aiomics VS ViewForge

aiomics's answer

Python and FastAPI on the backend; React, TypeScript and Tailwind on the frontend. A multi-agent LLM verification layer orchestrated with LangGraph and observed via Langfuse, built on LlamaIndex. Graph and vector storage via FalkorDB. Clinical standards: ICD-10-GM, OPS, LOINC, HL7 v2 and FHIR R4. Hosted entirely in the EU on AWS Frankfurt; sovereign European and on-premise alternatives are available upon request. The marketing site runs on Next.js with a Sanity CMS.

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

aiomics's answer

Anonymization rule applies here — no named clinic groups in public materials. Use generics:

• A large rehabilitation and acute-care hospital group, live across multiple sites; additional live pilots in Germany, Switzerland and Sweden; Used by various smaller clinics.
• 30+ sites covered under a group framework agreement (in progress)
• University hospital and large public hospital groups in active pilots

aiomics's answer

aiomics verifies clinical data instead of just generating it. Most AI tools extract or draft text in a single pass — feed them an incomplete record and they return one that is fluent, formatted, and wrong. aiomics runs every extraction through an adversarial protocol in which independent models draft, a critic audits each statement against the original document, and an arbiter resolves the rest. What comes out is a structured patient record where every data point traces back to its source. Proposed by AI, verified by the physician.

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.

aiomics's answer

German and DACH-region hospitals and rehabilitation clinics, typically within larger hospital groups. The buyers are CFOs (revenue integrity, audit defense), CIOs (KIS-agnostic integration, security), and senior physicians (time returned to clinical work). Expanding into acute-care hospitals, oncology centres, vocational rehabilitation, and individual physician practices, with first engagements in Switzerland and Sweden.

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.

aiomics's answer

No competitor verifies new data against the existing patient record. Scribes generate but don't check; extraction tools pull data but don't reconcile contradictions across sources. aiomics sits on top of the systems a hospital already runs — it stays agnostic to the KIS and ingests whatever arrives, in any format. It is ISO 27001 certified, runs entirely in the EU, and is deliberately positioned as an administrative data layer outside the medical-device regulation. Its accuracy is being evaluated independently at a university hospital. The defensibility is integration depth: every connected site accumulates field mappings and edge-case resolutions that take a year to build and cannot be carried elsewhere.

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.

aiomics's answer

A physician at a hospital opens her morning with around ten referral documents for a single admission — most incomplete or contradicting one another, scattered across five to ten systems. By the time she has assembled a coherent picture, the documentation that decides reimbursement and survives a payer audit is already being written, against the clock, from fragments. Hospitals treat this as a billing problem and try to fix it at the end, but the cost and audit exposure are decided at the start, in the documents. aiomics was built to fix it there: an intelligence layer that ingests everything arriving at the hospital, verifies it against the source, and hands back a record the hospital can trust. Founded in Berlin by a physician and a physicist.

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.