Your Plant's
Institutional Knowledge,
Finally Findable
Operating plants accumulate decades of critical asset information — buried in legacy PDFs, scanned drawings, outdated formats, and personal hard drives. Asset Intelligence ingests that chaos, extracts structured metadata, builds tag-level references, and makes every piece of engineering knowledge queryable in seconds.
Four Ways Plant Asset Knowledge Gets Lost
Operating plants generate enormous volumes of critical asset information across decades of operation — but four recurring failure modes make that knowledge inaccessible when it's most needed.
What the Asset Intelligence Agent Delivers
Every capability addresses a specific failure mode in how operating plants currently manage their asset information — turning a liability into a live, queryable knowledge base referenced to every equipment tag.
Ask Your Plant's Knowledge Base Anything
Operations team members query the full asset knowledge base in plain language — and receive answers with direct links to source documents. No training. No search syntax. Source references on every answer.
From Legacy Document Heap to Queryable Asset Knowledge
A four-stage pipeline ingests whatever format your legacy documents exist in, extracts metadata using AI, builds equipment-tag relationships, and makes everything accessible through a natural-language query interface.
Asset Intelligence vs Conventional Document Management
Every dimension of plant knowledge retrieval is transformed — from how engineers find information at 2am, to how MOC reviews assemble document registers, to how institutional knowledge survives retirements.
| Aspect | Conventional Document Management | ✦ Wrench AI Asset Intelligence |
|---|---|---|
| Document Retrieval | Shared drive search by filename and folder navigation | ✔ Natural-language query with source-linked answers |
| Equipment-Tag Linkage | Not maintained — documents filed by project or date | ✔ Bidirectional tag-to-document graph across all records |
| Legacy Format Access | TIFFs, scans, paper documents effectively unreadable | ✔ OCR + AI extraction makes every format queryable |
| Metadata Population | Manual entry — inconsistent, often incomplete or skipped | ✔ AI-extracted from content, applied consistently at scale |
| Knowledge Preservation | Lives in retiring engineers' heads and personal drives | ✔ Captured, structured, and synthesised into AI manuals |
| MOC Document Register | Days of manual searching; registers often incomplete | ✔ Generated in minutes, cross-referenced, demonstrably complete |
| On-Call Information Access | Phone the day shift; hope someone knows where it is | ✔ Mobile-accessible, tag-level retrieval in seconds |
What Changes for Operations Teams
Three situations every plant operations team faces — and how the Asset Intelligence AI Agent changes the outcome. Each scenario draws on the same underlying capability: tag-level retrieval over a structured knowledge graph.
- Query: "All maintenance records and vendor manual for P-101"
- Last 5 maintenance records retrieved instantly
- Vendor manual section on emergency isolation linked
- Most recent inspection certificate attached
- Mobile-accessible from the site, tag-referenced
- Every document the engineer worked with already ingested
- Equipment relationships extracted and indexed
- Operating procedures synthesised into AI instruction manuals
- Annotation habits become training data for the AI
- Knowledge gets preserved, not retired
- Queried by system or tag number
- Complete document set across 20 years returned in minutes
- Automatically filtered to modification scope
- Cross-referenced register generated, demonstrably complete
- MOC compliance evidenced, not assumed
Four Outcomes for Operating Plants
The Asset Intelligence agent doesn't just speed up document search. It converts an unmanaged liability — decades of fragmented asset knowledge — into a structured, queryable, compliance-supporting resource that the entire operations team can use.
Built for Every Operations Stakeholder
From Knowledge Retrieval to Predictive Operations
Today the agent delivers structured knowledge retrieval. The roadmap takes that foundation — a tag-referenced, structured operations data layer — into live operational intelligence and digital-twin capability.
Why Plant Knowledge Management Needs AI
Operating plants accumulate critical knowledge over decades — but the way that knowledge is stored makes it functionally inaccessible at the moments it matters most. Manual document management cannot solve this; structural change is required.
Without Asset Intelligence — The Risks
With Wrench AI — The Result
Everything You Need to Know
What document formats can the agent ingest?▾
Any format — PDF, Word, Excel, TIFF, scanned drawings, hand-annotated images, and legacy image formats. OCR is applied automatically to non-searchable content like title blocks, annotation fields, and equipment schedules. Paper documents are also supported via the physical-document digitisation pipeline. No pre-processing or manual conversion required.
Does the agent make operational decisions on its own?▾
No. The Asset Intelligence agent retrieves, synthesises, and presents — engineers verify and act. Human supervision is built into the architecture: engineers validate AI-extracted metadata, approve AI-generated instruction manuals before they become controlled documents, and retain authority over all operational and safety decisions. The AI provides the information; engineers make the call.
How are equipment tags identified and linked across documents?▾
The AI identifies equipment tag numbers wherever they appear across every document — drawings, maintenance records, inspection reports, vendor manuals — and builds a bidirectional reference in a knowledge graph. Every tag knows all its associated documents; every document knows all the tags it references. Cross-document relationships are then built on top: P&ID references, datasheet links, and spare-parts ties to equipment.
How does AI instruction-manual generation work?▾
Generative AI synthesises content from multiple source documents — vendor manuals, operating procedures, inspection records, and maintenance histories — into a coherent, current instruction manual for any equipment item. Source references are preserved. Engineers review and approve before any manual is issued as a controlled document. This is how institutional knowledge that would otherwise leave with retiring staff gets preserved.
How does it fit with the rest of the Wrench AI suite?▾
Asset Intelligence handles operating-plant knowledge retrieval. Its siblings in the WAA suite cover capital-project domains: the AI Analyzer (conversational queries on PMIS data), the AI Control Tower (predictive project governance), and the AI Reviewer agents (Technical Reviewer for engineering documents, Bid Analyzer for tenders, Contract Review for commercial governance). All sit on top of Wrench SmartProject PMIS as a single integrated suite.
Is the agent useful before our document library is fully cleaned up?▾
Yes — that is exactly its purpose. The agent is designed to ingest your library as it currently exists: inconsistent filenames, missing metadata, TIFFs alongside modern PDFs, paper records mixed with digital. Its first job is to extract structure from that chaos. There is no clean-up prerequisite; the agent does the clean-up as it ingests.
See How It Works on Your Plant's Document Library
Bring us a sample of your legacy document challenge — scanned drawings, unstructured archives, equipment without tag references — and we'll demonstrate how the Asset Intelligence agent processes it. Live, in 30 minutes.