Praxis
Praxis turns methodology consensus into experimental design. Power and sample-size computations are grounded in the parameter values. Lumen extracted from the literature, with lab outcomes from Labtrack feeding back in as your own evidence source.
Inputs
- Methodology consensus from Lumen
- Study constraints (effect size, variance, budget, platform)
- Lab-in-the-loop outcome history from Labtrack
Outputs
- Experimental design with statistical power computation
- Sample-size recommendations with the literature basis cited
- Design records persisted for downstream ingestion
How the AI suggests, and where you decide
Praxis proposes designs ranked by evidence strength and surfaces the assumptions behind each. The researcher chooses the design; the platform does not auto-commit a protocol.
Illustrative
Praxis in practice
Citation grounding
Each design parameter references the methodology consensus and the papers behind it. Lab-derived signals are attributed explicitly as user-specific lab evidence, distinct from published literature.
For the technical reader
Technical details
- Power and sample-size computation parameterised by literature-derived effect sizes and variance.
- Design-Build-Test-Learn loop: Labtrack outcomes refine subsequent Praxis designs.
- Design records persisted with provenance for Veraxa validation.
Researchers, Clinical research, Pharma enterprise. The decision-support boundary is surfaced consistently: the platform suggests; the user decides.
Each capability is one link in the closed loop. See the whole chain end to end.