Observed Signal · May 18, 2026 · Technical Implementation · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
TechLit Viewer: StructFlow + Power Apps Dashboard
The author built TechLit Viewer, an end-to-end technical literature management system combining LDX hub StructFlow for AI extraction with Microsoft 365 tools (SharePoint, Power Automate, Power Apps) and a standalone HTML dashboard using Chart.js. The system processed 18 documents, auto-extracting eight fields (Title, DocType, Authors, Year, FieldMajor, TRL, RelevanceScore, Summary). Automation uses two Power Automate flows (an always-on update trigger and a manual bulk-processing flow). StructFlow extracted all fields correctly for 15 of 18 documents (83% accuracy); average processing time was ~67 seconds per document and ~20 minutes for a full run. Lessons include locking down SharePoint column types, designing schema from decision criteria (including TRL and RelevanceScore), and normalizing domain values via system-prompt instructions. Next steps list reprocessing, scaling to 100+ documents, adding OCR pre-processing, and integrating RefineLoop for translations.
Practical case study showing integration of AI extraction (StructFlow) with Microsoft 365 automation and low-code UIs—useful as a reference for teams building document-intelligence pipelines but not a major platform announcement.
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Key Takeaways & Evidence Grounding
- TechLit Viewer built to manage technical literature using StructFlow, SharePoint, Power Automate and Power Apps
- 18 documents processed; StructFlow extracts 8 fields (Title, DocType, Authors, Year, FieldMajor, TRL, RelevanceScore, Summary)
- StructFlow extraction accuracy: 15 of 18 documents (83%) had all fields extracted correctly
- Automation uses two Power Automate flows: TechLit_Pipeline_UPDATE (automatic on item update) and TechLit_BulkUpdate (manual bulk processing)
- Average processing time ≈ 67 seconds per document; full run of 18 documents ≈ 20 minutes
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