Tetra OS

Your instruments generate data. Too much of it never gets used.

Unify every instrument in your lab with a single, open data foundation. We automatically standardize and contextualize your data, creating an engine for scientific intelligence that compounds year over year.

Layered data foundation stacking instrument data into a single platform
The Problem

Most labs have already solved connectivity. They did it in patches — CSV exports, manual uploads, point-to-point scripts. The problem is what those patches leave behind.

The Scientific Data Foundry includes pre-built, validated connectors for instruments across every stage of the scientific workflow from discovery through QC. Each connector does more than move a file. It parses raw output into a standardized Intermediate Data Schema (IDS), enriches it with experiment context, and publishes structured, AI-ready data to a governed data lake.

No custom scripts. No fragile pipelines. No data that only one application can read.

The patches break

Every script is tied to a specific instrument and a specific software version. When the vendor pushes an update, the connection fails, and someone in the lab has to notice and rebuild it.

The data loses its context

A file lands somewhere, but stripped of the experiment it came from — no sample ID, no instrument, no researcher, no run conditions. It's data nobody can trust six months later.

It compounds

Every instrument you add and every year you don't fix this makes the pile of unusable data bigger. The cost isn't flat. It grows.

That's not a data management problem.
It's a scientific intelligence problem.
What TetraScience Does

Connectors for every stage of the scientific workflow

The Scientific Data Foundry includes pre-built, validated connectors for instruments across discovery, development, and QC. Each connector pulls raw output, enriches it with experiment context, and converts it into a standardized Intermediate Data Schema (IDS) — structured, AI-ready data published to a governed data lake.

No custom scripts. No fragile pipelines. No data that only one application can read.

Discovery & Research

Flow cytometry, plate readers, microscopy, liquid handling, spectrophotometry

Development / CMC

Cell counters, bioreactors, FPLC, HPLC, ELN/LIMS integration

Quality

pH meters, capillary electrophoresis, qPCR, and chromatography data systems including Chromeleon, Empower, Unicorn, ChemStation, OpenLab, and LabSolutions

Browse the full connector catalog at developers.tetrascience.com

How It Works

From raw instrument output to AI-ready data — automatically

Step 1
Connect

Containerized connectors deploy in your environment — cloud, on-prem, or hybrid. They monitor instrument outputs, shared folders, and file services, pulling data automatically without interrupting lab workflows.

Step 2
Parse and standardize

Each raw file is parsed and converted into a structured IDS file — a vendor-agnostic, normalized representation that makes scientific data comparable across instruments, sites, and time.

Step 3
Enrich with context

Experiment metadata, sample IDs, instrument type, site, and researcher information are attached at ingestion, not reconstructed later. The data arrives in the Foundry already understood.

Step 4
Publish to downstream systems

Standardized data flows automatically to ELN and LIMS systems, analytics applications, AI models, and cross-functional dashboards — no manual reformatting or scripting.

Abstract visualization of raw instrument data becoming structured, AI-ready data
For Teams That Need More Control

Build your own connectors. On the same open architecture.

If your instrument isn't in the library — or your workflow needs something custom — the Connector SDK gives your informatics and data engineering teams a framework to build, deploy, and maintain their own integrations, without rebuilding the infrastructure underneath.

Same containerized architecture
Same deployment model
Same AI-ready output format
Why It Matters for AI

AI models don't fail because of algorithms. They fail because of data.

Fragmented, inconsistent data represented as noise
The failure mode

Every AI application in your scientific workflow, from hit selection to process optimization to regulatory documentation, is only as good as the data it learns from. If that data is fragmented, inconsistently formatted, and stripped of context, the model can’t generalize. It learns noise.

The fix

Data that flows through the Foundry is structured the same way every time, from every instrument, at every site. That consistency is what lets scientific AI work at scale — across programs, across years — instead of stalling in a pilot.

Consistently structured data flowing through the Foundry

Make your scientific data work for AI.

Tetra OS turns scattered instrument and lab data into a foundation AI can actually use.

Talk to an expert

By transforming how our scientists access, analyze, and share research data, we're unlocking new levels of productivity and enabling AI-powered insights through a connected, online data environment. Beyond boosting productivity, we're leveraging data and agentic AI to accelerate innovation across our drug discovery engine.

Jim Villa
Global Head of Research Strategy & Operations

Our expanded partnership with TetraScience is delivering measurable value through unified access to instrument and CRO data that powers our automation and analytics at scale. The platform's audit capabilities have streamlined our regulatory preparation processes.

Linus Goerlitz
Regulatory Science Transformation Lead

Embedding AI and digital technologies across the R&D value chain is one of Takeda’s core strategic areas for our future. Our data-driven R&D approach will reduce discovery timelines, enable the identification of targets faster, and help us design better therapeutic candidates.

Nicole Glazer
Head of R&D Data, Digital and Technology

Our collaboration with TetraScience strengthens how we help customers automate data management at scale in the laboratory.

Sean Baumann
VP - Digital and AI, Life Sciences, Diagnostics and Applied

Our collaboration with TetraScience enhances the precision and speed of our quality control processes. By automating manual steps, we're empowering our scientists to focus on innovation that brings essential medicines to women faster and more safely.

Niamh O'Rahilly-Drew
AVP Quality

The capabilities provided by TetraScience enable us to standardize and harmonize data at scale... accelerating the speed and quality of scientific discovery.

Claudio Battilocchio
Digital Automation Lead R&D