Meet LOGOS: Stop Managing Files. Start Understanding Them.

Imagine you're facing a mountain of data:

• 500 user interview transcripts
• 1000 customer feedback emails
• 200 industry research reports

You know these files contain "gold," but the question is: where do you even begin?

What you truly want to uncover isn't just how many times a specific word (like "bug") appears. You want to find the core patterns you don't yet know, hidden within the lines. For instance:

• "Why are users raving about Feature A, while silently abandoning Feature B?"
• "When customers complain about 'price,' are they truly upset about the 'sticker price' or the 'perceived value'?"
• "Across all our failure cases, is there a common 'root cause' we've never identified?"

These are questions you can't answer with a simple "Ctrl+F" search.

The Traditional Way: Costly, Slow, and Subjective

Historically, answering these "why" questions relied on an academic method called "Grounded Theory."

In essence, it involves hiring one (or several) expensive domain experts to:

Read: Go through every single document, word by word.

"Code": Tag relevant segments with descriptive labels, like putting sticky notes (e.g., "confused by UI," "data security concerns").

"Cluster": Lay out hundreds of these "sticky notes" and start finding connections, merging similar ideas (e.g., "confused by UI" and "can't find button" both fall under "usability issues").

"Theorize": Finally, organize these clustered tags into a logical, hierarchical "insight map."

This process is incredibly time-consuming (weeks or even months), expensive (expert time is precious), and highly subjective. A different expert might produce an entirely different "map."

This is why, despite everyone knowing the importance of "qualitative analysis," most organizations ultimately choose to forgo it.

UniDrive's Core Engine: How LOGOS Empowers AI to Be a Research Expert

What if we could teach AI this "expert-level" way of thinking?

This is precisely what one of our core technologies – LOGOS – accomplishes.

LOGOS is a cutting-edge AI framework developed by researchers from UC San Diego and other top institutions. It's not a simple "search tool"; it's a fully automated "theory-building engine."

It meticulously replicates the entire thought process of human experts, but does so faster, more objectively, and more comprehensively.

LOGOS's 4-Step Intelligent Workflow

When you entrust your files to UniDrive, the LOGOS engine springs into action in the background, working like a tireless team of expert researchers:

Step 1: The "Sticky Note Frenzy" (Open Coding)

The AI first reads through all documents, "tagging" (generating "codes" for) any information it deems valuable. At this stage, it prioritizes quantity and coverage, generating thousands of granular tags like "cumbersome checkout," "likes blue theme," or "mentioned competitor A."

Step 2: Smart "Connect the Dots" (Axial Coding)

Now, the AI looks at this wall of "sticky notes" and starts seeking out their "semantic connections." It will discover that "cumbersome checkout" and "payment button hidden too deep" are both discussing the same underlying theme. It then automatically clusters these disparate tags and generates a higher-level "conceptual tag," for example: "Purchase Friction."

Step 3: Building the "Insight Pyramid" (Selective Coding)

This is the crucial step. LOGOS doesn't just cluster; it actively understands the logical relationships between these "conceptual tags." It constructs a hierarchical structure, like a family tree (technically called a "graph"):

"User Complaints"
├── "Purchase Friction"
│   ├── "Cumbersome Checkout"
│   └── "Payment Button Hidden Too Deep"
└── "Functional Defects"
    ├── "App Crashes"
    └── "Data Sync Failure"

Step 4: Iterative Refinement

The most powerful AI isn't right the first time; it knows how to "reflect." After building the initial "pyramid," LOGOS re-reads the original text with this new "map" in mind, validating and refining its tags.

• "Is my 'Purchase Friction' tag too broad?"
• "Should 'App Crashes' and 'Data Sync Failure' be separate, as one is a 'stability issue' and the other an 'account issue'?"

It iteratively refines itself until it outputs a highly reusable, logically rigorous, and comprehensively covered "insight map" (in the paper, this is referred to as a "Codebook").

Why LOGOS Is Revolutionary

Because its "thinking" process fundamentally surpasses other AI tools on the market.

In a study on "why AI systems fail at math problems" (see Figure 4 in the paper), researchers compared LOGOS with traditional RAG (Retrieval-Augmented Generation) techniques:

Traditional RAG (Search-based AI) would answer: "Sorry, I can't find the word 'error' in the files." They can only retrieve, not infer.

LOGOS (Expert-based AI) would answer: "I analyzed all failure cases and summarized 12 core failure patterns for you, such as: 1. Misinterpreting constraints, 2. Confused proportional reasoning (e.g., mixing multiplication and addition), 3. Inconsistent units..."

This is the fundamental difference between "search" and "insight."

LOGOS Performance Dataset Comparison

Even more astonishingly, in a complex study (on multi-agent system failures), the insight map automatically generated by LOGOS achieved an 88.2% alignment with conclusions reached by a team of human top experts after weeks of work!

LOGOS Performance Breakdown

What This Means for You (as a UniDrive User)

We're incredibly excited about technologies like LOGOS and have integrated them as UniDrive's core engine because they unlock a completely new way to interact with your files.

When LOGOS runs within your UniDrive, it means:

• Truly "Intelligent" Organization: You no longer need to manually create complex folders. UniDrive will automatically "understand" your thousands of files and organize them for you with "smart tags" generated by LOGOS.

• "Semantic Search" Beyond Keywords: You can truly "ask questions." You don't need to search for "app crash"; you can directly ask: "What recent issues have we had with 'stability'?" UniDrive will instantly pull up all relevant files, paragraphs, and notes that LOGOS has categorized under "stability issues."

• Discovering the "Unknown Unknowns": UniDrive will proactively show you the "hidden patterns" it discovers. You might be surprised to find that three seemingly unrelated documents from "Sales," "Tech," and "Customer Service" are all pointing to the same "root problem."

Stop sifting for gold in your "pile of files."

It's time to let a true AI expert draw that treasure map for you.

Interested in the deep technical details of LOGOS? Feel free to read our whitepaper, or join the UniDrive community to ask our founders directly!

References

• Pi, X., Yang, Q., & Nguyen, C. (2025). LOGOS: LLM-Driven End-to-End Grounded Theory Development and Schema Induction for Qualitative Research. arXiv preprint. https://arxiv.org/pdf/2509.24294