An investment
worth playing with.
Development boutique showroom. Seven product brands connected by passerelles, three active patent families — a multi-product portfolio instead of single-device risk. Place a non-binding bid on any asset; Joeri Gredig reviews each one personally and responds with an AVPs offer.
Products
Strategic assets
Sponsoring partners — in-kind
Tier 0 — small & medium enterprises offering services, materials, or engagement without cash or equity. Lowest threshold, highest network signal.
Foundation partners — research grants
Tier 1 — Swiss foundations supporting research with project-bound cash grants (no equity). Active partners and historic supporters (since 2011).
Active
Historic
Public funding track record
Non-dilutive public funding (Swiss Innovation Agency Innosuisse + historic KTI). Cumulative cash & total-volume signal R&D maturity and validated research-partner network across 16+ years.
Licensing partners — IP licenses
Tier 2 — companies licensing brand IP for commercial use (royalty or fixed fee, no equity). Academic and public-research use stays free for citation; commercial use is private-capital licensing.
Science & market intelligence
The rehabilitation market is shifting from selling devices to proving recovery that survives daily life. The decisive layer is no longer the machine — it is the evidence that a movement is meaningful, not merely present. Motion is not recovery. This is the layer the science below builds toward.
Two trust anchors investors care about: does Joeri Gredig understand the science behind the products? and does Joeri Gredig understand the market they enter?
Closing the Substrate Gap in the JEPA Family
Working paper positioning Joeri Gredig's patents (EP 2 364 686 B1 granted 2015, PCT/EP2024/076634 drive-unit pending) inside the LeCun JEPA family. Introduces HCWM (Human-Centered World Model) as a structurally distinct construct, anchored in active-inference notation and instantiated as a 4-actuator EtherCAT testbed at 100 Hz hard-real-time.
↓ Download paper (360 KB)Harvest Research Engine (HRE)
HRE is a continuous web-intelligence pipeline that tracks competitor filings, market signals, regulatory shifts, and scientific publications across each brand's vertical. Its feasibility was confirmed by an Innosuisse Innovationscheck (FHGR / Albert Weichselbraun, approved 2020). Operationalised on NVIDIA Jetson Orin Nano edge hardware (data_fetch_assistant.py).
The HCWM paper above is the flagship of a three-paper programme. Two companion papers extend the same patent-disclosed substrate — both pre-arXiv and available on request; their acknowledgements are pending contributor consent.
Counterfactual Latent Prediction (Predictor) — a world-model that asks not “did the patient move?” but “would this movement still hold under changed conditions?” Built on do-operator counterfactuals in latent space: the computational form of motion is not recovery.
The portfolio does not start from a blank page. It rests on validated aquatic-exercise and neuro-rehabilitation research, archived in a sovereign studies repository:
- Fenzl et al. — Release of ANP/BNP & fat oxidation in aquatic aerobic exercise (2012)
- Fenzl — Heart-rate variability for aquatic performance diagnostics (2013)
- Bansi et al. — Aquatic endurance training, multiple sclerosis (2013)
- Rezek — Randomised controlled trial, study protocol (2013)
- Rewald — Aquatic cycling (2018)
- Clijsen et al. — NIRS muscle-oxygenation measurement (2023)
- Frei, Schöch, Joos — Exergames in rehabilitation (2023)
- + contemporary reference: HIIT & protein turnover, Nature Communications (2026)
The field's leading authority describes — independently — the same axes this work is built on: intelligence as predict-then-realize, closing the loop between prediction and realization, grounded in embodiment.
- R. Riener (Ed.), AI to Eye — Between Code and Conscience, vdf Hochschulverlag Zürich, 2026 (ISBN 978-3-7281-4228-3)
- H. van der Kooij, …, R. Riener — “AI in therapeutic and assistive exoskeletons and exosuits: Influences on performance and autonomy”, Science Robotics 10(104), 2025 — DOI 10.1126/scirobotics.adt7329
Robert Riener — Professor of Sensory-Motor Systems, ETH Zürich; initiator of Cybathlon. Cited as independent reference, not as endorsement or affiliation.
How we know what we know
Three Innosuisse rejections, one ChatGPT launch in between, and the lesson that survives both: tools age in months — domain expertise grows in decades. The grail of the AI era isn't the best tool. It's the curator who knows which tool to point at which question.
