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DiaMonTech AG

Moving continuous glucose monitoring from bench to wrist

Industry
Medical Devices and Digital Health
Headquarters
Berlin, Germany
Public information as of
January 2026

A4BEE prepared this analysis from publicly available sources. It reflects our own reading of DiaMonTech AG's published strategy and is not endorsed by, or produced in cooperation with, DiaMonTech AG. Company website

Strategic priorities

DiaMonTech is a Berlin-based medical device company developing non-invasive continuous glucose monitoring based on mid-infrared photoacoustic spectroscopy. The current product is the D-Pocket, a handheld point measurement device; the strategic target is the D-Sensor, a wearable developed with Samsung NEXT that is intended to integrate into consumer smartwatches and the Samsung Health ecosystem. Both products are still in development as of 2026.

In September 2025 the company closed a Series C round of approximately €9.5 to €12 million, led by Samsung NEXT with participation from Companisto. The capital is being used to accelerate miniaturization of the sensor and to prepare US market entry, with the company participating in the German Accelerator Life Science Program to build a US regulatory and commercial presence. The intended FDA pathway is De Novo or 510(k), with a target clear-or-file horizon of 2026 to 2027.

The operational shift the company is mid-way through is the move from hand-assembled prototypes built in a physics lab to repeatable manufacturing at wearable volumes. The D-Sensor relies on precision alignment of quantum-cascade laser components and custom optical assemblies, which is hard to do at prototype scale and harder still at consumer volumes. DiaMonTech has publicly stated the goal of keeping medical-grade accuracy (a Mean Absolute Relative Difference below 15 percent) through that transition.

The two pieces of work that have to land at the same time are the FDA digital thread — every design input, verification result and manufacturing record traceable under 21 CFR Part 11 — and a secure device-to-cloud pipeline for continuous glucose data that satisfies both GDPR and HIPAA. The same period also has to deliver the Samsung Health integration API surface, so the system that meets regulators is also the system that meets the consumer-health platform.

Challenges we see

  • Quality Manufacturing

    Holding medical-grade accuracy as volumes rise

    D-Sensor depends on precision alignment of mid-infrared laser components and custom optical assemblies. The company has stated a target of keeping measurements inside a Mean Absolute Relative Difference of 15 percent — the threshold typically used to call a glucose monitor medically accurate. The same component set is currently hand-assembled in Berlin.

    A hand-assembled photonic instrument and a consumer wearable ask different things of the production line. The shape of the transition is set by what the optical subassembly needs at each station, not by how the prototype was built.

  • Data Integration

    Closing the gap between R&D and production data

    Spectroscopy calibration data, laser characterization and assembly tolerances are produced in the R&D environment, while yield, defect and component-traceability data is about to be produced on the emerging production floor. The two data sets shape each other: process conditions that explain drift live on the R&D side, and the evidence that a unit is in spec lives on the production side.

    Where the two estates sit in separate systems, the question 'why did this batch drift' is reconstructed by hand. A shared model for units, lots and measurements means the answer comes from the same data the record was built on.

  • Compliance Regulatory

    Building the FDA digital thread before the submission window

    DiaMonTech has stated a target of FDA De Novo or 510(k) clearance in 2026 to 2027. The submission needs an unbroken traceability chain from design inputs through verification testing to manufacturing records, with electronic records and signatures meeting 21 CFR Part 11.

    The submission window is set by external regulators, but the evidence behind it is assembled internally. How design inputs, test results and production records are linked during the build phase determines whether the audit trail is read off a system or compiled by hand.

  • Security Operations

    Securing the device-to-cloud pipeline for patient glucose data

    The D-Sensor streams continuous glucose readings to a mobile app and from there to the cloud and to consumer-health platforms. The data path is regulated under GDPR in Europe and HIPAA in the United States, and the Samsung Health integration is a public third-party endpoint.

    Connecting a medical device to a consumer-health platform is a public-facing surface with two regulatory regimes attached. The architecture decisions taken early — identity, encryption, consent and audit — set the security posture for the lifetime of the product.

  • Supply Manufacturing

    Managing a narrow supplier base for specialized optical components

    Mid-infrared quantum-cascade lasers and specialized optical elements are produced by a small number of vendors globally. The hardware roadmap, the FDA clinical submission and the Samsung Health launch all run on the assumption that these components are available on the schedule the engineering plan assumes.

    When a small number of components come from a small number of suppliers, the production plan and the regulatory plan share the same dependency. Planning for second-source qualification, lead-time buffers and lot-traceability is part of the same work as process design.

