AutoStat converts statistical analysis into thesis-ready academic reports. No coding. No ambiguity. No variation.
AutoStat is designed for researchers who need structured, defensible statistical reporting without the overhead of manual analysis workflows. It addresses a specific gap in doctoral and academic research: the time and effort required to translate raw statistical output into examiner-ready narrative, reducing iteration cycles with supervisors and eliminating reporting friction from the research process.
AutoStat is a deterministic statistical reporting system designed for academic research. It executes a fixed analytical pipeline — including regression modelling, assumption diagnostics, and hypothesis mapping — and produces structured, examiner-ready reports with full traceability from input data to output narrative. It eliminates ambiguity, automates diagnostic reporting, and removes the manual effort of statistical write-up.
AutoStat executes a fixed analytical pipeline for every submission. Identical input data, variable selection, and hypothesis configuration always produce byte-identical output. Each report includes a SHA-256 hash that provides a verifiable integrity reference.
AutoStat automatically selects and executes the appropriate regression model and diagnostic tests based on the structure of your dataset. No manual test specification, coding, or statistical software configuration is required.
Generated reports include structured hypothesis decisions, assumption diagnostic results, coefficient tables, and APA-aligned narrative — formatted for direct inclusion in academic chapters and suitable for examiner review.
Traditional tools such as SPSS, R, and Stata require syntax knowledge, software configuration, and familiarity with statistical environments. AutoStat operates from a CSV upload with no coding required and no software licenses needed.
Manual statistical workflows introduce transcription errors, incorrect test selection, and reporting inconsistencies. AutoStat eliminates these by executing a deterministic pipeline where each output is directly traceable to the submitted input.
What typically requires hours of software configuration, test execution, and manual write-up is completed in a single submission. AutoStat generates the full analysis and structured academic narrative in one step.
Generative language models produce variable output on identical inputs due to probabilistic sampling. AutoStat is architecturally deterministic — the same dataset and configuration will always produce the same report, every time.
AutoStat computes statistical values directly from submitted data using established procedures. It does not generate, estimate, or interpolate results. There are no hallucinated coefficients, invented p-values, or fabricated model outputs.
Reports include computed coefficients, standard errors, test statistics, and assumption diagnostics — all directly traceable to the submitted dataset. Output is structured for examiner review and academic submission.
Ardelion Intelligence is a software research company focused on building deterministic systems for complex academic and professional workflows. Its work is grounded in the principle that structured problems require structured solutions — reproducible, traceable, and free from probabilistic ambiguity.
AutoStat is Ardelion Intelligence's first product. It was developed in response to a specific and well-documented problem in academic research: the friction between raw statistical output and the structured narrative required for doctoral and academic submission. AutoStat converts that gap into a deterministic, verifiable process.
The company's broader vision is to systematise workflows that currently depend on manual interpretation, domain-specific expertise, or probabilistic AI tools — replacing them with rule-based execution systems suitable for high-stakes academic and professional use.