Self-hosted & edge AI
Intelligence that stays on your premises.
Maurits Embedded Systems helps organisations leverage private AI that runs locally — in your server room, office, home, or directly on embedded and IoT hardware — so confidential information never needs to leave your control.
When AI leaves your network, your risk profile changes
Cloud assistants can be extraordinarily capable — but they can also route sensitive prompts, documents, and metadata through systems you do not operate. Providers may retain data for safety monitoring, quality review, abuse prevention, or longer-term product improvement — including training and evaluation workflows where human reviewers may be in the loop.
For many teams, the question is not whether a vendor is trustworthy — it is whether your organisation can accept those default data flows at all.
Security best practice, privacy standards, and sensible AI governance
Self-hosted AI only earns trust when the surrounding discipline matches the sensitivity of the data: clear ownership, controlled integrations, retention you can explain, and monitoring that supports investigation without turning every log line into a new risk. We help you keep prompts, context windows, and model outputs inside the networks and devices your organisation already governs.
Privacy by design — local inference, deliberate architecture
We help you deploy models and pipelines that run where your data already lives: on dedicated servers, secure office networks, private appliances, and resource-constrained embedded platforms. The objective is simple — powerful AI without exporting confidentiality to someone else's cloud.
- Data residency aligned to your policies — including air-gapped patterns where required
- Operational control: upgrades, logging, retention, and access boundaries you define
- AI governance baked into delivery: roles, approvals, and change control you can evidence
- Edge-first options for latency, bandwidth, and offline continuity
Embedded & edge
Bring inference closer to sensors and controls — without widening your attack surface to the public internet.
Built for teams that need outcomes — not another SaaS dependency
Self-hosted AI stacks
Design, hardening, and handover for on-prem and private cloud inference — tuned to your privacy standards and internal security policies.
Edge deployment
Practical pipelines for offices and devices where bandwidth, latency, or continuity matter — with sensitive prompts and artefacts kept off the public internet.
Security-minded delivery
Architectures that assume sensitive workloads: least privilege, encryption, logging you can trust, and minimised data movement by default.