PercepticOS

An AI discovery operating system for scientific research and R&D teams, officially enabling teams and AI to reason together in the same research: AI is responsible for organizing evidence, experts make the final judgment, and the entire decision-making process is traceable.

AI Research Tools#Research AI#Evidence tracing#R&D decisions

Updated: 2026-10-27

What is PercepticOS?

PercepticOS is an AI Operating System for Scientific Discovery launched by Perceptic, which is officially hailed as the world's first AI operating system oriented towards scientific discovery. It integrates complex cross-functional R&D decisions into a single research framework, entrusting AI with the work of evidence collation and leaving the final judgment to experts. Meanwhile, it keeps records of every step of reasoning, preventing knowledge from being confined to a single conversation or an individual's mind.

Who is it for?

Scientific research teams and R&D organizations engaged in complex, cross-functional R&D decision-making.
Scientists and analysts who need to organize multimodal evidence for pharmacological, clinical, or translational research.
Team leaders who want to沉淀 organizational data, expert experience and historical decisions into reusable records.
R&D management departments with clear requirements for security, compliance and access control.

Key features

Decision Decomposition

Breaks down the decisions to be made into a visible and challengeable argument structure, including sections, verifiable assertions, and the dependency relationships between assertions.

Multi-Agent Investigation

AI agents can be deployed for individual claims or entire sections to retrieve literature, access data and run analyses in parallel, with the team able to intervene at any time during the process.

Bidirectional Reasoning

Each claim has an independent operation record. The agent performs deduction from hypotheses and induction from data, with the entire workflow fully traceable.

Evidence Tracing

Every finding can be traced back to specific sources, down to a specific figure in a paper or a data point in in-house experiments, and both supporting and refuting evidence is retained in the records.

Structured Synthesis

The agent outputs conclusions in a structured overview, highlighting uncertainties and data gaps for experts to jointly verify and confirm the current status of evidence.

Flexible Models and Deployment

Officially stated to integrate with existing tools and infrastructure, select the appropriate model for each task, switch at any time without being locked into a single vendor.

How to use PercepticOS

1Pre-sort out the sources of public and internal literature as well as experimental data in advance; the retrieval and synthesis quality of the agent depends on the citeable evidence base.
2Start with a real cross-functional decision, clearly describe the problem and specify public or internal data sources, and then select the model and reasoning depth.
3Let the agent handle literature retrieval and preliminary analysis, and leave uncertain or conflicting evidence for team experts to adjudicate, avoiding direct acceptance of AI conclusions.
4Use assertions and links to sort out evidence dependence; when evidence conflicts is found, require the agent to stop and explain the impact of each option.
5Set the visibility scope of each study and dataset in accordance with the permission strategy, and confirm that access control complies with internal governance requirements.