EAM Boutique
Focused improvement work around HxGN EAM / Octave Attune EAM, work order logic, data quality, reporting, FlexSQL, JavaScript and operational workflows.
View pathEAM · interfaces · AI & ML
Improve EAM. Stabilise interfaces. Apply AI where it adds value
I improve EAM environments, fix unreliable data flows between business systems and develop focused AI/ML solutions around real data and decisions. Each capability is available on its own.
Deep HxGN EAM / Octave Attune EAM experience; interface engineering with APIs, SQL and monitoring; and AI/ML pilots with representative tests, clear evaluation and human oversight.
Problems I solve
When systems, data and decisions stop lining up
Whether the starting point is EAM, an interface or AI/ML, value is lost when technical behaviour, data meaning, ownership and the real workflow stop matching. That is where I work.
Work-order logic no longer matches maintenance reality
Reports or custom rules have drifted away from their operational meaning
APIs return success while records, statuses or timing still fail downstream
Manual corrections hide weak exception handling and unclear ownership
An AI idea has no defined decision, test set or acceptable failure level
A promising AI prototype has no integration path, guardrails or operational owner
Daily Operations Work, decisions, coordination and accountable owners
EAM Systems Assets, work orders, maintenance logic and reporting
Interfaces APIs, ERP, CRM, MES, SaaS and data flows
Applied AI & ML Retrieval, extraction, classification and decision support
Reporting & Automation SQL, dashboards, workflows and work apps
Three capabilities
Three capabilities, seven ways to start
Start with EAM, interfaces or applied AI. They often meet in operational work—but none of the three requires buying the others. Seven focused services provide a clear way to begin.
Interface Engineering
Reliable APIs, mappings and data flows across enterprise, operational and cloud systems—whether or not EAM is involved.
View pathApplied AI & Machine Learning
Computer vision, sensor and time-series ML, document intelligence and decision support—built as focused pilots, tested against measurable criteria and kept within human guardrails.
View pathHow I work
From the problem to a tested next step
The work starts with evidence: a work order, interface record, data sample, document, report or manual handover. The goal is to decide what should change, test it against real cases and document the result.
01
Start with the real bottleneck
Unclear work orders, unreliable interfaces, poor data, a manual workflow or one defined AI/ML opportunity.
02
Build or specify the smallest useful change
An EAM change, interface fix, data-readiness decision, focused work app or AI/ML pilot that can be tested against real cases.
03
Make the next decision explicit
Document the logic, ownership, test evidence and handover path—then deploy, harden, integrate or stop with a clear reason.
Partner Support
Technical support for partner teams
Add hands-on depth around EAM logic, SQL/FlexSQL, APIs, interface reliability, data readiness, AI/ML pilots or operational workflow automation—without changing the broader partner structure.
Partner-safe modes
- Visible specialist in client conversations
- Background delivery resource for defined work packages
- Technical reviewer for difficult EAM, interface, data and AI/ML edges
- Sprint-based implementation support for project teams
- Subcontractor support inside an existing delivery model
Difficult areas strengthened
EAM boutique: HxGN EAM / Octave Attune EAM EAM logic: work orders, SQL, FlexSQL, reports and custom rules Interface engineering across ERP, CRM, MES, EAM and cloud tools API contracts, data meaning, synchronization and exception handling Data quality and AI readiness across operational and business workflows Applied AI/ML pilots for extraction, classification, retrieval and prediction Evaluation, failure modes, human guardrails and integration paths Focused work apps and workflow automation
The intent is to make the partner's delivery stronger in the difficult operational edges, not to take over broader implementation, project management or account ownership.
Field notes
Field notes from systems and projects
Practical walkthroughs, external notes and selected topics across the EAM last mile, interface reliability and applied AI/ML. Service links are labelled as topics—not presented as published articles.
YouTube
Practical walkthroughs
Short demonstrations of EAM, interface behaviour, operational automation and realistic AI/ML use cases.
Open YouTube LinkedIn
Field notes and takeaways
Operational observations from EAM, interfaces, data quality, applied AI and partner delivery work.
Open LinkedIn Blog
From pilot to production
Longer notes on EAM last-mile work, interface reliability, data readiness, applied AI/ML and practical automation.
Open BlogEAM topic
The last mile is where EAM projects usually get expensive
Why work order logic, reports, status flows, interfaces and user workflows often determine whether an EAM setup works in day-to-day operations.
Explore topicInterface topic
A successful API call can still be an operational failure
Operational reliability depends on data meaning, timing, validation, retries, ownership and downstream results—not only an HTTP success response.
Explore topicAI readiness topic
Before an AI pilot, define the decision it should improve
A useful AI/ML use case starts with a real workflow, a baseline, representative data and explicit acceptance and stop criteria.
Explore topicApplied ML prototype
Water-pressure monitoring: separating normal pump cycles from small leak patterns
In a self-initiated prototype, a pressure sensor, Arduino and Linux mini PC captured the time series; ML separated normal water-use and pump-restart cycles from persistent patterns consistent with small leaks.
Explore topicApplied ML prototype
Hoof image screening: flagging visible anomalies for human review
A self-initiated computer-vision prototype used a camera in a barn passage to capture hooves as horses walked past and flag visible irregularities for human review—not to make a veterinary diagnosis.
Explore topicWorkflow topic
Sometimes the useful tool is not another EAM module
Small operational apps can help with requests, checklists, handovers or reporting when the core system should remain stable but day-to-day workflows still need improvement.
Explore topicBest fit
Who I work best with
The strongest fit is where systems, data, documents and decisions have to survive real operational pressure—not only look good in a project plan.
Manufacturing & process industry
Energy & utilities
Facilities & building services
Logistics & transport
EAM, integration, data & AI partners
If your teams live between enterprise systems, APIs, spreadsheets, documents, data and real operational decisions, this is usually a good fit. EAM can be part of that landscape—but it does not have to be.
Start
What is getting in the way?
It can be an EAM bottleneck, an unreliable interface, an AI/ML opportunity or a manual operational workflow. One concrete example is enough for a useful first assessment.