Hosted Docling UI & Web Interface for Doc Conversion

FileDigest is a hosted web interface for Docling document conversion. You drop, paste, or choose a file in the browser, and FileDigest runs Docling on warm Modal L4 GPUs to produce AI-ready Markdown, JSON, DocTags, and retrieval chunks, with no local install, GPU setup, or model downloads required.

What a hosted Docling UI does for you

Docling is an excellent open-source document converter, but running it yourself means installing Python dependencies, downloading models, provisioning a GPU, and wiring up upload handling, storage, and job tracking. FileDigest packages all of that as a hosted product so you can use Docling from any browser.

Upload is one step. You drop a file onto the page, paste it, or choose it from disk, and processing starts automatically. There is no separate "process" button to hunt for. The app routes you straight to a live job view where you watch the conversion progress and then inspect the results.

The conversion engine is Docling running on warm Modal L4 GPUs. The converter and its models load once per warm container and stay resident, so repeat jobs do not pay the cold-start model-loading cost every time. That keeps interactive, back-to-back conversions fast.

File types you can convert

FileDigest accepts the document and data formats Docling handles, including:

• PDF (digital and scanned)
• DOCX, PPTX, and XLSX
• Images
• TXT, Markdown, HTML, and CSV
• ZIP bundles, so you can submit many sources in a single job

Scanned PDFs are detected automatically and OCR is applied without extra configuration. Optional enrichments let you convert formulas to LaTeX, extract code, and generate picture descriptions, and a high-accuracy VLM tier is available when you need the cleanest possible structure recovery.

Outputs you can actually use

Every job produces a combined digest.md and a manifest.json, plus a full set of per-source artifacts. For each source file you get:

• Markdown
• HTML
• Docling DocTags
• Docling JSON
• Heading-contextualized RAG chunks, ready for embedding and retrieval

You can view every output side-by-side with the original PDF, so you can confirm that tables, headings, and figures landed where they should before you ship the result into a pipeline. The combined digest.md gives people and LLMs one readable artifact, while manifest.json gives automation a structured record of what was produced.

An API for agents, not just a UI

The same conversion runs behind an agentic REST API, so what you test in the web interface is exactly what your code calls in production. Submit a job with POST /v1/parse and poll it with GET /v1/jobs/{id}. Authentication uses a Bearer key. The full contract is published as OpenAPI 3.1 at /openapi.json, errors follow the RFC 9457 problem+json format, and idempotency keys let agents retry safely without creating duplicate jobs. Agent-oriented documentation lives at /llms.txt so coding assistants can discover the API on their own.

Privacy and plans

Storage is private and per-user. Ownership checks gate every request, and downloads are served through private signed links rather than public URLs. FileDigest offers Free, Pro, and Business plans. Paid tiers add OCR, support larger jobs, and raise token quotas, so you can move from trying a single document to running it at scale.

FAQ

Is FileDigest a hosted version of Docling?

Yes. FileDigest runs the open-source Docling engine on warm Modal L4 GPUs and wraps it in a browser UI plus a REST API, so you get Docling conversions without installing the library, downloading models, or managing a GPU.

What output formats does the Docling UI produce?

Each job returns a combined digest.md and manifest.json. For every source file you also get Markdown, HTML, Docling DocTags, Docling JSON, and heading-contextualized RAG chunks, all viewable next to the original PDF.

Can it convert scanned PDFs and images?

Yes. Scanned PDFs are detected automatically and OCR is applied. Images are supported as direct inputs, and optional enrichments can convert formulas to LaTeX, extract code, and describe pictures.

Can I call FileDigest from code or an AI agent?

Yes. Use POST /v1/parse to submit a job and GET /v1/jobs/{id} to fetch results, with Bearer-key auth. The API ships an OpenAPI 3.1 spec at /openapi.json, RFC 9457 problem+json errors, idempotency keys, and agent docs at /llms.txt.