Machine-learning models and inference pipelines are increasingly embedded in everyday software: search, translation, photo libraries, voice assistants, and workplace tools. Vendors often market the whole category as so-called "Artificial Intelligence" ("AI"); this dossier uses that label sparingly and prefers concrete terms where possible. Most of this capability is delivered through centralized cloud services operated by a handful of corporations. Users gain convenience, but often surrender prompts, documents, images, and behavioural signals to infrastructure they neither own nor can inspect.

NGI-funded projects explore a different path: Self-sovereign AI — models and pipelines that can run on your own hardware, keep inference local, share compute through community infrastructure, and build on open, inspectable data foundations. None of these projects alone replaces every proprietary assistant or API, but together they show that useful machine-learning work need not require permanent dependence on opaque remote systems.

Critical note: "Local" and "open" are not automatic guarantees of privacy or safety. Running a model on your own device still requires trusting the software stack, the model weights, and whoever maintains updates. Community infrastructure such as AI Horde shares compute rather than keeping everything on-device. Read each project's scope carefully before assuming your data stays private.

Understanding what centralized, vendor-hosted model stacks cost helps clarify why alternatives matter. The dominant pattern — API access to remote models trained on opaque corpora — creates interconnected risks:

Surveillance and data extraction. Cloud assistants such as ChatGPT, Gemini, Claude, Microsoft Copilot, Perplexity, and Grok, and other marketed "AI" features bundled into products, process prompts, uploads, and usage patterns on vendor infrastructure. Terms of service can change; data may be retained for training, compliance, or product improvement without meaningful user control.

Vendor lock-in and dependency. Models, fine-tunes, and integrations tied to one provider become expensive to migrate. Creators and organisations that build workflows around a proprietary API risk sudden price changes, deprecations, or regional unavailability.

Opacity and unaccountability. Closed weights, training data, and safety filters make it difficult to audit bias, hallucination rates, or environmental impact. When something goes wrong, users rarely have insight into why.

Energy and infrastructure concentration. Large-scale inference and training concentrate compute in a few datacentre regions, often far from the communities that consume the services. Engagement-driven recommendation and assistant features further increase demand for always-on processing.

Sovereignty and supply-chain risk. Relying on foreign hyperscalers for core cognitive infrastructure creates strategic dependency — especially for public sector, research, and civil-society use cases that handle sensitive material.

Waiting for better regulation alone is insufficient. Practical, inspectable building blocks that communities and organisations can run themselves are needed alongside policy work.

These NGI-funded projects keep models close to the user — on-device, on local hardware such as FPGAs, or on infrastructure you choose to trust. They address surveillance, latency, and sovereignty risks from cloud-first model stacks, though each makes different trade-offs between convenience, hardware requirements, and complete data isolation.

	SensifAI applies on-device machine learning to tag and organise images without sending photos to a cloud vision API. That matters for personal archives, accessibility workflows, and any setting where visual data must stay on the user's hardware — libraries, clinics, newsrooms, or offline field kits.
	LLM2FPGA explores running open LLMs on FPGAs rather than renting remote GPU clusters. The goal is predictable, locally controlled inference for labs, edge sites, and organisations that cannot or should not route prompts through commercial datacentres.
	Open NPU drivers develops libre Linux drivers for Neural Processing Units, including Mesa Teflon support for Rockchip NPUs. The goal is inspectable, locally controlled inference on edge hardware — phones, boards, and laptops — without depending on proprietary firmware to schedule opaque workloads on accelerators you already own.
	OpenVoiceOS is a self-hostable voice assistant stack built around open speech and language models. It offers an alternative to always-listening cloud assistants: operators can run, audit, and harden the full pipeline, and keep wake-word, transcription, and reasoning off third-party infrastructure when required.
	Offline Translator performs on-device translation with open models, so text never has to leave the phone or laptop. Useful for travel, humanitarian field work, journalist source protection, and any workflow where intermittent connectivity or third-party translation APIs are ruled out.
	AI Horde provides collaborative, community-run generative model infrastructure: volunteers contribute GPU workers and users share capacity instead of relying on one corporate API. It decentralises *who* operates inference, though prompts and outputs still cross the network — closer to mutual aid than to fully offline use.
	AI-VPN applies machine learning locally to VPN traffic analysis for security monitoring. Network operators can detect anomalies and classify flows on their own machines instead of piping sensitive traffic metadata to a SaaS analytics vendor.
	Video chat privacy uses on-device models to edit video feeds in real time — removing or anonymising backgrounds during calls so webcams do not broadcast bookshelves, family members, or street views that recipients never needed to see.
	Provability Fabric adds verifiable evidence and run-time security around model stacks — including privacy-preserving logging for LLM workflows — so operators can audit what ran, what data was touched, and whether policy constraints held without trusting vendor dashboards alone.
	Spacylize distils knowledge from large language models into smaller, more efficient NLP models that teams can run with less compute. That lowers the hardware bar for local text classification, tagging, and extraction without routing every document through a remote API.

