How KnowledgeBuilder Compares to Conventional and RAG Database Building Methods

Most architecture and engineering firms store information in scattered formats – documents, drawings, spreadsheets, emails, and shared drives. Databases, when they exist, are often built manually with a rigid schema defined early in a project. That means only predefined fields (e.g. project location) are captured, making it hard to adapt if new information needs arise later. As a result, much of a project’s data remains underused because it stays unstructured.

Indeed, analysts estimate that 80-90% of enterprise data is unstructured – a huge trove of knowledge not being fully exploited. Clearly, better methods are needed to turn these archives of files into usable insights.

This article compares three approaches to building and querying project databases: the conventional method (traditional structured databases), RAG systems (AI-driven search over existing data), and Tektome’s KnowledgeBuilder. We’ll examine how each works, their pros and cons, and why KnowledgeBuilder offers a fresh solution for organisations looking to make the most of their data.

Conventional Method

The conventional approach uses a structured relational database set up by IT with predefined tables and fields. This mature, familiar method works well for predictable, structured data that fits neatly into rows and columns.

However, conventional databases have major limitations. The schema is fixed upfront, so you’re limited to the fields decided at the start – adding a new field later is difficult without redesign. These systems also rely on IT support for changes and queries, which can slow down access to information. Data updates or extractions might only happen infrequently via batch processes. And critically, a traditional database can’t handle unstructured content (like PDFs or images) without someone manually entering that information. Researchers note that building a knowledge base this way is “time-consuming and expensive” due to all the human effort required. In short, conventional databases are reliable for structured data, but they’re rigid and leave a lot of valuable information on the sidelines.

RAG System

A RAG system (Retrieval-Augmented Generation) is an AI-driven approach that finds answers from your existing data on the fly. You can ask questions in natural language, and the system retrieves relevant documents from your files and generates a direct answer.

The big advantage of RAG is convenience. Anyone can query information in plain English and get quick answers without needing technical skills. This makes it great for quickly finding specific details in a mountain of documents.

The main drawback is that RAG doesn’t organise your data – it only uses what’s been fed into it. It provides no new structure or insights, acting “only [as] a database” of existing content. If your data is incomplete or messy, RAG won’t fix that. Moreover, the answers it gives are limited to what it can retrieve, and results may be inconsistent if the underlying information is inconsistent (the AI might pull different sources each time). RAG is a handy query layer, but by itself it won’t build a unified knowledge base for you.

KnowledgeBuilder

Tektome KnowledgeBuilder is a next-gen solution that automates structuring of unstructured data using AI. Instead of relying on IT to predefine fields, users can simply give natural-language prompts for what information they want to extract. The system then scans documents, drawings, and other files to pull out those details and populates a searchable database.

The key benefit of KnowledgeBuilder is flexibility. Unlike a rigid schema, you can add new data fields at any time as the project evolves. In other words, users themselves can configure new items on the fly. The AI handles the heavy lifting – reading PDFs, images, etc. – and turns that unstructured content into organised data. This vastly saves time and reduces human error. It also supports a wide range of file formats and layouts, so it’s not thrown off by different document styles.

There are some considerations. Setting up KnowledgeBuilder requires initial training and adjustment – the team needs to learn how to write effective prompts and integrate the tool into their workflow. And it works best when adopted across the organisation, so that you build a truly comprehensive knowledge base rather than isolated pockets. With that commitment, however, KnowledgeBuilder can turn previously idle data into a powerful, structured resource.

Conclusion

Conventional databases need IT support and only handle predefined fields. RAG systems add convenience but do not impose any structure on new data. In contrast, KnowledgeBuilder lets users flexibly add new fields and perform extractions on demand (simply by uploading documents).

Below is a summary of the pros and cons of each approach:

Method	➕ Pros	➖ Cons
Conventional	Mature and familiar; works well for structured, predictable data.	Rigid schema (fixed fields); needs IT support for changes; slow to update; poor at handling unstructured data.
RAG System	Easy natural-language querying; quick answers from existing info.	No data structuring or new insights; limited to provided data; results can be inconsistent over time.
KnowledgeBuilder	User-driven configuration (no coding); can add new fields anytime; auto-structures content from files; saves time and reduces errors.	Initial setup and training needed; best results when adopted across the whole organisation (requires commitment).

In summary, KnowledgeBuilder combines the reliability of structured databases with the ease of AI-driven search. It addresses the rigidity of conventional methods and the shortfalls of RAG by providing a flexible, automated way to build a knowledge base. For firms awash in underutilised documents and data, it can be a game-changer.

Ready to unlock your organisation’s hidden knowledge? Learn more about KnowledgeBuilder or start a free trial programme and turn your past project knowledge into actionable insights today!