Objects VS aiomics

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.

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.

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.

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.

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.