Building a Technical Literature Dashboard with Power Automate Power Apps LDX hub StructFlow

Creation of TechLit Viewer, a technical literature management system built using a no-code/low-code stack that combines LDX hub StructFlow for AI-powered data extraction from PDFs, patents, and reports with Microsoft 365 tools like SharePoint, Power Automate, and Power Apps. The system automatically extracts eight structured fields from documents, stores them in SharePoint lists, and displays the data through both a Power Apps interface and a standalone HTML dashboard featuring interactive charts. In testing across 18 documents, the system achieved 83% extraction accuracy, with the remaining inconsistencies attributed to mixed-language issues in the field for major technology domain.

Verification Case Report 006 — 2026.05.18 | Time spent: ~3 days Overview | Metric | Value | |---|---| | Documents processed | 18 | | Power Apps screens | 4 | | Power Automate flows | 2 | | StructFlow extracted fields | 8 | We built TechLit Viewer — an end-to-end technical literature management system that uses LDX hub StructFlow to extract structured data from documents, Power Automate to keep everything updated, and both Power Apps and a standalone HTML dashboard for visualization. Background Many customers express a need to continuously collect and evaluate technical literature across diverse fields. The old approach — storing PDFs and patent documents in folders — made it nearly impossible to answer questions like "which technologies are at which maturity level?" or "which documents are most relevant to our business?" We set out to verify whether a practical technical literature management system could be built within a no-code/low-code stack by combining LDX hub StructFlow for AI extraction with Microsoft 365 tools for storage, automation, and display. What we're testing:Can StructFlow turn unstructured technical documents PDFs, patents, reports into structured data that integrates cleanly with SharePoint, Power Automate, and Power Apps? System Architecture: 5 Layers // TechLit Viewer — System Overview 1 INPUT Source files PDF / patents / reports └─ Stored in SharePoint document library. 18 documents total. 2 AI EXTRACTION LDX hub StructFlow └─ Auto-extracts 8 fields: Title / DocType / Authors / Year / FieldMajor / TRL / RelevanceScore / Summary 3 DATA LAYER SharePoint Lists TechLit Master / TechLit Metrics └─ Power Automate writes; Power Apps + HTML dashboard reads. 4 AUTOMATION Power Automate 2 flows └─ ① Auto-extract on item update TechLit Pipeline UPDATE ② Manual bulk processing TechLit BulkUpdate 5 DISPLAY Power Apps TechLit Viewer + HTML dashboard └─ 4-screen Power Apps + standalone HTML dashboard Flow Design: Two Flows for Two Purposes ① TechLit Pipeline UPDATE always-on Fires automatically when a SharePoint list item is updated. Detects new document registrations or changes to existing records, sends the file to StructFlow, and immediately writes the extracted results back. Day-to-day document additions are fully automated via this flow. ② TechLit BulkUpdate manual trigger A manual-trigger flow for bulk processing all 18 documents. Used after schema changes, prompt revisions, or for initial data loading. Iterates through all items with a foreach loop, waits for StructFlow polling to complete, and writes results back to the list. Key design insight:The update trigger excels at immediacy but isn't suited for full reprocessing. The manual trigger handles bulk operations well but requires operator action. Using both flows together covers both daily operations and administrative tasks. StructFlow Extraction Schema: 8 Fields Schema design started from the question: "what do we want to decide?" — not "what can we extract?" | Field | Description | Type | |---|---|---| | Title | Document title | string | | DocType | Document type patent / paper / report / other | string | | Authors | Authors / applicants | string | | Year | Publication / filing year | integer | | FieldMajor | Primary technology domain Materials Science, Energy Engineering, etc. | string | | TRL | Technology Readiness Level 1–9 | integer | | RelevanceScore | Relevance to our business high / medium / low | string | | Summary | 2–3 sentence technical summary | string | About TRL Technology Readiness Level :Originally developed by NASA and adopted by EU Horizon programs. Levels 1–3 = basic research, 4–6 = demonstration, 7–9 = operational/production. Quantifying maturity makes it easy to distinguish research-stage from production-ready technologies at a glance. Power Apps: 4 Screens for Different Use Cases | Screen | Features | |---|---| | Screen 1: Search | Free-text search by title, author, or domain. Filtering by DocType, TRL, RelevanceScore. Real-time queries against SharePoint list. | | Screen 2: Detail View | Full field display for individual documents. Review StructFlow-extracted Summary. Visual TRL and relevance indicators. | | Screen 3: Metrics Comparison | TRL distribution by technology domain. Document count by relevance. Year-over-year trend charts. | | Screen 4: Tech Dashboard | Advanced visualization via embedded HTML component. Dynamic charts with Chart.js. Integrated full-text search over all 18 documents. | HTML Dashboard: Standalone Strategic View Alongside Power Apps, we built a standalone HTML dashboard techlit dashboard.html that runs entirely in the browser — no Power Apps dependency. This makes it easy to share with executives or external stakeholders. js // StructFlow extraction results stored directly as a data array const data = { id: 17, title: 'Immobilized Photocatalyst Paper', docType: 'patent', authors: ' Author name masked ', year: 2002, fieldMajor: 'Chemistry', trl: 4, relevance: 'high', url: '' // SourceFileUrl — will become a live link once populated }, // ... 18 documents total ; The dashboard includes 4 charts technology domain distribution, TRL distribution, year-over-year trend, document type breakdown plus a searchable full-document table. Passing StructFlow's structured data to Chart.js generates all analysis charts automatically. Results StructFlow Extraction Accuracy 15 of 18 documents 83% had all fields extracted correctly. The remaining 3 showed mixed-language inconsistencies in FieldMajor English vs. Japanese — no impact on usability, but a clear prompt improvement opportunity. Power Automate Automation Average processing time per document: ~67 seconds including StructFlow polling . Full run of all 18 documents: ~20 minutes. Both scheduled and trigger-based execution confirmed stable. Technology Domain Visibility Environmental Science was the largest category 6 documents , followed by Materials Science 4 . TRL distribution: basic research 1–3 was highest at 8 documents; operational stage 7–9 had 3. For the first time, we had a quantitative picture of the entire portfolio. Relevance Scoring 15 of 18 documents scored RelevanceScore = High . By embedding our business context translation & localization into the StructFlow system prompt, we achieved automatic screening aligned with our actual evaluation criteria. Biggest insight: from "searching" to "discovering" Technical documents that could previously only be searched by filename can now be filtered across TRL, technology domain, and relevance simultaneously. Being able to instantly narrow down to "TRL ≥ 4 AND Materials Science AND High relevance" fundamentally changes the speed of technology strategy decisions. Lessons Learned 1. Lock down SharePoint column types before you start Column types hyperlink vs. single line of text can't be changed after the fact. Confirm the format Power Automate will write, then finalize column design before building. 2. Design the StructFlow schema from your decision criteria Don't start from "what can we extract?" — start from "what do we need to decide?" Including evaluation-axis fields like TRL and RelevanceScore from the beginning dramatically increases dashboard value. 3. Separate Power Apps and HTML dashboard by purpose Power Apps suits day-to-day search and update operations; the HTML dashboard suits sharing and presentations. Using two UIs on the same data source expands both the audience and the use cases. 4. Control FieldMajor normalization via the prompt Mixed English/Japanese values like "Materials Science" and "材料科学" split aggregations. Adding an explicit instruction to the StructFlow system prompt "always output technology domains in English" prevents this drift. Next Steps | Item | Details | |---|---| | Populate SourceFileUrl | Add source URLs to the SharePoint list so users can reach original documents in one click from the dashboard | | Schedule automation | Move from manual triggers to fully automated monthly runs with auto-distribution to leadership | | Normalize FieldMajor | Add English-only instruction to StructFlow prompt and reprocess all 18 documents | | Scale to 100+ documents | Add ExtractDoc as a pre-processing step to handle scanned PDFs | | RefineLoop integration | Auto-translate and improve summaries of foreign-language documents English, Chinese into Japanese | Kawamura International is a translation and localization company documenting its AI process experiments in public. StructFlow, RefineLoop, RenderOCR — and whatever comes next.