Opportunities, by urgency and business impact

Each bubble is one opportunity, numbered to match the list below. Further right means it bites sooner; higher means a bigger effect on the business. A bigger bubble means a bigger implementation effort.

Source: A4BEE analysis of public sources

  1. Putting production data on a single platform from the prototype line

    DiaMonTech is moving from hand-assembled prototypes to a repeatably manufactured D-Sensor. The production line that will support consumer volumes does not yet have a unified data platform spanning yield, quality deviations and component traceability.

    A manufacturing execution system connected to the alignment stations, inspection points and test rigs gives the team a single record of what each unit experienced, so the same data set serves the engineers, the quality organisation and the FDA submission.

    • DiaMonTech press materials on D-Sensor miniaturization, 2025
    • DiaMonTech_DeepResearch.md, January 2026

  2. Linking design inputs, verification tests and manufacturing records

    An FDA De Novo or 510(k) submission needs an unbroken traceability chain from design inputs through verification testing to the manufacturing record. The same links also serve the day-to-day work of resolving deviations and reviewing changes.

    A shared requirements-to-evidence model lets the company trace any design input to the verification artefact and the production record that confirms it, so the audit trail and the operational change record are reading the same data.

    • DiaMonTech_DeepResearch.md, January 2026
    • DiaMonTech corporate communications on US market entry, 2025

  3. Designing the device-to-cloud architecture for two regulatory regimes

    D-Sensor glucose data has to traverse a device-to-mobile-to-cloud pipeline that lands under GDPR in Europe and HIPAA in the United States, with Samsung Health as a third-party consumer endpoint.

    A documented security architecture — identity, encrypted transport, consent and audit logging — declared once, with the implementation expressed as a reusable set of components, so the same pattern covers the regulatory submission and the consumer-health integration.

    • DiaMonTech product information on D-Sensor and diamoki, 2026
    • DiaMonTech_DeepResearch.md, January 2026

  4. Automating inline verification of laser and optical alignment

    D-Sensor accuracy depends on the alignment of laser and optical subassemblies within tolerances that are hard to verify by eye at production volumes. The current hand-assembly approach cannot scale to consumer wristband quantities.

    Inline optical inspection built on top of the production data platform checks each unit's alignment against the same spec used in R&D, and feeds the verification record into the same audit trail every other verification step uses.

    • DiaMonTech product materials on D-Sensor, 2025
    • DiaMonTech_DeepResearch.md, January 2026

  5. Structuring the firmware and software development lifecycle for SaMD

    D-Sensor firmware and the diamoki mobile app fall under Software as a Medical Device, with version control, risk management and release procedures aligned to IEC 62304. The current build, test and release practice is sized for an R&D team, not for the volume of regulated releases the FDA submission and the field-installed base imply.

    A documented software lifecycle — branching, code review, automated tests, release candidate generation, audit-ready change records — lets the engineering team ship faster without each release needing to be re-argued against the standard.

    • DiaMonTech_DeepResearch.md, January 2026
    • IEC 62304 medical device software standard

What we'd propose

  • Digital CDMO

    Manufacturing data platform for the D-Sensor production line

    We connect the alignment stations, inspection points and test rigs on the D-Sensor line to a manufacturing execution system, so yield, deviations and component traceability are captured at the cell and shared with engineering, quality and the regulatory record.

    • Equipment and station connectivity

      Data off the line

      Connect alignment stations, optical inspection and final test through OPC UA (Open Platform Communications Unified Architecture) or MQTT so process values and inspection results leave the equipment in a documented, vendor-neutral form rather than sitting inside a closed controller.

    • Real-time yield and deviation view

      The line on one screen

      Build dashboards for yield, units-in-spec and deviation alerts that match the way the engineering team already describes the process, so the same view supports shift handovers, quality reviews and engineering investigations.

    • Component and batch traceability

      Where each unit came from

      Record every component lot and serial number against the unit it went into, queryable back to the supplier batch and forward to the verification record, so FDA design-history-record questions are answered from the system.

    • Yield, deviations and component traceability are read from the same record the engineers use.
    • The FDA audit trail is generated by the line rather than compiled after the fact.
    • The same platform is reusable when the Samsung Health integration wires into the production data.

    We've done this before

    See how we do Digital CDMO
  • Digital Lab

    Digital thread for the FDA De Novo or 510(k) submission

    We build a shared requirements-to-evidence model that links design inputs to verification tests and to the manufacturing records produced by the line, so the FDA traceability chain and the day-to-day change control read from the same place.

    • Requirements and verification traceability

      Inputs to outputs

      Model design inputs, design outputs, verification protocols and test results as a single graph so a query can return every artefact that backs a claim, and so a change can be propagated to the documents it touches.

