mdstill VS ViewForge

mdstill's answer

mdstill is built specifically for LLM and RAG workflows, not generic file conversion. Drop any of 20+ document formats (PDF, Word, Excel, PowerPoint, EPUB, and more) and get back clean, structure-preserving Markdown that's tuned for ChatGPT, Claude, Gemini context windows and vector-database ingestion. Tables stay intact, headers become linkable anchors, output is ~40% more token-efficient than raw text extraction. Free web tool + REST API — humans and pipelines use the same engine.

ViewForge's answer:

Three things. (1) Data ownership: ViewForge writes fitment as Shopify metaobjects native to your store — most competitors store fitment in their own database. (2) 8 verticals out of the box: auto, motorcycle, tractor, marine, power equipment, bicycle, printer, electronics — most competitors are automotive-only. (3) Smart Parse: extract fitment automatically from your existing product titles and descriptions instead of re-typing everything.

mdstill's answer

Alternatives fall into two camps: developer libraries that require setup, or enterprise SDKs that require a sales call. mdstill fills the middle: open a browser, drop a file, get Markdown in seconds — and when you need to scale, the same conversion runs through a REST API. 20+ formats in one tool instead of picking a different parser per format. Tables survive the trip (most tools mangle them). Files are deleted immediately after processing. Free tier, no credit card, no signup for basic use.

ViewForge's answer:

Data ownership. Fitment lives in your Shopify metaobjects, so uninstalling does not take your compatibility data with it. Convermax, EasySearch and PartFinder all keep it in their own databases, and getting it back depends on their export tooling on the day you cancel.

Cost at the low end. The search widget, the compatibility table on the product page and the saved-vehicle garage are all on the free tier, up to 50 products, with no expiry. EasySearch puts the table and the garage behind its $75/month Premium plan. Convermax starts at $250/month.

Automotive and non-automotive coverage. Eight built-in templates, and fully custom templates from $19/month, for catalogs that do not decompose into Year/Make/Model at all.

mdstill's answer

Two overlapping groups. Developers building AI features — engineers feeding documents into ChatGPT, Claude, or Gemini APIs; teams building RAG pipelines and AI agents who need reliable document ingestion. Knowledge workers and researchers — Obsidian and Notion users importing legacy PDFs, students preparing papers for AI analysis, analysts converting spreadsheets for LLM review. Common thread: anyone who's discovered that pasting raw PDF text into an LLM loses tables and wastes tokens.

ViewForge's answer:

Shopify merchants whose customers need to know whether a part fits before they will buy it — and who do not have an engineer on staff to build that themselves.

Concretely: auto and truck parts retailers, powersports and motorcycle dealers, tractor and agricultural parts sellers, marine and outboard suppliers, small-engine and power equipment stores, bicycle and e-bike component shops, printer supply merchants, and electronics accessory sellers.

Catalog sizes run from a few dozen products on the free tier up into the tens of thousands; it is running in production on a catalog of roughly 40,000 SKUs. The common thread is not the industry — it is that "does this fit my thing" is the question deciding the sale.

mdstill's answer

mdstill started from a personal frustration: feeding documents into ChatGPT and Claude meant pasting messy PDF text with broken tables and lost structure, or paying for heavyweight enterprise tools just to preprocess a few files. The fix seemed obvious — Markdown is what LLMs understand best, so the conversion should be a utility anyone can use, not a product you buy. mdstill was built to make high-quality document-to-Markdown preprocessing free and instant for everyone, with an API for teams who need to scale.

ViewForge's answer:

ViewForge came out of agency work. Normal View was building for a parts retailer running roughly 12,000 SKUs who needed fitment search, and every app we evaluated stored the merchant's compatibility data in the vendor's own database.

That is a strange trade when you look at it directly. Fitment data is genuinely expensive to produce — it is weeks of work — and the merchant would not own the result. It would belong to whichever app happened to be installed that year.

Shopify metaobjects made a different answer possible: write fitment as native structured data inside the merchant's own store. The theme reads it, the Storefront API queries it, Admin GraphQL exports it, and it is still there after an uninstall. That decision is what the rest of the app is built around.

Everything else came from real catalogs rather than a roadmap. Eight verticals exist because a tractor catalog is not Year/Make/Model. Smart Parse exists because that retailer had already written fitment into 12,000 product titles, and nobody was ever going to retype them.

mdstill's answer

mdstill launched publicly in April 2026 and is in the early-adopter phase. Currently used by individual developers, indie AI-tool builders, and small research teams — customer logos will be added as early adopters opt in to share them.

mdstill's answer

Python + FastAPI on the backend, Next.js + TypeScript on the frontend.

ViewForge's answer:

• TypeScript
• Shopify metaobjects, as the fitment data store
• Shopify theme app extensions for the storefront components: fitment search, saved-vehicle garage, compatibility table, product-page fit notice
• Shopify Storefront API, for querying fitment from the theme
• Shopify Admin GraphQL API, for writing and exporting fitment records
• NHTSA vehicle database, for VIN decoding
• ACES and PIES XML parsing, for automotive catalog import