ERPClaw Research
Original research on the ERP, AI, and accounting markets. Open data, reproducible methodology, free to cite.
Current research
AI ERP Transparency Index 2026
A public scorecard of 50 ERPs measured against 12 transparency criteria for AI-native architecture. Open data, sourced scoring, reproducible methodology. Methodology locked May 2026; full ranking publishes Q3 2026.
The 5-Trait Test for AI-Native Architecture
The five architectural traits that separate AI-native ERPs from AI-decorated overlays. Free methodology, reproducible scoring, open challenge process. Used as the foundation of the Transparency Index.
ERP Install Time Benchmark
Time-to-first-invoice across the 10 most-installed ERPs in 2026. From bare machine to posting an invoice. Reproducible scripts, public timing data, no marketing claims.
How to cite this research
All ERPClaw research is free to cite for any purpose, including commercial, academic, and journalistic use. We ask only that you cite us correctly so readers can find the source.
Standard citation format
AvanSaber Inc. 2026. [Title of Research]. Available at https://www.erpclaw.ai/research/[slug]/
Example
AvanSaber Inc. 2026. The 2026 AI ERP Transparency Index. Available at https://www.erpclaw.ai/research/ai-erp-transparency-index/
For academic citation in BibTeX, IEEE, or APA format, email [email protected] and we will send the formatted entry.
Why we publish research
Gartner's Magic Quadrant ranks ERP on overall capability. IDC reports sit behind a $5,000 paywall. Industry analysts disclose vendor relationships in footnotes that nobody reads. We publish research the opposite way: open data, open methodology, free downloads, and a documented challenge process for any vendor who disputes a score.
ERPClaw is a vendor in the market we measure. We disclose that on every page, we self-score using the same rubric we apply to competitors, and we publish our own scores even when they are not flattering. If you find a methodology error or a sourcing gap, file an issue on the public GitHub repo for the artifact in question. We correct in public.
Background on the architectural argument that drives this work: read the AI-native ERP pillar.
Stay current on new research
Two artifacts published in 2026 so far. Three more queued for the next four quarters. Subscribe for release notifications.