Auto Researcher
中文 Sign in Sign up free
Menu
Pricing How it works Integrity FAQ Enterprise
Sign in 中文
Auto ResearcherEnterprise

Turn the parts of your R&D that run in software
into an always-on AI research system

Built for biotech and pharma, cosmetics, aerospace, robotics and embodied AI, and any other industry with an R&D team. It works when the key steps (experiments, data generation, simulation, training, validation and analysis) can run on computers, in a simulator or on a programmable lab platform.

Tell us what you need
Example: an enterprise R&D loop

Researchers set the goals and make the key calls; the system does the repetitive work

  1. 01

    Research goal

    Researchers set goals and constraints

  2. 02

    Literature & knowledge search

    Internal knowledge base + external literature

  3. 03

    Hypothesis generation

    Candidate hypotheses and priorities

  4. 04

    Experiment design

    Parameters, controls, statistics

  5. 05

    Data / simulation generation

    Connects to simulators and lab platforms

  6. 06

    Training or computation

    Company compute cluster

  7. 07

    Result validation

    Reproducibility and statistical checks

  8. 08

    Failure analysis

    Finding why runs failed

  9. 09

    New hypotheses / experiments

    Multiple automated iterations

    Goal not met: back to 04 Experiment design for another round

  10. 10

    Conclusions and outputs

    Internal reports, patents, papers

Integration

How it connects to your systems

Personal and Enterprise run on the same research engine; only the data, tools and permissions they connect to differ.

  • Data
    Internal databases, lab records, documents and knowledge bases
  • Compute
    Private GPU clusters, or a pool of AI-Scientist-v2-compatible compute nodes
  • Simulation
    Robot-arm and embodied-AI simulators, molecular simulation, CFD and more
  • Approvals and permissions
    Decision points can go through your approval workflow; high-risk research always gets human review
  • Deployment
    Private Docker deployment; apart from model API calls, data stays inside your network
  • Billing
    Deployment and support are quoted separately; models and compute can run on your own accounts, and every study has a budget cap
Examplerobotics

Generate robot-arm data in a simulator → train a policy → run tasks → analyze failures → adjust the experiment automatically, repeating until the target success rate is reached.

Tell us what you need

Describe your R&D work and the tools you use. We'll reply to your work email and help you pick the first step worth automating.