    • 21 CFR Part 11 electronic records

      Records that hold up to inspection

      Implement electronic signatures, role-based access and a tamper-evident audit trail to the US rule on electronic records and signatures, so the submission file and the running system share the same evidence.

    • Design history record compiled from the system

      One document, many sources

      Generate the design history record directly from the requirements and verification graph plus the manufacturing record, so the submission stays in step with the engineering work rather than drifting.

    • Submission preparation reads from a system rather than from a reconstructed folder.
    • Every change in design or production can be traced to the documents it touches.
    • Audits answer 'where did this number come from' from the record itself.

    We've done this before

    See how we do Digital Lab
  • Enterprise AI

    Secure device-to-cloud architecture for D-Sensor and Samsung Health

    We design the identity, transport, consent and audit layer for the D-Sensor-to-mobile-to-cloud pipeline under GDPR and HIPAA, and expose the data to Samsung Health and the diamoki app through a documented API surface.

    • Identity and encrypted transport

      Who is talking to whom

      Establish device identity, mutual authentication and end-to-end encryption from the D-Sensor to the cloud, with key management documented for the regulatory file and for the security review.

    • Consent and audit logging

      Every action traceable

      Capture data-sharing consent, role-based access and tamper-evident audit logs that satisfy GDPR data-subject-rights and HIPAA audit requirements from the same data store.

    • Health-platform API surface

      One shape for external endpoints

      Define a documented API for Samsung Health and any future consumer-health or clinical endpoint, with version handling and deprecation rules, so partner integrations become a configuration rather than a rebuild.

    • The same architecture satisfies the GDPR file and the HIPAA file.
    • Samsung Health integration is a published API rather than a one-off build.
    • Security posture is documented with the product, not retrofitted after a field incident.

    We've done this before

    See how we do Enterprise AI
  • Agents

    AI agents for the FDA, CE and clinical documentation workload

    Narrow, reviewable agents that draft the first version of the repetitive parts of regulatory and clinical documentation — study reports, design history record sections, verification summaries, change-impact assessments — from the source records in the digital thread, with a named reviewer approving every output.

    • Drafting from source records

      First drafts from system data

      Pull from the requirements-to-evidence model and the manufacturing record to generate the first draft of study reports, verification summaries and design history record sections, so the author edits and judges rather than assembles.

    • Template and completeness checking

      Gaps found before review

      Check a submitted document against its FDA, CE or IEC 62304 template and the company's own checklist, returning missing or inconsistent sections before it enters the human review queue.

    • Change impact across the document set

      Which documents a change touches

      When a design input, a standard or a specification changes, retrieve every controlled document that references it and rank them by how directly they are affected, so the update scope is known on day one.

    • Submission and clinical documentation arrive at review already complete.
    • The scope of a standards change is established by search rather than by recollection.
    • Every output is traceable to the source records it came from and signed off by a named reviewer.

    We've done this before

    See how we do Agents

Digital maturity: today and target

Scored out of 100 across six dimensions. The target is what DiaMonTech AG's own published ambition implies — not a perfect score.

Source: A4BEE analysis of public sources
Manufacturing digitalization 30 → 80
D-Sensor units are currently hand-assembled in Berlin. The transition to repeatable manufacturing with inline inspection is the work the company is mid-way through, and the data infrastructure that supports it follows from that.
Data integration 25 → 85
R&D calibration and characterization data and the production data the line will generate live in different estates. The Exact Sciences-style problem at a smaller scale: the two need to be queryable as one before the FDA submission file is compiled.
Regulatory infrastructure 35 → 90
The FDA De Novo or 510(k) window of 2026 to 2027 sets the schedule. The digital thread that meets 21 CFR Part 11 is built during the engineering work, not added at the end of it.
IoT and connectivity 40 → 85
The D-Sensor-to-cloud pipeline is in development alongside the device itself. The architecture decisions now — identity, consent, audit — cover GDPR, HIPAA and the Samsung Health integration at the same time.
Quality systems 45 → 90
Prototype-level inspection is sized for the current build volume. The volume question is whether the same verification standard can be held at consumer-wearable scale, with the answer recorded against each unit.
Software lifecycle 40 → 80
D-Sensor firmware and the diamoki app are regulated as Software as a Medical Device. The engineering practice scales with the release cadence the FDA submission and the field-installed base imply.

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This is an independent analysis prepared by A4BEE from publicly available information as of January 2026. It reflects A4BEE's own interpretation and opinion, is not affiliated with, endorsed by, or verified with DiaMonTech AG, and may be incomplete or inaccurate. All company names and trademarks are the property of their respective owners. To request a correction or removal, contact [email protected].