Define the Decision
State the technical or financial question precisely enough to determine what evidence is required.
Independent maintenance decision consultancy
Eden Tech
Consulting
Maintenance Decision Assurance
Maintenance evidence, engineered for decisions.
We help asset-intensive and regulated organizations determine what their maintenance information can reliably support — then translate qualified evidence into technical, operational and financial clarity.
People.
Evidence.
Decisions.
Existing systems first
Read-only by default
Evidence before recommendation
Client authority preserved
01 / Who we are
Built around the decisions maintenance teams are already expected to defend.
Eden Tech Consulting works at the intersection of CMMS/EAM, maintenance data, asset reliability and operational decision-making. We do not begin by prescribing a transformation. We begin with the decision the organization needs to make and determine whether the available evidence can support it.
Our role is to reconcile information, qualify the analytical population, expose uncertainty, quantify technical and financial meaning, and define practical controls for stronger future decisions.
Why Eden02 / The problem
Having maintenance data is not the same as having maintenance evidence.
Years of records can exist across CMMS/EAM, calibration systems, inspections, spreadsheets and technical documents without producing a decision that can be defended.
01
A field can be populated but misunderstood.
02
A calculation can be correct but based on the wrong population.
03
A KPI can be visible but not defensible.
04
A maintenance program can be expensive without clear evidence of value.
05
A new sensor, dashboard or AI tool can add information without fixing the decision problem underneath it.
Eden works between the existence of information and the ability to use it responsibly.
03 / Method
Start with the decision. Work backward to the evidence.
Reconcile the Evidence
Identify the relevant sources and establish what fields, statuses, dates and records actually mean.
Qualify the Data
Define defensible inclusion, exclusion and confidence rules before calculation.
Translate the Value
Analyze only the qualified population and connect findings to operational and financial consequence.
Recommend Controls
Define the minimum approved changes needed to improve future decision capability.
04 / Maintenance Decision Diagnostic
One decision. Existing information. A defensible starting point.
The client brings one maintenance question worth answering. Eden evaluates what already exists, performs only the analysis the evidence can support, translates the opportunity or exposure, and defines what would make the next decision stronger.
Defined entry boundaries
- No software replacement
- No hardware deployment
- No initial write access
- No autonomous changes
- No initial SOP change
- No required data migration
Deliverables
Evidence ReconciliationDecision-Readiness AssessmentTechnical AnalysisConfidence StatementFinancial TranslationEvidence Gap RegisterFuture-State Evidence SpecificationControlled Recommendations
05 / Use cases
The method is decision-specific, not industry-generic.
01
Calibration interval analysis
Do our calibration histories contain enough evidence to challenge current intervals responsibly?
02Preventive maintenance optimization
Which PM tasks or frequencies deserve engineering review, and what evidence supports that review?
03Bad-actor and repeat-failure analysis
Which assets or failure mechanisms are genuinely recurring after record meaning and population rules are reconciled?
04Maintenance KPI evidence readiness
Can our records support MTBF, MTTR, downtime or backlog metrics without misleading management?
05Maintenance cost analysis
What maintenance spend is associated with the assets, activities or uncertainty we are trying to manage?
06Predictive maintenance and AI readiness
Is the underlying asset, work-history and failure evidence strong enough to justify advanced monitoring or AI investment?
Decision Advisor
Describe your challenge and see the evidence it demands.
Use the in-app advisor to state a maintenance or CMMS/EAM technology challenge in your own words. It frames the decision behind it, names the records required to answer it, and flags the data-meaning risks that most often make conclusions misleading.
What it returns
- The single decision your challenge reduces to
- The evidence required to answer it defensibly
- The data quality risks most likely to mislead
- Sequenced next steps you can act on
AI-assisted guidance based only on what you describe. Share context, not confidential records.
06 / Why Eden
Evidence before transformation.
Work backward from the decision, not forward from a software feature.
Establish meaning before mathematics.
Distinguish supported conclusions from directional evidence.
Translate technical findings into operational and financial meaning.
Recommend the minimum necessary intervention.
Preserve engineering, Quality, IT, Validation and management authority.
07 / Governance
Designed to work inside your controls — not around them.
Read, not write
Read-only access or controlled extracts are the default starting point.
Analyze, not execute
Eden may identify an action; the client determines whether and how it is executed.
Recommend, not change
No production configuration, procedure or maintenance-strategy change occurs without approved scope and client authorization.
Source-cite, not assume
Material findings identify source records, interpretation rules, assumptions and limitations.
Qualify, not blindly calculate
The analytical population is defined before conclusions are presented.
Disclose commercial interests
Any relevant technology relationship is disclosed and every recommendation must remain technically supportable.
Client approves, not Eden
Engineering, Quality, IT, Validation, Legal and other governing functions retain their authority.
08 / Insights
Evidence before opinion.
Original, technically useful material built around real maintenance decision problems. The first research notes are in development.
Research note / forthcoming
Can Your CMMS Data Actually Support MTBF?
Maintenance KPI and CMMS data analysis
Research note / forthcoming
Before Optimizing PM Frequencies, Test the Evidence
PM optimization and reliability decisions
Research note / forthcoming
NULL, Blank or Zero? Why Maintenance Data Semantics Matter
Maintenance data meaning and integrity
09 / Contact
Bring us one maintenance decision worth examining.
Start with the question, not the system. Tell Eden what your team is trying to determine, what information is available and why the decision matters.