From research to practice
Built on a career spent on both sides of the gap: as a professor, watching strong research stop at publication — as an entrepreneur, building the deployment path it never had.
In practice today: automated data profiling, AI-generated pipelines, self-healing integration — with human review on every change.
AI profiles every source — schemas, semantics, data quality, lineage — before a single pipeline is written.
AI generates the ETL itself: mappings, transforms, validation rules — reviewed and approved by people.
AI watches the pipelines in production — schema drift, breakages, new fields — and proposes the repair.
Knows every tag, scale and PLC quirk — watches calibration and sensor drift.
Keeps rosters, cost centers and org changes mapped to operations.
Maintains OCR, form versions and the capture-to-warehouse path.
Tracks regulator dataset refreshes and upstream schema changes.
Agents work like specialist employees: scoped responsibility, deep domain context, human sign-off on change — integration maintained as a practice, not a project.
Better documented, better maintained, and easier to evolve than the code it replaces.
Built on a career spent on both sides of the gap: as a professor, watching strong research stop at publication — as an entrepreneur, building the deployment path it never had.
Much of the published safety literature was built on public regulator data. Praxis makes that literature an operating asset instead of a library.
AI reads the published safety literature — much of it built on public MSHA datasets.
Identifies the exact datasets and methods behind each study.
Re-runs the analysis on current data — does the finding still hold?
Applies the validated method to your operation's own data.
Findings become procedures, forms and field checks via Forma and Performa.
Field outcomes pose the next research question — replication at industrial scale. Explore Praxis →
Continuous process signals — tonnage, density, amps, levels, pressures.
Discrete states — run / stop, open / closed, interlocks, alarms.
Operating context from Nexus — crews, products, delays, maintenance, quality.
An agent assembles just the tags, history and operating context one problem needs — a context library per circuit, not one giant model. Relational × time-series, joined.
Small, purpose-built models per control problem — assembled, grounded and maintained by agentic AI, not hand-built one at a time. Explore TSAI →
Bring one process problem and one safety question. We'll show you what the platform does with each.