01 / DATA CONTROL
Where does the data go?
Patient records, financial documents and confidential information come with restrictions on location, access and retention. Generic AI deployments may not satisfy them.
Founding Design Partner Program3 partner places open
Powerful AI.
SOVEREIGN AI INFRASTRUCTURE
Powerful AI.
Within your
boundaries.
Embed advanced AI into healthcare and financial software — with open-weight models, qualified local inference and a fully managed infrastructure layer.
Tell us what you're doing with frontier AI. We'll tell you whether it can run privately, where it can run, and at what cost.
Backed by the engineering experience of Sentinel IT · Since 2006
ARCHITECTURE CONCEPT01 / PRIVATE INFERENCE
YOUR SOFTWARE
Healthcare / Financial SaaSApplication & business logic
MANAGED CONTROL PLANESentinel Private AI
Model selectionPolicy checksLifecycle operations
Deployment policyEVALUATE → ROUTE
Private cloudApproved local datacenter
On-premisesCustomer-controlled environment
Inference stays inside the approved processing boundary.
OPEN-WEIGHT MODELS
LOCAL INFERENCE
CONTINUOUS GOVERNANCE
HARDWARE-INDEPENDENT
THE CHALLENGE
AI is easy to demo.
Hard to ship responsibly.
SaaS providers are under pressure to bring AI into their products. But when customers operate in healthcare or finance, an API key alone is not a deployment strategy.
02 / COMPLEXITY
Who operates the AI?
Self-hosting a model means owning capacity planning, inference performance, upgrades, incident response and the growing security surface.
03 / CHANGE
How do you stay competitive?
Models, hardware economics and requirements change fast. Yesterday's best model can become tomorrow's expensive bottleneck.
PRIVATE AI, EXPLAINED
The power of AI.
The control of local.
Three ideas make independent, privately operated AI possible. Here's what they mean — without the jargon.
01
Private AI
AI processing within agreed privacy, security and operational boundaries — with controlled access, defined data flows and clear retention policies.
Control is designed into the entire service, not just its API.
02
Open-weight models
Trained AI models whose weights can be deployed independently on chosen infrastructure, subject to their licences — instead of calling their creator's API for every request.
Open-weight does not automatically mean open-source.
03
Local inference
Prompts and documents are processed where the AI model actually runs: on-premises or in an approved datacenter that meets the relevant geographical and governance requirements.
Local doesn't have to mean physically on your premises.
THE SENTINEL APPROACH
One managed layer.
The full AI lifecycle.
From the first workload assessment to production operations, we focus on the whole system: model quality, security, performance, cost and change.
01
Assess the workload
Understand the existing frontier-model use case, data sensitivity, quality expectations, latency, throughput and regulatory constraints.
02
Select & benchmark
Compare suitable open-weight models against representative tasks. Evaluate accuracy, hallucination risk, response times and total cost.
03
Qualify & deploy
Evaluate existing hardware providers and customer environments. Deploy only where relevant location, security and operational controls can be satisfied.
04
Operate & govern
Manage infrastructure health, access controls, software security, lifecycle operations and evidence-based reporting within agreed responsibilities.
05
Continuously optimize
Re-benchmark emerging models and configurations. Recommend validated changes that improve quality, capacity or economics without uncontrolled upgrades.
DEPLOYMENT WITHOUT OWNED HARDWARE
Your requirements.
Your deployment.
We don't sell servers. Our platform is being built to use suitable existing infrastructure, reducing hardware lock-in while keeping each workload inside its approved boundaries.
01 / MANAGED PRIVATE CLOUD
Private cloud inference.
Deploy open-weight models on qualified, rented GPU capacity in appropriate local datacenters. Controlled environments without buying or maintaining your own hardware.
Existing cloud capacity Jurisdiction-aware
02 / MANAGED ON-PREMISES
Your environment. Our expertise.
Run inference on customer-controlled infrastructure while using the same disciplined model evaluation, lifecycle management and governance approach.
Customer-controlled hardware Managed lifecycle
Important distinction: a local datacenter address alone does not establish sovereignty or compliance. Eligibility depends on the full service, including legal jurisdiction, administration, subcontractors, data handling and evidence.
BUILT FOR REGULATED SOFTWARE
Your product. Their trust.
Our infrastructure.
We work with SaaS companies bringing AI into sensitive workflows — not to replace their products, but to power them.
