In the world of advanced manufacturing, precision isn't just for parts—it applies to data too. As we release our latest Vault update for Multiaxis Intelligence, one message has become increasingly clear:
AI is only as good as your data.
Garbage In, Garbage Out—Especially with AI
It's easy to assume that if a human can read it, AI can too. But that's a dangerous assumption. Just because a PDF looks legible to us doesn't mean it's usable by an AI model.
On the left is a scanned document. On the right is the OCR-extracted text used by AI. While it may appear readable, the formatting, context, and semantic structure are often broken, leading to misinterpretations or no recognition at all.
This is why our Vault system now includes intelligent OCR-based preprocessing—but even then, the results are only as good as the source.
What's New in the Vault Update
Our latest release includes a dual-mode Vault system:
- Local Vault – Supports Markdown (MD), PDF, TXT, and HTML with size limitations to ensure fast, safe, and structured indexing.
- Cloud Vault – Available to select customers. Offers assistant-linked, vectorized search, with direct embedding and advanced retrieval options.
Why Markdown? Why Now?
We've added a Markdown Intermediate Converter for all local users to preview and enhance document quality before indexing. This step is critical for reducing noise, catching formatting issues, and improving relevance scoring.
Markdown may feel like a "developer thing"—but it's not. It's the language of structured clarity for AI.
We've benchmarked many files across our vaults and the results are staggering:
Markdown provides a semantic backbone—headings, lists, code blocks, even embedded images—all in clean, parseable text. This structure directly enhances embedding quality and AI comprehension.
For the Technical Crowd
Behind the scenes, here's what happens:
1. Conversion
Files go through OCR (if needed), and are parsed into markdown with configurable chunk sizes.
2. Relevance Evaluation
Our "Arlo Vault Chain of Thought" logic evaluates content worth indexing.
3. Embedding & Indexing
Content is semantically embedded and stored for fast AI retrieval.
4. Query Handling
Your question is matched to high-relevance vault content before hitting the model.
Whether you're using the local vault manager or the cloud vault assistant via vector stores, the data flow only works when the input is clean and meaningful.
The Data Quality Impact
Poor Quality Input: Scanned PDFs with broken formatting, unclear structure, and OCR errors lead to confused AI responses and missed information.
High Quality Input: Structured Markdown with clear headings, proper formatting, and semantic organization results in precise, relevant AI responses.
- Structured Content - Headings, lists, and formatting provide context clues for AI understanding
- Clean Text - OCR preprocessing eliminates scanning artifacts and formatting errors
- Semantic Organization - Logical document structure improves embedding quality
- Reduced Noise - Filtering irrelevant content before indexing improves relevance scoring
Vault System Benefits
Multi-Format Support
Handle MD, PDF, TXT, and HTML files with intelligent preprocessing for optimal AI comprehension.
Local or Cloud Options
Choose between local privacy or cloud-powered advanced search based on your security needs.
Fast Retrieval
Optimized indexing and embedding ensure quick, relevant responses to your queries.
Smart Relevance
Arlo's Chain of Thought logic ensures only the most relevant content gets indexed and retrieved.
Final Thought
If you want smarter answers from your AI, start with smarter documents.
Let's NOT feed it 100MB image scans with zero structure and expect magic.
Instead, let's embrace structure, clarity, and intention—one document at a time.