insights can't wait - contact a product expert today.

Request Info
Terms and Conditions