// industries · aviation mro
Turn reactive maintenance into predictive intelligence.
Most aviation MROs run blind until equipment fails. We wire shop-floor telemetry into the legacy ERPs that actually run your operation — so TAT commitments are something you can see, not something you hope for.
> ask_ai("which work orders are at risk of blowing TAT?")
x ERROR: source of record = legacy_erp.dbf
x no API. no telemetry. no audit trail.
> deploy mro_modernization_stack
+ shop-floor telemetry → staging
+ legacy ERP exposed read-only
+ TAT risk visible per work order
// the operating reality
Your shop already produces the data. It just can't reach it.
Every job traveler, inspection sign-off, and parts issue is recorded somewhere — an ERP from another decade, an Access database one retirement away from orphaned, a spreadsheet only the lead knows how to read. The failures aren't surprises. The data that predicted them was just locked up.
What unplanned equipment failure costs a mid-size MRO every year it keeps running reactive.
The AI-Ready Data Audit turns a preliminary blueprint into a validated engineering plan, for $5,000.
Work trusted by the US Navy and US Air Force happens onshore, end to end.
// the mro modernization stack
From shop floor to system of record
We map every system of record — ERP, MES, maintenance logs, the FoxPro database nobody admits to — and find where the data actually lives, who owns it, and what is reachable.
Decades-old ERPs untangled and exposed through governed, read-only access layers. No rip-and-replace, no big-bang migration, no downtime on the floor.
Shop-floor telemetry wired into the systems that run your operation, so equipment status is data in a queryable table — not a walk to the floor.
Dashboards and AI agents that answer real questions: which jobs are at risk, which parts keep causing delays, where TAT is slipping — with an audit trail on every query.
// what you could ask
Questions your systems should be answering
// governed by design
CMMC, ITAR, CUI — AI doesn't get a pass.
If you supply aerospace and defense programs, "just upload it to the cloud" is not an architecture. We build least-privilege, read-only access layers that keep controlled data inside the boundary you already defend — with an audit trail on every query an AI agent runs.
> grant ai_agent SELECT on work_orders
+ scope: read-only · least privilege
+ boundary: restricted programs excluded
+ audit: every query logged
> export work_orders → public_cloud
x denied by policy
// tools we bring
Built for the shop floor
// where to start
Map the access problem before buying another AI tool.
The AI Data Access Blueprint is a free preliminary map of where an AI initiative will stall in your shop: data access, legacy systems, governance, or ownership. The paid audit then validates it against your real systems.
We map the fix in 5 days for $5,000 — credited toward the build if you hire us.