AI Infrastructure & Orchestration — production AI systems on AWS Bedrock for regulated and mid-market enterprises. Chicago and Melbourne.
Founded 2011, bootstrapped. We put AI agents to work inside the processes an organization already runs: retrieval and data plumbing, reliability engineering, multi-tenant platform work, and the discovery layer that determines whether any of it is ever cited by an answer engine.
This organization hosts the parts of that work we publish openly — a scoring standard, the scanner that implements it, and verbatim mirrors of our technical whitepapers. Canonical editions live at isimplifyme.com.
| Repository | What it is | License |
|---|---|---|
| aeo-standard | The AEO Standard — a versioned 100-point rubric for how answer engines read a page | CC BY 4.0 |
| aeo-scan | The open-source scanner implementing the standard's mechanical checks — npx aeo-scan <url> |
MIT |
| whitepapers | Technical papers — multi-agent orchestration, spend and model routing, retrieval, reliability engineering, business integration, private LLM deployment, AEO | CC BY 4.0 |
| isimplifyme-ui | React/Next.js component library used across our properties | — |
| nexus-reddit-monitor | Read-only Reddit mention alerting for small businesses | MIT |
Most published advice about ranking in AI answers is unfalsifiable. The standard exists to make the mechanical part checkable: every criterion is marked either [M] mechanical, verifiable by a machine, or [J] judgment. The scanner scores only the former and reports the rest as unscored rather than inventing a number for it.
A structural score is a floor, not a forecast. No score guarantees citation by any answer engine.
Issues on these repositories are reviewed on a monthly cadence. Reports of false positives or false negatives in aeo-scan checks are especially welcome — attach the page HTML, or the smallest reduction that still reproduces the problem.
See CONTRIBUTING.md and SECURITY.md.
Chicago, IL · Melbourne, AU