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PlotStudio AI | Agentic Analytics for Academic Research

An agentic analytics platform built for research. Specialised AI agents plan, code, execute, and interpret analyses on the researcher's own machine — producing defensible, reproducible results suitable for papers, theses, grants, and peer review.

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# PlotStudio AI | Agentic Analytics for Academic Research

> An agentic analytics platform built for research. Specialised AI agents plan, code, execute, and interpret analyses on the researcher's own machine — producing defensible, reproducible results suitable for papers, theses, grants, and peer review.

PlotStudio AI is built for work that has to survive peer review. Rather than returning a chart and a paragraph, it writes real R and Python for every step, shows the researcher the code, gates execution behind a statistical plan they approve in advance, and exports a reproducibility package a supervisor or reviewer can trace line by line. Analysis runs locally, so data never leaves the researcher's device — the only workable approach for IRB-governed, NHS, or GDPR special-category data.

## What Makes It Research-Grade

### Local Execution

- All analysis runs on the researcher's own machine; data is never uploaded
- Makes the tool viable for patient records, clinical trial data, and other special-category data that cannot lawfully be sent to a cloud service
- API calls are made directly from the user's environment
- Optional private deployment (Azure / bring-your-own-environment) for institutional data sovereignty

### Human-in-the-Loop Plan Approval

- Every analysis is gated behind a plan the researcher reviews before any code runs
- The plan states methods, assumptions, statistical thresholds, and what counts as success
- An approved, frozen plan functions as a pre-registration and an audit trail
- Review depth is configurable: Off, Plan, Detailed, or Research Protocol

### Skills — Encoded Field Methodology

- A researcher or lab encodes how their field does analysis once: required steps, thresholds, forbidden shortcuts, reporting conventions, references
- The engine then plans, codes, and interprets every subsequent analysis that way
- The domain expert writes the skill; the engine runs it — no generic defaults
- Skills are shareable, so methodology spreads across a lab or department

### R and Python

- Runs R natively alongside Python — clinical, epidemiology, and biostatistics work is R-native, and Python-only tools are disqualified in those fields
- Supports the methods academic work actually requires: survival analysis, Cox proportional hazards, mixed-effects models, regression, ANOVA, multiple-comparison correction
- Domain intelligence across 12 specialisations, including IV/2SLS, GARCH, and Cox PH

### Transparent, Auditable Analysis

- Agents generate and execute real code, with visible chain-of-thought reasoning
- Self-correcting execution with plots, tables, and datasets produced live
- The researcher can read, defend, and hand over every step — nothing is a black box

### Data Profiling

- Immediate data-quality assessment: Clean, Usable, or Needs Cleaning
- Automated cleaning plans the researcher approves before they run
- Handles the messy reality of research data, including REDCap and clinical-system exports

### Analysis
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