From research and product
to regulated market fit.

The Problem

CSRD Scope 3 reporting mandates were arriving. Enterprise software teams had no tooling to measure, report, or reduce the carbon intensity of their software processes. The Green Software Foundation had published the SCI specification. Nobody had built the ledger.

The Approach

A double-entry Net-Footprint Ledger designed to satisfy CSRD Scope 3 auditors, Green Software Foundation SCI compliance, and enterprise procurement requirements simultaneously. Modular B2B SaaS architecture. Digital Twin modelling. No retrofitting required. Compliance was load-bearing from the start.

Field Evidence

Architecture complete. PRD published. Graduated to independent brand and independent funding pathway. Seeking research partners through Decarb-AI research program (pending).

Compliance Layer

CSRD Scope 3 · Green Software Foundation SCI

Partners

Decarb-AI research program (pending)

The Problem

The TripAnimal pilot validated the methodology. The next step was scaling it, building the independent platform, the independent brand, and the independent funding pathway that the validated approach warranted.

The Approach

Built directly on TripAnimal’s proven architecture and field evidence. Independent brand identity developed. Independent funding pathway opened. The graduation model working exactly as designed.

Field Evidence

Graduated to independent brand. Top 3 at EIT Mobility & Tourism Challenge, Vienna 2026. The field evidence from TripAnimal (100+ users, 100% on-chain transaction success, 4.5/5 EU Low-Footprint Scorecard) is the foundation.

Compliance Layer

EU Low-Footprint Software Scorecard

Partners

Superteam IE · EIT Mobility & Tourism

The Problem

Rural tourism platforms had no mechanism for verifying or rewarding genuinely sustainable behaviour, and no way to demonstrate their own environmental footprint. The EU Low-Footprint Software mandate was arriving. The tooling to comply with it didn’t exist.

The Approach

NGI TrustChain funding criteria, EU Low-Footprint Software Scorecard requirements, and real user needs in rural tourism were architected together, not sequenced. On-device ML and offline-first architecture (carried from Kibleep and Fieldnotes). Solana for on-chain transaction verification. The full stack (technical, governance, and commercial) was designed simultaneously.

Field Evidence

12-week live pilot across three villages in Crete, December 2025 – February 2026. 100+ real users. 100% on-chain token transaction success rate. 4.5/5 on the EU Low-Footprint Software Performance Scorecard. Top 3 at EIT Mobility and Tourism Challenge, Vienna 2026. These are not projections.

Compliance Layer

EU Low-Footprint Software Scorecard · NGI TrustChain

Partners

EU NGI TrustChain · Superteam IE · EIT Mobility & Tourism

The Problem

Agri-food operators and climate programmes needed real-time, verifiable field-level data, but existing systems produced data that couldn't be independently verified, couldn't travel across supply chains, and couldn't satisfy emerging regulatory requirements for climate reporting.

The Approach

ISO-standardised DLT use case models deployed with agri-tech clusters at MTU, NUIG, and Atlantic University in Ireland; URV and Generalitat in Spain. Hyperledger Besu for field data capture. NFT publishing (Ethereum, Polygon) for stakeholder reputation. Living Lab events, not lab conditions. The stakeholder reputation and knowledge transfer model developed here became OCN’s ecosystem design methodology.

Field Evidence

Living Lab events across Ireland and Spain. Open innovation field workshops. Peer-reviewed publication. The stakeholder reputation model developed through CFC and VIN-Q now governs how OCN structures every multi-disciplinary R&D partnership.

Compliance Layer

ISO TS23257 · ISO TR3242 · ISO DTR6277

Partners

NGI TRUBLO (Horizon Europe) · URV · Agri-ledger · MTU · NUIG · Atlantic University · Generalitat de Catalunya

The Problem

Regenerative viticulture produces measurable environmental benefit, but without verifiable data infrastructure, those claims couldn't be substantiated for buyers, regulators, or investors. The impact was real. The proof wasn’t.

The Approach

DeSci (Decentralised Science) architecture combined with stakeholder reputation modelling. The methodology for multi-disciplinary innovation ecosystems developed through VIN-Q was refined and carried into CFC, and subsequently into every OCN venture requiring multi-stakeholder coordination.

Field Evidence

Stakeholder reputation model developed. Methodology contributed directly to the CFC project and became the foundation of OCN’s ecosystem design approach.

Compliance Layer

Multi-disciplinary innovation ecosystems

Partners

Led by URV (Universitat Rovira i Virgili), with academic, industry and supply chain partners across the Catalan wine sector

The Problem

Community biodiversity initiatives produced real environmental benefit, but that benefit was invisible to funders, planners, and policymakers. Without verifiable proof of actions taken, community groups couldn’t demonstrate impact, couldn’t attract sustained funding, and couldn’t influence planning decisions.

The Approach

Soulbound tokens (NFTs on OpenSea) as verifiable proofs of community biodiversity actions. Web3 verification layer designed to be accessible to non-technical community participants. Trialled the CFC application in a community context.

Field Evidence

75 metres of urban hedgerow supported in Dublin. Web3 proofs deployed. The application of soulbound tokens to community environmental action established a verification model applicable to any community-led sustainability initiative.

Compliance Layer

Environmental action verification

Partners

Phibsboro Biodiversity Group · Connecting Cabra

The Problem

Farmers participating in environmental management schemes needed to prove that the actions they took actually happened, in the field, in real conditions, without connectivity. Existing systems required internet access, centralised infrastructure, or manual data entry that introduced the possibility of error or fraud.

The Approach

On-device ML and offline-first architecture for verifiable proof of environmental actions. Hyperledger Fabric for data provenance. Designed to operate on farm equipment in remote locations without reliable connectivity, applying the same architectural principles established in Kibleep to agricultural field conditions.

Field Evidence

Proof-of-concept deployed across six farms and vineyards in Spain and Ireland. The field evidence generated through Fieldnotes contributed to the CFC project’s ISO standardisation work and to the 2023 peer-reviewed publication on smart farming and climate action.

Compliance Layer

Environmental action verification · Data provenance

Partners

EIT SEED (Horizon 2020)

The Problem

Children with severe food allergies face real danger in ordinary settings. The EU Food Information Regulation mandates clear allergen labelling, but the gap between the label and the person reading it, under pressure, in unfamiliar settings, is where the risk lives.

The Approach

On-device computer vision trained to read EU allergen declarations on packaged food labels. Offline-first architecture: allergen profile and recognition model stored and executed on-device. No server dependency. No connectivity required. Designed for non-specialist users, children and families, in high-pressure real-world conditions.

Field Evidence

Live at kibleep.app. OCN's first deployment of on-device ML for real-world regulatory compliance. The architecture (intelligence on the device, compliance without connectivity) was carried directly into TripAnimal and Tiny Wilderness.

Compliance Layer

EU Food Information Regulation No 1169/2011

Partners

N/A

The Problem

Local food has a measurable carbon advantage over imported alternatives, but that advantage was invisible. Without a way to calculate and communicate it, local sourcing decisions were made on price and convenience alone.

The Approach

A last-mile carbon calculator integrated with a local sourcing platform, deployed in Dublin 7 with local organic food providers. Pre-commercial R&D establishing the methodology for food carbon provenance.

Field Evidence

Proof-of-concept deployed in Dublin 7. The carbon provenance methodology developed here informed OCN’s subsequent approach to field-level data capture in CFC and Fieldnotes.

Compliance Layer

Carbon footprint methodology

Partners

Local organic food providers (Dublin 7)