01 / HEALTHCARE
Healthcare software
EPD / EHR systems, care platforms and medical-administration SaaS integrating assistive AI with patient-data safeguards.
INITIAL WORKLOADS TO EXPLORE
- Medical document summarization
- Structured information extraction
- Secure record retrieval
- Administrative drafting assistance
02 / FINANCIAL SERVICES
Financial software
FinTech, RegTech and insurance platforms embedding AI into confidential financial-document and operational processes.
INITIAL WORKLOADS TO EXPLORE
- KYC and onboarding document analysis
- Financial statement extraction
- Insurance and credit-file summarization
- Compliance knowledge retrieval
Initial focus: document and workflow intelligence. Clinical recommendations, autonomous credit decisions and other high-risk applications require additional, use-case-specific assessment.
PRIVACY, SECURITY & GOVERNANCE
Sovereignty is more
than a location pin.
Private inference should be demonstrable — not assumed. We design around technical controls, qualified infrastructure, contractual requirements and continuous evidence collection.
Discuss your requirementsDESIGN PRINCIPLESGovernance by architecture
PROPOSED CONTROLSQualified processing locationWhere data runs, who controls it and who can access it
Zero content retention by designNo persistent prompts or outputs where verified
Content-free operational telemetryMonitor health and costs without collecting request bodies
Auditable operating modelEvidence, access controls and validated change management
FRAMEWORKS WE DESIGN TO ACCOUNT FOR
EU GDPR
ISO 27001
NEN 7510
DORA
EU AI ACT
References to frameworks indicate relevant assessment considerations, not certification or a claim that this proposed service already complies with every requirement.
A specialist AI platform.
Built on real engineering.
Sentinel Private AI is an initiative of Sentinel IT B.V., a Netherlands-based software engineering company founded in 2006. Our foundation is enterprise software delivery, information security and practical AI engineering.
We're translating that experience into a standardized managed inference platform for SaaS providers — not another custom software agency or GPU reseller.
Meet Sentinel IT ISO 27001-certified parent company*
* Sentinel IT B.V.'s existing certification must not be interpreted as certification of the proposed Sentinel Private AI service or any particular deployment.
APPLICATIONS OPEN · 3 PARTNER PLACES
Let's build the right
private AI together.
We're inviting three ambitious healthcare and financial SaaS companies into our Founding Design Partner Program.
Bring a real workload. We'll explore whether open-weight AI can meet your quality, security, performance and cost requirements — and map a credible path to deployment.
01
Direct engineering collaborationExplore your actual workflows, not generic demos.02
Real technical and economic evaluationReview models, infrastructure constraints and indicative costs.03
Influence the platform's directionHelp define the control plane around real production needs.Initial scoping is without commitment. Any benchmark engagement, pilot or production deployment will be scoped and agreed separately. Admission is selective, not first-come-first-served.
FOUNDING PARTNER APPLICATION
Tell us about your AI use case.
A few details are enough to start. Please don't include patient, financial or other sensitive records.
Can open-weight models actually replace frontier models?
For some tasks, yes — for others, not yet. We assess each workload against representative examples and compare task quality, latency, reliability and cost. We will recommend staying with a frontier provider if the private alternative isn't ready.
Does private AI have to run on-premises?
No. It can run on premises or on appropriate cloud capacity in a datacenter. A deployment qualifies only when its complete technical, contractual, jurisdictional and operational controls meet the customer's requirements.
What exactly does zero data retention mean?
Inference necessarily processes data temporarily. Our design objective is zero persistent storage of prompt and response content: no content logging, training, backups or retained request bodies. Any such guarantee requires verification of the complete deployment, including the underlying provider. Ordinary business contact correspondence is separate from inference data.
Do you own or operate datacenter hardware?
No. The model is hardware-independent: we qualify and use existing provider capacity or customer-owned infrastructure. Our own value lies in model evaluation, deployment orchestration, governance, security and continuous operations.
Are all deployments automatically compliant with GDPR, NEN 7510 or DORA?
No. Requirements differ by customer, use case, jurisdiction and provider. We aim to assess and evidence the relevant controls. A software layer or local GPU alone does not grant legal compliance or third-party certification.
What happens after the founding partner application?
We review the use case and commercial fit, arrange an introductory technical discussion where appropriate, and outline a potential assessment or pilot. Initial exploration is non-binding; any funded work and deliverables are agreed separately.
THE NEXT STEP