60%
Traditional code and database work
- Sensor-data ingestion from BLE tags
- Cabin-asset records and audit logs
- Mobile app sync, device fleet management
A short note from Matt Creamer, CRO at Eduba
A read on where AI actually helps inside AEROSENS. And where the BLE workflow is already the right answer.
Un análisis de dónde la IA realmente aporta valor en AEROSENS, y dónde el flujo BLE ya es la respuesta correcta.
AEROSENS compressed a six-hour cabin inspection to one minute on a mobile device. This page is a short note on what we saw and the first conversation we'd like to have.
01
What we read this week
The AEROSENS team pairs Airbus technology pedigree with Airbus LATAM relationships and a Miami operating base. We spent time on the AEROSENS site, read the Aerospace Xelerated spotlight, and mapped the places where an AI narrative would sharpen the enterprise conversation without distracting from the IoT core.
One observation stood out. Enterprise airline procurement in 2026 assumes an AI-enabled data layer on any IoT vendor. The AEROSENS site talks IoT, workflow, and safety inspections. There is no public AI framing yet. Either the work is underway and marketing has not caught up, or it is the next sprint. A 60/30/10 map of the existing workflows highlights exactly where AI earns its keep and where it does not.
02
The Eduba frame, applied to AEROSENS
Most organizations automate the wrong things. About 60% of what an IoT-plus-workflow platform does every day is traditional code and database work. About 30% is rule-based logic. Around 10% is a genuine AI problem.
30%
Rule-based logic
- Safety-equipment inspection schedules
- Tamper-alert triggers
- Regulatory reporting workflows
10%
Genuine AI problems
- Anomaly detection across inspection patterns
- Predictive maintenance recommendations
- Narrative generation for compliance reports
The proposed work is to sort AEROSENS current workflows into those three layers and identify where an AI layer would sharpen an enterprise airline conversation. No new software stack. A written map of where AEROSENS already is, and where the next engineering sprint pays off.
03
Four hypotheses worth a conversation
H1
The AI-narrative gap on enterprise RFPs
Tier 1 carriers assume an AI-enabled data layer on any IoT vendor. The 60/30/10 map produces a defensible answer that avoids overclaiming.
H2
LATAM and U.S. as two different sales motions
The AEROSENS LATAM relationships and the Miami HQ create two buyer paths. Without a documented playbook, founder time splits across both and neither converts fast.
H3
Certification readiness for enterprise airline buyers
SOC 2, ISO 27001, and airline-specific cybersecurity reviews gate the Tier 1 conversation. A documented 12 to 18 month readiness plan is scoped work.
H4
Packaging IoT data into a fleet-insight product
AEROSENS captures inspection data across cabins. That stream is a future data product; packaging it as an analytics offering is an architecture question, not an engineering one.
04
The relevant case study
Regulated-industry consulting, leadership audience
KPMG UK, one of the Big Four
Eduba trained 40+ executives at KPMG UK (Big Four) on the methodology for running AI-adoption conversations inside regulated industries. Aviation-industry buyers trust that same consulting-firm-to-regulated-industry posture.
Translating to AEROSENS: an audit-first engagement that produces the AI-adoption narrative, the enterprise-airline procurement path, and the scoped analytics roadmap as written deliverables.
1,500+
Enterprise learners trained since May 2025 across Pacific Life, Colgate-Palmolive (via Correlation One), and KPMG UK.
05
The relevant paper
Methodology
Interpretable Context Methodology: Folder Structure as Agent Architecture
Submitted to ACM TiiS. Open source, MIT license.
github.com/RinDig/Interpretable-Context-Methodology-ICM-
AEROSENS needs an organizational-context architecture that connects the sensor data layer, the workflow layer, and the eventual AI analytics layer into a coherent audit-ready stack. ICM's folder-structure-as-architecture framing is directly useful for that work.
06
Credibility, in one paragraph
Jake Van Clief, founder
- Marine Corps veteran, 8 years on cryptographic systems, F-35 and F-18 avionics, and the Joint Helmet Mounted Cueing System, with direct relationships with Pentagon program managers
- MSc Future Governance, University of Edinburgh
- Published in ACM TiiS (ICM) and arXiv (Ethics Engine)
- 1,500+ enterprise learners trained since May 2025
- Built an online community to 22,000 members in five weeks
Ecosystem
Eduba partners with NLP Logix for work that sits below the orchestration layer. NLP Logix has been in machine learning since 2011 and runs over 150 data scientists.
On AI governance for regulated data: the Ethics Engine, a psychometric assessment tool for evaluating ideological and moral patterns in LLMs.
One concrete next step
Thirty minutes. Bring the RFP question that stalled your last airline sale.
We will walk the 60/30/10 map together and scope a first engagement from there. Audit-first. Written deliverables. Bilingual on request.
Book time with Matt Creamer
Matt Creamer
Chief Revenue Officer, Eduba