Four questions every investor asks
Not the tool. Not the data. The curation — by an operator who lived three rejections, watched a language model obsolete his scientific edge in 19 days, and built the synthesis that survives both. That is what carries an investor across the gap between "interesting product" and "fundable thesis".
Readiness & market trend
Two views of one question — how ready is this portfolio, and is the market moving our way? The outside-in trend (what the field demands by 2030) and our own inside-out readiness scoring meet here, and feed the investment phases and the valuation.
The trend has moved toward our approach
Between May 2025 and June 2026 the trend line itself shifted — toward what craPos / HCWM has stood for: force-aware, safety-critical execution; reliability over hype; sovereignty; orchestration of intelligence layers. A 12-dimension Trend-Soll vs. IST gap-matrix reads 6 strong / ahead, 6 in progress, 0 critical gaps.
Cybersecurity — answered, not deferred
The sharpest 2026 demand (board-priority #1) is answered by design, not deferred: security-by-design oriented to IEC 62304, hash-chained SHA-256 audit logging, and a documented device-hardening path (IEC 81001-5-1), mapped as "Next" in the passerelle.
A six-layer Neuroplasticity Operating System — a framework articulated in the field (notably by Zen Koh, June 2026) — describes the architecture rehabilitation now needs. The portfolio already instantiates all six layers:
| Recovery-system layer | Our component | |
|---|---|---|
| 1 · Impairment map | HCWM / Predictor — reconstructs the hidden neuromotor state | v0 |
| 2 · Signal interface | craPos™ — force-aware robotic interface, 100 Hz, “runder Tritt” | built |
| 3 · Adaptive challenge | craPos gaits + Δ-parameters · ECP-UGC™ composer | built |
| 4 · Feedback engine | ECP-UGC™ game feedback + craPos live visualization | built |
| 5 · Dose engine | NAMRU (mobile/home) + ECP-UGC™ home scaling | concept |
| 6 · Translation layer | ascros™ medical pathway (Class IIb) + clinical Δ-outcomes | reg. |
The recovery loop is the defensible position — and that loop is the closed loop disclosed in EP 2 364 686 B1 / PCT EP2024/076634. Cited as independent observation of the field; no affiliation or endorsement implied.
Read the full mapping →Inside-out · readiness across the portfolio. The seven readiness levels (RRL·TRL·SRL·IRL·BRL·HRL·LRL, scale 0–9) per product — the same scores shown on each product card, laid side by side so the portfolio-wide pattern becomes visible.
| Product | RRL | TRL | SRL | IRL | BRL | HRL | LRL |
|---|---|---|---|---|---|---|---|
| ECP-UGC™ | 8 | 8 | 7 | 6 | 4 | 5 | 5 |
| craPos™ | 7 | 6 | 5 | 6 | 4 | 6 | 6 |
| Patello™ | 7 | 6 | 5 | 5 | 3 | 5 | 4 |
| ascros™ | 5 | 5 | 4 | 4 | 3 | 5 | 5 |
| craPos-dpcla™ | 5 | 5 | 4 | 4 | 3 | 5 | 4 |
| cranos™ | 6 | 4 | 3 | 3 | 2 | 4 | 4 |
| TRC™ | 6 | 4 | 4 | 3 | 2 | 4 | 3 |
Reading the columns: research & technology are the strength (RRL/TRL highest). Business Readiness (BRL) is the lowest column across every single product (2–4) — not a weakness to hide, but the precise thing a funding round buys. The technology is de-risked; market access is what capital accelerates. But the buyer is not missing — it is conditional: rehabilitation clinics tell us they would adopt ascros once its clinical hypotheses are confirmed. The gate is evidence, not willingness. That confirmation — proof that a gain survives daily life — is exactly what a funding round buys, and what unlocks procurement and reimbursement.
Place a bid
Non-binding offer. Tiers 3-4 (work-investor / cash-investor). Pick an asset and phase, name your CHF amount. We compute AVPs (share-value points) live from the share-valuation model (v2.0):
AVPs = offer × phase_multiplier × (1 + α × γ) · α per asset, γmedtech = 0.50.
Passerelles
Investment phases (6)
Investable entity today
Roadmap
Skeleton (live): read-only asset vitrine — products, passerelles, phases, entities.
Bidding (live now): non-binding bids → Joeri Gredig review → AVPs offer.