A multi-agent virtual lab that fits GP surrogates, quantifies uncertainty with coverage-audited intervals, and draws the line where a model stops being valid. Other tools ask did it reproduce? — SwarmLabs answers how confident, and where does it break?
Four pillars of the virtual acceleration lab
Engines built on the Arrhenius equation and reaction kinetics — no random-number generation. Each is calibrated against published benchmark values: 161 stored calibration points across 82 validated scenarios, ~5% calibration error, and 0 of 82 have experimental ground truth yet (stated on the facts table).
Eight specialized Bee Agents share knowledge through a blackboard pattern, covering literature search, hypothesis generation, experiment design, analysis, and peer review.
Connected to Crossref / PubMed / arXiv. Every prediction traces back to its source paper — transparent and auditable.
AI searches for optimal experiment parameters with physics-guided grid search and local refinement, converging to the global optimum within 100 iterations.
Not a rubric score or an audit trail. Every claim carries a computed number: coverage against nominal, or an explicit pass / controlled / reject boundary. 18 literature benchmarks — including the 7 in the red zone — are published, not filtered.
Generates standard research reports from results and validation data, with abstract, methods, results, and data-availability statements.
Browse the V&V report library →What SwarmLabs delivers that Claude, ChatGPT and other general AIs cannot
Built on ASME V&V 10-2019(R2025) and Sandia's 16 SciML recommendations. Every prediction is scored on fidelity / simplicity / stability / coverage. General AIs cannot offer a physics audit.
Browser-side molecular-orbital rendering of 10^6 grid points in ~100 ms, benchmarked against MOrbVis (ACS Omega 2026). Zero install, pure web.
SHAP-style parameter attribution with a physical rationale for every recommendation. Explainable Bayesian optimization (XBO) shows *why*, not just *what*.
You own your data; we never train on it. Transparent pricing at a fraction of enterprise lab platforms.
82 validated scenarios on 57 engine modules across chemistry, energy, materials, biology, separation, analytics, pharma, and electrochemistry — each calibrated on published data. General AIs cannot match domain depth.
157,836 structured research entities form an experiment parameter–result knowledge graph. Physics from explicit equations and published benchmark values, not statistical guesses from web text.
From literature to report — the full research workflow in one place
Real academic search via Crossref
AI generates hypotheses from literature
Pick engine & parameters
Run the physics-model prediction
Uncertainty & confidence assessment
Eight automated review checks
Standard-format research report
Every published claim on this site was re-run through the SwarmLabs virtual-experiment engine. 18 literature benchmarks, 7 in the OOD red zone — all published, none filtered. Try the live OOD guard yourself.
A demo account is ready — experience the full loop from literature to report.
🚀 Enter the lab