HeyDonto

A semantic integration layer for healthcare data that aligns siloed dental and oncology data into actionable signals.

Contact for pricing United States

HeyDonto is a US-based health tech holding company offering a suite of products focused on a single mission: using AI to align healthcare data from different systems and formats at the "semantic layer," ensuring that data retains its consistent meaning as it flows across systems.

Features & Use Cases

The hardest part of medical data integration has never been moving data around—it is the fact that the same concept goes by different names and has different definitions across systems, meaning a field in System A might cover a completely different scope than the same-named field in System B. HeyDonto's core platform, Axiomera, handles exactly this: enterprise data classification, mapping, and reconciliation to guarantee cross-system semantic consistency. Its other products divide and conquer: Conduit serves as a dental interoperability exchange layer, leveraging FHIR standards to connect dental systems into broader healthcare IT infrastructures (as dental data has long been a silo in medical informatics); Quantara targets oncology, structuring pathology, imaging, and molecular data into actionable clinical intelligence; DFT Labs is the research arm, developing a theoretical framework called Data Field Theory and publishing peer-reviewed papers; and Red Wheelbarrow is a multi-agent software development platform used internally and offered externally. With offices in Knoxville, USA, and Berlin, Germany, the company does not publicly disclose pricing on its website.

Ideal for healthcare systems, dental groups, oncology care center data teams, and health tech companies that need to handle cross-system medical data integration.

Key Features

Axiomera: Enterprise data classification, mapping, and semantic reconciliation
Conduit: FHIR-standard integration connecting dental practices to broader healthcare IT infrastructure
Quantara: Clinical intelligence for oncology pathology, imaging, and molecular data
DFT Labs: Data Field Theory research and peer-reviewed publications
Red Wheelbarrow: Multi-agent software development platform
AI-powered semantic intelligence and data classification
Maintenance of cross-system data meaning consistency

Common Use Cases

Dental groups aligning and integrating data scattered across multiple clinics
Healthcare systems bridging dental and medical data using FHIR
Oncology care centers integrating pathology, imaging, and molecular test results
Health tech companies reconciling multi-system data following mergers and acquisitions
Research teams building semantically consistent datasets

Key Features

Axiomera: Enterprise data classification, mapping, and semantic reconciliation
Conduit: FHIR-standard integration connecting dental practices to broader healthcare IT infrastructure
Quantara: Clinical intelligence for oncology pathology, imaging, and molecular data
DFT Labs: Data Field Theory research and peer-reviewed publications
Red Wheelbarrow: Multi-agent software development platform
AI-powered semantic intelligence and data classification
Maintenance of cross-system data meaning consistency

Pros

Focuses on the semantic layer rather than just data movement, addressing the root problem
Dental interoperability has long been overlooked, making Conduit's positioning uniquely valuable
Features a dedicated research arm publishing peer-reviewed papers, moving beyond pure marketing
Multimodal oncology data integration delivers clear clinical value

Cons

As a holding company with a scattered product line, the overall value proposition isn't instantly intuitive
Pricing is not publicly disclosed on the website
Operates at the infrastructure layer, making its benefits somewhat abstract for non-data teams
Designed with the US healthcare IT ecosystem (FHIR, insurance frameworks) as a prerequisite

Use Cases

Dental groups aligning and integrating data scattered across multiple clinics
Healthcare systems bridging dental and medical data using FHIR
Oncology care centers integrating pathology, imaging, and molecular test results
Health tech companies reconciling multi-system data following mergers and acquisitions
Research teams building semantically consistent datasets

Editor's Note

To be frank, HeyDonto's website is a bit tough to navigate; with a holding company housing a sprawl of products, it's easy to miss the point at first glance. Yet the problem they are solving is crucial—after years of hype around medical AI, the real bottleneck has never been model capability, but rather misaligned data meanings. Companies willing to invest resources into the semantic layer and foundational research are rare, making HeyDonto well worth keeping on your radar.

FAQ

What is "semantic layer" data integration?

Traditional integration considers the job done once data is moved from System A to System B. However, identical field names can have entirely different definitions in each system, corrupting combined analytics. Semantic layer integration solves the question of "are these two fields actually talking about the same thing?"—a problem far harder than format conversion and critical to data quality.

Why does dental data require specialized handling?

In most countries' healthcare IT landscapes, dentistry operates as a relatively isolated vertical. Systems, codes, and workflows are completely custom, making it difficult to interoperate with general medical data. This creates clinical blind spots—such as the challenge of integrating and analyzing the link between oral health, cardiovascular disease, and diabetes. Conduit targets precisely this isolated silo.

Can international healthcare institutions utilize this?

The core concepts apply universally, as medical institutions worldwide face inconsistent cross-system data definitions. However, because the product is built around US FHIR implementations and healthcare ecosystems, direct adoption in regions with distinct national insurance billing formats and electronic medical record standards will require significant localization.