Your AI scientist
Ask a research question in natural language and start an in silico research program.
Ask a research question. Scout runs the in silico science so you can explore more, fail earlier, and validate the best hypotheses with Lab-in-the-Loop.
Scout turns a question into an in silico scientific program. Ask in natural language. Scout determines what work is required, runs the right computational pipelines, critiques the evidence and returns a cited report,helping your team learn earlier, before committing time and cost to physical experiments.
Scout proposes. SigmaticOS orchestrates. Scientists guide. The lab validates. Results return to Scout—so every cycle gets smarter.
Ask a research question in natural language and start an in silico research program.
Review evidence, add context, challenge conclusions, and decide what moves forward.
Test the highest-value hypotheses and generate trusted experimental evidence.
Compare the typical capabilities of research agents and AI workbenches with Scout. No competitor names—just the capability gap that matters.
| Autonomous research agentsgeneral category | AI research workbenchesgeneral category | Sigmatic Scoutautonomous science | |
|---|---|---|---|
| Literature synthesis at scale | ✓ | ✓ | ✓ |
| Runs its own code & analysis | ✓ | ✓ | ✓ |
| Cited, reproducible output | ✓ | ✓ | ✓ |
| Target validation dossier | ad hoc | ad hoc | ✓ purpose-built |
| Clinical & patent landscape (FTO) | — | — | ✓ |
| Designs novel molecules & proteins | — | tool access only | ✓ generative by default |
| Developability & off-target profiling | — | tool access only | ✓ |
| Reads your ELN, LIMS & data lake | — | — | ✓ |
| Ingests wet-lab assay results | — | — | ✓ closes the loop |
| Trains on your proprietary science | — | — | ✓ |
| Adversarial critic on every step | — | — | ✓ |
| Deploys inside your perimeter | — | varies | ✓ |
In silico research is most valuable when it changes which experiments you choose to run. Scout helps teams explore more possibilities computationally, eliminate weaker paths earlier and send higher-value hypotheses into the lab.
Commonly cited industry range from discovery through approval.
Published estimates vary materially by methodology, indication and included cost of failure.
Only a minority of candidates entering clinical development ultimately reach approval.
A 2026 industry example reported reaching development candidates in roughly nine to twelve months versus about 4.5 years traditionally for that milestone. This is an external company-reported example, not a guaranteed Scout result.
The point is fewer low-value experiments, faster iteration, better use of scientific talent and more learning from every dollar of R&D.
Industry benchmarks shown for context; outcomes vary by program, therapeutic area and workflow. Sigmatic does not guarantee the external benchmark results shown here.
This isn’t a sandbox or staged demo. Run your own research questions on the real Scout product and evaluate the same research output a paying customer receives.
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