Built for Academic Research

State-Aware AI for Empirical Research

Sytra is designed for the rigorous demands of empirical research. It doesn't guess—it executes code in your local environment, verifies results, and guards against common validity threats.

The Data Janitor Problem

A recurring constraint in empirical research is not estimation per se, but the workflow that precedes it: acquiring data, normalizing formats, validating merges, diagnosing missingness, and producing reproducible outputs.

Generic AI Coding Tools

  • • Treat code as text, not stateful operations
  • • Cannot observe your actual data in memory
  • • Miss silent failures that don't raise errors
  • • Optimized for software, not statistical validity

Sytra

  • • Synchronizes with your Stata workspace state
  • • Executes locally, sees real error messages
  • • Audits for validity-threatening failures
  • • Enforces methodological discipline

Silent Killers

Results produced without errors that are nonetheless invalid. Sytra detects these first-class hazards automatically.

Sample Attrition

N drops across specifications due to missing controls or unintended filters

Merge Mismatch

Nontrivial mass of unmatched observations; duplicates violate m:1 assumptions

Omitted Variables

Variables dropped for collinearity or empty categories without warning

Clustering Drift

Different vce() or cluster variable across tables

Weight Misuse

Missing weights drop observations; wrong weight type changes estimand

Phase-Enforced Workflow

Sytra constrains agent actions by phase, preventing premature or unsafe operations.

Investigate
Understand data
Plan
Specify approach
Execute
Implement actions
Verify
Audit & summarize

Example: In INVESTIGATE phase, Sytra allows describe, codebook, tab, misstable but blocks destructive edits and estimation until you've understood your data.

Security & Privacy

Private by Design

Your data never leaves your machine. Sytra runs 100% locally.

Replication Ready

Generate complete do-files and logs for your appendix.

IRB Compliant

Meets data protection requirements for restricted microdata.

Design Principles

Transparency

AI-assisted transformations are logged and attributable. Provenance is a first-class output.

Reproducibility

Fixed settings, deterministic scripts, and explicit version recording.

Human Authority

AI may enumerate, execute, and diagnose; it does not decide which specification "counts."

Validity First

Biased toward flagging threats to validity early, not just speed.