MOI preserves tables, flowcharts, charts, SmartArt, and embedded images across PDF and Office files, creating structured, traceable inputs for knowledge bases and Agents.
The client is a leading wafer foundry whose process documentation, equipment and process data and historical work orders were spread across systems, so finding one piece of process information meant searching several of them.
Semiconductor process documents are dense with information, and the information that matters is precisely what is not in the body text. Process parameters live in tables, sequences in flowcharts, trends in charts, hierarchies in SmartArt, and a great deal more in embedded images that the surrounding prose never explains.
Generic document parsing distorts this kind of content: tables spanning pages are split apart, merged-cell relationships are lost, flowcharts are flattened into unordered text fragments, charts survive only as axis labels. None of this raises an error — the distortions enter the knowledge base looking perfectly plausible.
For an agent this is more dangerous than a parsing failure. A failure is visible; a distortion is not. It resurfaces as a confidently worded wrong answer, and in a process context a wrong parameter is not a user-experience problem.
MOI strengthened structure preservation across PDF, Word, PowerPoint and Excel, separating tables, images and headings into distinct content blocks rather than flattening them into one continuous stream of text.
Structural reconstruction keeps table rows, columns and merges as HTML while retaining the original image as a fallback. Where reconstruction confidence is low, the system degrades conservatively — handing back the original image and its source location instead of a structured result that may already have drifted.
Every content block carries provenance: which document, which page, which position. An answer the agent gives can therefore be traced back to an exact place in the source.
Parsed output feeds the process knowledge graph and semantic retrieval that together form the agent's data foundation, with visibility governed by permission and confidentiality tiers.
Tables, flowcharts, charts and embedded images are parsed with their structure intact, so the agent reads something close to the original meaning rather than a flattened approximation.
Conservative degradation ensures the system exposes uncertainty instead of generating a confidently worded wrong conclusion — the single most important boundary in a process context.
Provenance lets engineers check an answer, so agent output can be verified rather than merely trusted.
Cross-system document search time falls, process knowledge accumulates in reusable form, and handover between shifts no longer depends on what someone remembered to say.