Platform

One system, from raw data to defensible decision.

Most teams stitch this together from a pipeline runner, a plotting library, an ELN and a slide deck. Pluto is the platform where translational science lives.

Book a demo See the frameworks

01ProgramsStage, health, activities, evidenceJump to section ↓
- Stage, owner, modality and therapeutic area on every program
- Health tracked as on track, at risk or off track
- Evidence tab and immutable change history behind each decision
FeedsWhere decisions are made and recorded, against the framework.
02AI HarnessBounded skills, persistent context, attributionJump to section ↓
- Runs the same validated analyses your scientists use
- Keeps program context between sessions and people
- Links every result to its figure, table or source
FeedsReads results from Analysis and writes scored, attributed evidence up to Programs.
03AnalysisPipelines, no-code analyses, resultsJump to section ↓
- No-code analyses and publication-ready displays
- Pinned pipeline versions and recorded parameters
- The same results through the UI, API and SDK
FeedsTurns raw data into results your team and the harness can use.
04DataManaged compute and storage, in your infrastructureJump to section ↓
- Runs in your cloud, on managed compute and storage
- Connects to sequencers, ELNs and the storage you already use
- Your data never leaves your infrastructure
FeedsThe foundation: everything above it is built on data that stays in your environment.

Each layer is only useful because of the one beneath it. That order is the reason agents can reason over this at all. Hover a layer to see what it holds; click to jump to it.

Programs

The program is the unit of work.

Every experiment, dataset, analysis and decision is organized against a program.

Table, list and kanban views, grouped by stage
Health tracked as on track, at risk or off track
Activity map linking experiments, targets and literature
Gantt timelines, evidence tab and immutable change history

AI Harness

Agents that call the same tools your scientists do.

Before a program review, your scientists spend days pulling evidence together by hand. Pluto's agents take on that legwork using the same validated analyses your team already runs, so the results match what a scientist would get and every one can be checked.

Reproducible results: agents run your validated analyses, not a model's best guess
Reviewable in minutes: each finding links to the figure, table or source behind it
No re-explaining: program context carries over between people and runs
Works with the AI model your company has already approved, including self-hosted ones

call differential_expression(treated, vehicle)call pathway_enrichment(sig=…)score Multi-omic biomarker consistency · strongflag conflict kept · Table 1write evidence → STING program

No-code analysis

Analysis without the queue.

No-code bioinformatics analyses with publication-ready figures. No R, no Python, no waiting on the bioinformatics queue.

Analyses configured from the UI, and new ones added as programs need them
Displays styled for publication
Every figure links back to the data and parameters behind it
Computational teams get the same results through the API and SDK

Why it holds together

Every output lands in the framework.

A pipeline run, a figure, a spatial view, a shared report — each one is evidence against one of 42 criteria. That is what turns a folder of results into a readout of program readiness.

Explore the framework

Biology & Disease Discovery8

Target & Therapeutic Actionability8

Model & Disease Alignment14

Mechanistic & Functional Confidence12

Data & ecosystem

Connected to the stack your scientists already use.

Pluto runs on managed compute and storage inside your own cloud, so data never leaves your infrastructure. It connects to your identity provider, ELN, sequencing vendors and public references — and a documented API and SDK give computational teams the same surface in code.