generate-data.com
Synthetic test data for systems that fail on tidy demos.

We study hard problems in intelligent systems where they show up under real operational pressure, then turn what we learn into tools.
§ 01 — How we operate
Smith Advanced Systems studies hard problems in intelligent systems and applied software where they show up under real operational pressure, then turns what we learn into tools and research projects.
“We build where we have felt the gap ourselves, without forcing teams into locked platforms.”
Fragile automation, brittle model workflows, and systems that look fine in demos and fail in production: those constraints shape what we research. We prototype with practitioners, then ship software meant to be owned by the teams who run it.
We study complex problems where they actually exist: in production systems, under real load, with real operational consequences. Through experimentation, prototyping, and direct work with operators, we build a ground-level understanding of the constraints. The right tools follow from that understanding.
Read our approachOut of that field work comes a portfolio of software projects: agent orchestration, applied ML products, and supporting tools that integrate into existing stacks and can stand alone as independent companies.
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Scale intelligence. Orchestrate anything.
Agentic financial intelligence.
One agent asked to plan, build, test, and validate still fails on large work the way large human projects fail. Skailr is the org structure we install on top of Claude Code and Cursor.
Matching pipelines, scoring models, and agentic systems all fail on the same class of case: the messy one a tidy demo never shows you.
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