Minutes
Lookup time
Anonymized engagement brief
Oil & GasAsset Management
Spare Parts & Obsolescence Intelligence
Multi-OEM installed base: Honeywell, Schneider, ABB, Siemens, Emerson and more.
Engagement answer
NeoBram built AI agents that map installed equipment to replacement parts and end-of-support status across multiple OEMs, deployed completely offline inside the customer's environment. Part and obsolescence lookups now take minutes instead of hours, with no data ever leaving the plant.
Reported outcomes
What changed in the stated scope.
100% offline
Deployment
Anonymized engagement brief
Multi-vendor
OEM coverage
Anonymized engagement brief
Single source
Answer consistency
Anonymized engagement brief
These figures are not a guarantee or a benchmark for another organization. A new project requires its own baseline, scope, measurement method and acceptance test.
Client context
Who the brief describes
An oil and gas operator managing a multi-OEM installed base across multiple sites, with equipment from Honeywell, Schneider, ABB, Siemens, Emerson and other vendors.
Business problem
What needed to change
Finding replacement part numbers and end-of-support status across equipment from different manufacturers meant hours of manual digging through OEM catalogs, datasheets and emails for every single query.
Baseline
Where the work started
- Part lookups taking hours per query across OEM catalogs
- Obsolescence status scattered across vendor bulletins and emails
- No single consistent answer across OEMs and sites
- Strict plant security rules ruling out cloud SaaS tools
Data
Evidence used by the system
- OEM catalogs and datasheets for Honeywell, Schneider, ABB, Siemens, Emerson
- Vendor end-of-support and product-change bulletins
- Installed-base register and engineering tag list
- Historical purchasing and spares-consumption data
Solution
What NeoBram built
AI agents that map installed equipment to replacement parts and obsolescence status across OEMs, deployed completely offline inside the customer's environment. No data ever leaves the plant.
Integration
Systems and interfaces
- Local ingestion of OEM catalogs, datasheets and bulletins
- Connector to the installed-base / asset register
- Single search and chat interface for engineering and reliability teams
- Periodic offline sync of new OEM documents under change control
- Air-gapped deployment - no external network calls
Timeline
Reported delivery sequence
Working agent on the first OEM library in 4 weeks. Multi-OEM coverage and offline deployment completed within one quarter.
Governance
Controls described in the brief
- Fully offline, air-gapped deployment inside the plant
- No external network calls and no data egress
- Source documents and answers fully traceable per query
- Change-controlled refresh of OEM content
- Customer owns the agents, the index and the underlying content
Measurement method
How to read the outcome
- The page preserves the engagement's reported baseline, implementation scope and outcome labels.
- The customer dataset, calculation workbook and acceptance records are not publicly available for independent review.
- Any percentage, time or accuracy value applies only to the stated scope and should not be treated as a forecast for another site.
Limitations
What this brief does not prove
- Customer identity is withheld, so public reference checking is not possible.
- Performance depends on the original data, process, hardware, users, thresholds and review workflow.
- The brief does not establish causality beyond the engagement's reported comparison.
- Future buyers should define their own baseline, held-out test and acceptance criteria.
Build your own evidence record
Define the baseline and acceptance test before the pilot.
NeoBram will help turn one operating problem into a scoped, reviewable AI project and hand over the production capability.