Build a Low-Code Internal Tool in Three Days

A founder constructed a technical literature management system in approximately three days, processing 18 documents with AI extraction and Microsoft 365 tools. This playbook details the five-layer architecture and dual automation flows.

A founder spent approximately three days building TechLit Viewer, a technical literature management system capable of processing 18 documents and extracting 8 structured fields. This low-code solution, detailed in a dev.to post, integrated LDX hub StructFlow for AI extraction with Microsoft 365 tools like Power Automate, Power Apps, and SharePoint. The project aimed to verify if a practical system could be constructed quickly using a low-code stack to address the challenge of evaluating diverse technical literature.

Founders often struggle with unstructured technical documents, making it difficult to answer specific questions about technology maturity or business relevance. The TechLit Viewer project demonstrated a repeatable approach to converting PDFs, patents, and reports into structured, queryable data using readily available low-code platforms.

System Architecture: Five Layers for Data Flow

The TechLit Viewer system operates on a five-layer architecture, designed for end-to-end technical literature management. This structure moves documents from raw input to a displayable dashboard. The initial input layer consists of source files—PDFs, patents, and reports—stored within a SharePoint document library. For the verification case, 18 documents were processed through this system.

The second layer handles AI extraction, utilizing LDX hub StructFlow. This component automatically extracts eight specific fields from each document: Title, DocType, Authors, Year, FieldMajor, TRL (Technology Readiness Level), RelevanceScore, and Summary. These fields were chosen based on the question, "what do we want to decide?" rather than simply "what can we extract?", guiding the schema design toward actionable insights.

The extracted data then flows into the third layer, the data layer, which comprises SharePoint Lists. Specifically, two lists, TechLit_Master and TechLit_Metrics, store the structured information. Power Automate writes data to these lists, while Power Apps and a standalone HTML dashboard read from them for visualization. This separation ensures data integrity and accessibility for different display interfaces.

Automation: Dual Power Automate Flows for Operations

The fourth layer, automation, is powered by two distinct Power Automate flows, each serving a specific operational purpose. The first, TechLit_Pipeline_UPDATE, is an always-on flow triggered automatically when a SharePoint list item is updated. This flow detects new document registrations or changes to existing records, sends the file to StructFlow for processing, and immediately writes the extracted results back to the SharePoint lists. This automates day-to-day document additions and updates.

The second flow, TechLit_BulkUpdate, is designed for manual trigger and bulk processing. This flow is used after schema changes, prompt revisions, or for initial data loading, such as processing all 18 documents at once. It iterates through all items using a foreach loop, waits for StructFlow polling to complete, and then writes the results back to the SharePoint list. This dual-flow strategy ensures both immediate processing for individual updates and efficient handling of larger administrative tasks.

Display: Accessible Dashboards for Insights

The final layer, display, provides user interfaces for interacting with the processed technical literature. This includes a Power Apps application, TechLit_Viewer, which features four distinct screens for navigation and data presentation. Additionally, a standalone HTML dashboard offers an alternative visualization method. These interfaces allow users to query the structured data, enabling them to answer questions about technology maturity levels or document relevance, which was the initial problem statement for the project.

What We'd Change

The TechLit Viewer project demonstrates a functional approach to low-code internal tool development, but its specific implementation presents areas for modification when scaling or adapting to different organizational contexts. The reported development time of approximately three days for 18 documents suggests a significant per-document setup or configuration overhead. For organizations managing hundreds or thousands of technical documents, this initial time investment would need to be substantially reduced through more generalized ingestion pipelines or pre-configured extraction templates.

Reliance on the Microsoft 365 ecosystem, while convenient for existing enterprise users, introduces potential vendor lock-in. Founders outside this ecosystem would face integration challenges or require alternative services for storage, automation, and display. Exploring more open-source or platform-agnostic alternatives for data storage and workflow automation could mitigate this risk, offering greater flexibility and potentially lower long-term costs.

The manual TechLit_BulkUpdate flow, while necessary for schema changes, indicates an administrative burden. For continuous improvement of extraction models or frequent schema adjustments, a more automated or scheduled bulk reprocessing mechanism would be beneficial. This could involve event-driven triggers for schema updates or a scheduled nightly job to re-evaluate documents against the latest extraction logic, reducing the need for operator intervention.

Landing

The TechLit Viewer project validates the potential of low-code platforms for rapid internal tool development, specifically for transforming unstructured data into actionable insights. The systematic application of AI extraction, coupled with targeted automation, provides a blueprint for founders seeking to build similar data management systems without extensive custom coding. This approach offers a tangible path to addressing specific operational inefficiencies, provided the underlying platform dependencies and scalability considerations are thoroughly evaluated against an organization's long-term needs and technical strategy.

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