Improved retrieval relevance
More meaningful document chunks and technical fields can improve what the AI system retrieves.
IRISX / AI Acceleration
AI ACCELERATIONAI runs on data.
AI runs on data.
Engineering AI needs context.
Give RAG systems, technical copilots, search applications and AI agents better-prepared engineering information—not just disconnected text extracted from PDFs.
ENGINEERING AI · WORKFLOW CONCEPTIllustrative
Which drawing characteristics changed in Revision B?
AI-ready data view · concept
Structured revision information could link changed characteristics to drawing locations and reviewer evidence.
Illustrative source: Drawing Rev B → characteristics → change record
THE PROBLEM
An LLM cannot recover context it never received.
Generic document ingestion may recognize words while losing associations between technical objects, tables, diagrams, revisions and source evidence.
COMMON BASELINE
Raw PDFs → text chunks → RAG
- Callouts separated from the right features.
- Tables split into meaningless lines.
- Revision context lost during indexing.
- Technical relationships left implicit.
- Answers difficult to trace or verify.
These are common risk scenarios, not claims that every generic parser fails.
IRISX-ENHANCED APPROACH
Technical content → contextual data → AI
- Capture relevant technical elements.
- Preserve document structure where supported.
- Attach document, page and revision context.
- Represent selected relationships.
- Support source-linked, reviewable AI workflows.
Target architecture; each integration must be implemented and evaluated.
THE INTELLIGENCE LAYER
From technical records to AI-ready knowledge.
IrisX is the preparation layer—not a claim to replace the customer’s LLM, vector database, PLM or enterprise search product.
INPUT
Engineering documentsDrawings · specifications · scansIRISX
Technical content intelligenceExtract · review · structureOUTPUT
Structured technical recordsFields · metadata · source linksINTEGRATE
RAG / AI agents / analyticsCustomer-specific connectorsPOTENTIAL OUTCOMES
The data-layer advantage for enterprise AI.
Benefits to validate during a proof of concept—not guaranteed performance claims.
Clearer answer evidence
Source references and characteristic relationships can help users assess an AI response.
Less one-off preparation
Reusable technical records can reduce repeated document cleanup for future applications.
More practical AI agents
Agents can work with organized records rather than re-reading long technical PDFs for every action.
Faster application prototyping
Structured inputs can speed integration experiments and custom workflow development.
Sensitive-data control
Private processing can be paired with approved local AI infrastructure where implemented.
EXAMPLE CUSTOM PROJECT
Trace a requirement through engineering decisions.
Imagine an AI assistant asked which inspection records could be affected by a change to a pressure specification.
- Find the pressure requirementExtract the relevant clause from a source specification.
- Identify linked technical recordsConnect references to drawings, characteristics, and inspection documents.
- Generate a reviewable assessmentSummarize possible impacts with links to supporting source evidence.
- Engineer approvesUse human validation for safety- and quality-critical decisions.
Custom solution concept — not a shipped assistant
ILLUSTRATIVE SOURCE CHAIN
“Operating pressure: 150–200 psi”
Associated component and reviewed characteristic
Linked verification or inspection evidence
Synthetic data and links for conceptual demonstration only.
PROVE THE IMPACT
Start with a measured AI data-readiness pilot.
Rather than claim that content intelligence always improves AI accuracy, establish a baseline and evaluate improvement on representative engineering documents.
Extraction fidelity
Assess technical values and structural preservation.
Answer grounding
Measure evidence links and supported answers.
Review time
Compare effort required to validate outputs.
Processing efficiency
Track throughput, cost, and repeatability.
FROM OCR TO DOMAIN CONTEXT
A more useful foundation for RAG and engineering agents.
The source document provides the text, but the part, revision, BOM position and related decision explain what the information means. Structured connections create a richer retrieval and reasoning layer.
See the connected-context concept →
Illustrative architecture; performance benefits must be measured on real customer workflows.
START WITH YOUR DOCUMENTS
Better AI begins with better engineering data.
Bring a technical-document use case and let us define a proof-of-concept with measurable extraction and AI-readiness criteria.