More work can proceed.
The aim is to let well-supported steps advance under defined checks and approval rules rather than wait for repeated manual review.
Synartesis Labs / For the next era of discovery
Scientific verification
for the age of AI.
AI can produce sophisticated research and still make basic mistakes. We are building verification infrastructure to distinguish supported results from plausible errors, reduce repeated checking and help scientific discovery move faster.
More reliable results. More automation. Faster discovery.
Our vision
Reliability unlocks more discovery.
Faster generation alone is not enough if people must recheck every result. Our goal is to make more research dependable enough to automate: checking evidence as work unfolds so teams can explore more ideas without scaling manual review at the same rate.
Less avoidable rework.
Catch unsupported assumptions before they propagate into further analyses, experiments and conclusions.
Human attention where it matters.
Direct researchers toward unresolved evidence, consequential decisions and genuinely new directions.
Impressive output is not
dependable progress.
A convincing result can hide a missing control, an unsupported inference or a faulty calculation. When teams cannot tell which outputs hold up, verification and rework can absorb the time saved by generation. We focus on testing the work against evidence so useful results can move forward and uncertain ones receive the checks they need.
API vision
Verification in the flow of research.
Our vision is an API that research systems can call as they work: send the relevant context, a claim or a tool result; receive a scoped finding, supporting evidence and a recommended next action.
The planned system would use fast checks for routine questions and deeper analysis for uncertainty. Validated checks and customer-defined rules would determine which steps can continue automatically and which need further testing or human review.
The goal is less manual checking and faster research cycles, not a guarantee that every output is correct.
Explore the proposed APIResearch scope & workflow
Science first.
Starting with AI, data science and biotech research. Our broader ambition is verification infrastructure for AI-generated work wherever reliable results can unlock useful automation.
01 / Research in motion
Work takes shape.
- Literature & hypotheses
- Experiments & results
- Agent steps & tool outputs
02 / Evidence checks
Does it hold up?
- Inspect the sources
- Test the assumptions
- Examine the comparison
03 / The next decision
Act on the evidence.
- Continue with support
- Investigate a missing check
- Revisit an earlier step
See what a closer look reveals.
Explore our documented research investigations, or share work you would like us to check.