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 on 900k+ real validation data points at ~5% mean error.
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.
143 specialized engines across chemistry, energy, materials, biology, separation, analytics, pharma, and electrochemistry — each calibrated on published data. General AIs cannot match domain depth.
900k+ validation data points form an experiment parameter–result knowledge graph. Physics from real experiments, 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.
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