I Built Verto

tl;dr - this week I built and open sourced Verto, a utility library that watches folders and automatically converts documents to Markdown for AI agents.

1. The broader problem - enterprise knowledge context #

• Since January, I've been working on applied AI at Spectrum Equity.
• One of my focuses has been making the firm's internal knowledge and context accessible to AI agents.
• This isn't a novel problem: everyone recognizes the opportunity, but solving it “well” is hard.

2. What I think a good solution looks like (what “solving it well” means) #

• Andrej Karpathy's LLM Wiki is a seminal post in the emerging “enterprise knowledge management for AI” space. It describes something close to my ideal end state: continuously transforming an organization's raw information into a useful, AI-native knowledge base.
• I've spent much of this year looking for a startup or managed solution that could implement this vision in an enterprise setting.
• I have a few strong preferences about how this should work:
  • Portability: Outputs should be standard, portable files rather than proprietary representations.
  • Transparency: The transformation process should be understandable, with explicit logs.
  • Configurability: The entire pipeline should be configurable, ideally through explicit, stateful configuration files.
  • Minimal vendor lock-in: I should be able to change providers or tools without rebuilding everything.
• Despite the number of startups pursuing enterprise knowledge management, I haven't yet found a solution that nails things on all these fronts & I'm comfortable fully committing to.

3. The pragmatic, 80/20 approach #

• In the meantime, we still need our agents to leverage the firm's internal knowledge. I don't want perfect to be the enemy of good.
• Our approach has been to identify the 20% of documents that contain roughly 80% of the relevant institutional context and co-locate them in a shared drive.
• I've written agent skills that explain where these documents live and when they're relevant.
• Rather than building or purchasing an elaborate knowledge infrastructure, this gives us much of the utility with a fraction of the complexity.
• This approach has also been reinforced by a broader shift this year (2026) in how agents retrieve information:
  • Agents have become increasingly capable of navigating files and using basic tools like grep ,find , andcat .
  • Cursor moved away from semantic codebase indexing in favor of grep-based retrieval .
  • Mintlify adopted a filesystem-style interface that lets agents search and explore documentation, rather than relying solely on traditional top-K retrieval.
• Agents have become increasingly capable of navigating files and using basic tools like
• These developments make me increasingly comfortable with the idea that a well-organized collection of files, plus competent agents, can deliver much of the value of a more elaborate RAG system.

4. The remaining gap #

• Many of our most valuable internal documents are PDFs (unsurprisingly, given that we're an investment firm).
• Models are increasingly capable of reading PDFs directly, but it's more efficient to have the content available as Markdown rather than requiring repeated parsing.
• We'd already centralized the documents and built the agent skills. What was missing was a lightweight utility that would continuously convert new PDFs into Markdown.

- Solutions like [Reducto](https://reducto.ai/) and[Extend](https://www.extend.ai/) already do excellent document parsing. I wanted a lightweight utility that would continuously orchestrate them.
- I found an open-source project called [drop2md](https://github.com/docdyhr/drop2md) that came close, but I wanted two things it didn't offer:

• Multiple explicitly configured directories to watch and process independently.
• Integration with commercial parsing APIs like Reducto and Extend, rather than being limited to local conversion.
• I couldn't find anything that combined these capabilities with the simplicity, transparency, and configurability I wanted.

5. So I built Verto #

• I spent a couple of days creating Verto, a lightweight utility that scratches this exact itch.
• Verto continuously converts documents to Markdown across multiple specified directories, using interchangeable parsing providers.
• A few deliberate design choices:
  • Configuration: Everything is configured through JSON files that live alongside the source documents.
  • Directory selection: Users can explicitly configure multiple directories to watch and process continuously.
  • Adjacent outputs: Converted Markdown is stored in_markdown folders next to the source documents.
  • Logging: Processing activity is explicitly logged in JSONL files.
  • Provider flexibility: Parsing providers are interchangeable, with current support for Reducto and Extend and the ability to add tools like Docling.
• It's deliberately simple, configurable, and transparent.
• Verto isn't an attempt to solve the entire enterprise knowledge problem. It's a small piece of infrastructure that solves an immediate need without locking us into a particular vision for the rest of the stack.

See Verto here: https://github.com/jdamon96/verto.