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Speed up data analysis and decision-making across your lab and ML teams.

Lab Scientists

Tired of manually enriching data and writing code?

Manually parsing data, adding contex, and writing code for analysis is frustrating.

Automate data ingestion. Expedite scientific breakthroughs.

Upload data in any format, directly from your lab machines, LIMS, or object stores.
Verify metadata and add tags for improved searchability.
Use pre-built transformations and visualizations to make analysis and interpretation easier.
Merge datasets or compare them side-by-side on the same platform.

Data Science/ML Teams

Don’t waste days chasing the data you need.

Scattered data and lack of context around datasets make machine learning error-prone.

Prevent bad data from throwing off your ML models.

Break down data silos. Access experimental data with relevant attributes and lineage in one place.
Train better models with more reliable data. Refer to scientist annotations for outliers, technical artifacts, or experimental errors to create robust training data.
Focus on data science, not on building or maintaining web apps.

Software/Informatics Teams

Replace ad hoc analysis tools with our powerful APIs.

Data science slows down when you’re trying to keep up with increasingly complex datasets and building on-the-go tooling.

Expand your informatics team’s bandwidth, not your headcount.

Define schemas in code.
Get easily reproducible analyses with clear data lineage.
Build a centralized data catalog that everyone in the lab can use to share and find data.

Turn your raw data to useful insights in minutes.

Get started with analysis-ready datasets

Eliminate tedious data entry and formatting tasks through AI-powered raw data ingestion.

Run statistical tests that transform your data effortlessly

Leverage pre-built bio-specific statistical transformations without writing a single line of code.

Create publication-ready visualizations

Turn your data into stunning, easy-to-understand visualizations at any stage.

Enable scientist annotations

Add relevant context to your data and analyses, track lineage, and help all your teams make better use of experimental data.

Built to function effortlessly within your existing setup.

Use Sphinx alongside your LIMS, databases, and object storage for a unified data analysis experience.

Why do modern biotechs love Sphinx for data management and analysis?

Scientists love the AI-driven data entry and analysis

We use machine learning to automatically understand your data structure. The platform is built to save you hours of manual work on parsing and transforming millions of data points.

Informatics teams love the powerful APIs and code-first approach

Our code-first approach enables you to work with any type of data and experimental design without relying on inflexible tooling or building your own.

ML teams love the detailed data lineage

Get data scattered across desktops and legacy systems into a centralized cloud-based catalog. Verify data, track lineage, and gather context for your models.

Sphinx helps your teams use all available data for better collaboration and stronger decision-making.

Sphinx is the #1 tool for Software-Defined Biology.

Sphinx’s platform has been a game-changer for us. Our scientists are able to go from results to decisions within minutes, accelerating our experimental timelines.

Bettie Osuna

Head of Platform at Tacit Therapeutics

Sphinx has been an important part of integrating our wet lab and computational teams. Their platform has streamlined our workflows, eliminated bottlenecks and improved productivity for both scientists and data teams.

Erle Holgersen

Lead Data Scientist at Deep Genomics

Additional Resources

April 1, 2025

Rational Drowning

Most biotechs delay improving data practices until it's too late—leading to lost data, redundant experiments, and costly mistakes. Sphinx makes it easy to implement good data hygiene from day one, combining flexibility for early-stage iteration with automation and consistency as you scale.

March 27, 2025

Recency Bias

Biotech teams often analyze datasets individually, missing deeper insights by neglecting older data due to integration complexity. Automated tools now allow bench scientists to seamlessly combine diverse datasets into structured tables without manual coding, unlocking comprehensive analysis and better-informed decisions.

Bring your lab, informatics, and ML teams together on one platform.