How to build a no-code tech literature dashboard with AI extraction

Many companies still manage technical literature—patents, research papers, industry reports—like it’s 2005. Files accumulate in SharePoint folders, valuable insights remain buried in PDFs, and answering basic questions about technology maturity or relevance becomes a manual slog.

To solve this, a recent project built TechLit Viewer, a low-code technical literature management system that automates data extraction, storage, and visualization using Microsoft 365 tools and StructFlow’s AI engine. Over three days, the team processed 18 technical documents—turning unstructured files into structured, searchable data—with full automation and real-time dashboards.

The Core Problem: Why Spreadsheets and Folders Fail

Traditional document management fails in three key ways:

• Search is broken: Locating relevant patents or scientific papers often means keyword fishing in folders or relying on metadata that hasn’t been updated.
• Context is lost: It’s hard to tell whether a technology is still in lab research (TRL 1–3) or ready for deployment (TRL 7–9) without manually reading each document.
• Insights are delayed: Teams can’t answer strategic questions—like which technologies are maturing fastest—without consolidating data from multiple sources.

The solution? Automate extraction, centralize data, and visualize trends in real time—all without writing code.

A 5-Layer Architecture for Automated Literature Intelligence

TechLit Viewer is built on a modular, cloud-native stack designed for scalability and low maintenance. The system spans five layers, each handling a specific stage of the content lifecycle.

1. Input Layer: SharePoint as the Central Repository

All source documents—18 in total—are stored in a dedicated SharePoint library. The team uses SharePoint’s metadata columns to track basic attributes like document type (patent, paper, report), allowing initial filtering before AI processing begins.

2. AI Extraction Layer: StructFlow for Smart Parsing

LDX hub’s StructFlow extracts structured data from unstructured technical documents using a custom schema. It automatically identifies key fields such as:

• title, authors, year
• docType (patent / paper / report / other)
• fieldMajor (scientific domain)
• trl (Technology Readiness Level, 1–9)
• relevanceScore (high / medium / low)
• summary (2–3 sentence technical abstract)

This schema wasn’t pulled from thin air. The team asked: “What decisions do we need to make?”—not “What can we extract?”—ensuring every field directly supports business intelligence.

3. Data Layer: SharePoint Lists as the Single Source of Truth

Extracted data is written to two SharePoint lists:

• TechLit_Master – stores core metadata for each document
• TechLit_Metrics – aggregates summary statistics (e.g., TRL distribution by domain)

These lists become the system’s single source of truth, enabling consistent querying across Power Apps and the HTML dashboard.

4. Automation Layer: Power Automate Handles the Heavy Lifting

Two Power Automate flows manage document processing:

• TechLit_Pipeline_UPDATE – triggered automatically when a SharePoint item is updated (e.g., new document uploaded). It sends the file to StructFlow and writes back extracted fields in under 67 seconds per record.
• TechLit_BulkUpdate – a manual flow used for initial data loads or schema changes. It reprocesses all 18 documents in ~20 minutes using a foreach loop and wait logic.

The pairing of real-time triggers and scheduled bulk processing ensures both operational efficiency and administrative flexibility.

5. Display Layer: Two Ways to View Insights

• Power Apps – a four-screen app with search, detail views, metrics charts, and an embedded HTML component for advanced visualization.
• Standalone HTML Dashboard – a lightweight, browser-based view (techlit_dashboard.html) for executives or external partners. Built with Chart.js, it renders dynamic charts directly from the extracted data.

Design Lessons: Why Two Flows Are Better Than One

The team discovered a critical trade-off:

• Real-time triggers are perfect for daily operations—new files are processed instantly.
• Manual reprocessing is essential when schema or prompts change—it ensures consistency across the dataset.

Using both flows together avoids the pitfall of trying to force one process to do everything. The update flow keeps the system current; the bulk flow preserves data integrity during revisions.

Results: Accuracy, Speed, and Domain Insights

After processing 18 documents—including patents and research papers—the system achieved:

• 83% extraction accuracy: 15 of 18 documents had all fields extracted correctly. The remaining three showed minor inconsistencies in fieldMajor due to mixed-language content (English/Japanese), highlighting a prompt-tuning opportunity.
• Consistent performance: Average processing time per document was ~67 seconds, with full bulk runs completing in ~20 minutes.
• Clear domain visibility: Environmental Science led with 6 documents, followed by Materials Science (4) and Energy Engineering (3). TRL distribution skewed toward early-stage research (1–3), with 8 documents at that level—ideal for identifying emerging opportunities.

What’s Next for Tech Literature Automation?

This project proves that no-code tools and AI extraction can transform fragmented technical literature into a living knowledge base. The next steps include:

• Expanding the dataset to hundreds of documents across multiple domains
• Adding natural language querying to search summaries at scale
• Integrating with internal wikis or knowledge graphs for deeper context

Businesses no longer need data teams or complex pipelines to turn documents into decisions. With the right schema, automation, and visualization, technical literature can become a strategic asset—updated in real time and accessible to everyone.

The future of knowledge work isn’t just about storing files. It’s about making them actionable.