Critical note: Local inference often demands capable hardware, technical setup, and ongoing maintenance. Projects still vary in maturity — some are prototypes or research-oriented. Federation and shared compute (AI Horde) reduce per-user hardware needs but introduce trust in peer operators.

Machine learning depends on data as much as on models. Proprietary datasets, undocumented scraping, and siloed corporate stores make it hard to build reproducible, auditable machine-learning systems aligned with community needs. These NGI-funded projects strengthen open data foundations: structured descriptions, self-hostable storage, and linked-data tooling that teams can use for training, evaluation, and knowledge-intensive applications without surrendering custody.

	Data packages standardises how external datasets are described, validated, and packaged through the Frictionless Data ecosystem. Clear metadata and tooling make it easier to publish, discover, version, and reuse training and evaluation data across teams without each project inventing its own ad-hoc format.
	Atomic Tables offers a self-hostable tabular data layer for structured records with typed schemas and sync-friendly storage. Teams can keep feature stores, labelled tables, and operational datasets on infrastructure they control rather than defaulting to proprietary spreadsheets or opaque cloud warehouses.
	Atomic Data provides typesafe handling of linked data and knowledge graphs with explicit schemas and permissions. Semantic, interlinked datasets can feed retrieval-augmented and graph-based ML pipelines while keeping provenance inspectable and hosting under community control.
	SensifAI also bridges vision and data organisation: on-device tagging turns private image collections into structured, searchable material. That creates labelled visual datasets for downstream workflows without exporting raw photos to a cloud indexer or training pipeline you do not control.
	pgmpy provides open-source infrastructure for causal machine learning: modelling cause-and-effect relationships with inspectable graphs and tooling rather than opaque correlation-only scores from proprietary analytics platforms.

This is a first curated set of NGI work on open data foundations and on-device inference; the NLnet project list contains further entries as the ecosystem grows.

Critical note: Open data tooling does not by itself prevent misuse of models or biased training corpora. It raises the floor for transparency and self-hosting, but governance, consent, and documentation of datasets remain human responsibilities.

No single project in this dossier replaces proprietary cloud assistants such as ChatGPT, Gemini, Claude, Microsoft Copilot, Perplexity, and Grok. Choose based on which risk matters most to you.

Browse the NGI-funded projects in this dossier below.

• Keep media and prompts off cloud APIs → SensifAI, Offline Translator, OpenVoiceOS, Video chat privacy
• Run LLMs without datacentre dependency → LLM2FPGA, Open NPU drivers, OpenVoiceOS, Spacylize
• Share GPU capacity without one corporate gatekeeper → AI Horde
• Analyse network traffic locally → AI-VPN
• Audit model behaviour and runtime policy → Provability Fabric
• Own your datasets and metadata → Data packages, Atomic Tables, Atomic Data, pgmpy

Self-sovereign AI is a direction, not a finished product category. Combining local inference with open data practices reduces several centralized risks at once, but expects more operator effort than clicking "accept" on a terms-of-service popup. Starting small — one self-hosted assistant, one offline tool, one documented dataset — still builds skills and fallback capacity before the next API price hike or policy change.