Designed for sovereign AI architectures
Sovereign AI starts with sovereign data
Run your AI where your data lives. SQL, vectors, graph, ML, and local LLM inference in one self-hosted engine. Your infrastructure. Your data. Your control.
Not legal advice. SynapCores describes the technical controls it provides; confirm regulatory obligations with your own legal and compliance team.
Self-hosted
One binary or container, running on infrastructure you control — not a managed service you depend on.
No mandatory external AI API
AI operations run inside your deployment by default. Nothing has to call out to an external model to work.
Local LLM inference
Embeddings, generation, and agent reasoning execute on your hardware, next to your data.
SQL + vectors + graph + ML
One engine, not four stitched systems — a smaller footprint and a smaller attack surface.
Deploy anywhere you need to
On-prem, private cloud, EU sovereign cloud, or fully air-gapped — same engine, same behavior.
Why this matters right now
AI Act compliance, cloud portability, and open-source preference are converging with a specific European industrial policy push toward sovereign infrastructure. This is current policy direction, not an old data-residency talking point.
$80B
Forecast worldwide sovereign-cloud IaaS spend in 2026, up 35.6% year over year — government, regulated industries, and critical infrastructure lead.
€180M
European Commission sovereign-cloud procurement, explicitly intended to encourage sovereign digital solutions.
Aug 2026
The Commission’s enforcement powers for GPAI obligations under the AI Act took effect — sharpening the case for architectures with fewer external AI dependencies.
The sovereign AI stack
One engine sits between your agents and your data — and nothing below the customer boundary has to leave.
Enterprise applications
Your product, your internal tools
AI agents
Reasoning, retrieval, task execution
SynapCores
SQL · vector · graph — one engine, one boundary
Local / EU LLM
Inference stays inside the deployment
Enterprise data
Rows, documents, embeddings — never exported
Customer boundary
Nothing has to leave
The same deployment, wherever you need it to run:
Laptop
On-prem server
EU sovereign cloud
Air-gapped
How much sensitive data does your AI architecture send outside your infrastructure?
Most “AI-ready” stacks export data at every hop. SynapCores collapses the hops into one boundary.
The stitched stack
-- The stack most "AI-ready" architectures ship today:
-- Database --(export)--> Embeddings API
-- --(export)--> Vector DB
-- --(export)--> Graph DB
-- --(export)--> LLM API
--
-- Four vendors, four contracts, four places your data can leave
-- your infrastructure — and four jurisdictions to account for.SynapCores — one engine
-- The same capability, inside the boundary:
CREATE TABLE contracts (
id INT PRIMARY KEY,
clause TEXT,
embedding VECTOR(384) -- EMBED() runs on local inference
);
INSERT INTO contracts (id, clause, embedding)
VALUES (1, 'Data processed under this agreement shall not leave the EU.',
EMBED('Data processed under this agreement shall not leave the EU.'));
-- Vector search, graph traversal, and agent reasoning — same engine,
-- same connection, nothing exported to a third-party AI service:
SELECT AGENT_RUN('compliance-assistant',
'Find clauses in contracts related to cross-border data transfer
and flag any that conflict with our data-residency policy.'
);The questions a CISO or DPO asks first
Where does our data go?
Nowhere it does not have to. Local inference and one self-hosted engine mean no cloud model and no hosted vector store are required in the path.
What jurisdiction governs it?
The one you deploy in. Run on-prem, in an EU sovereign cloud, or air-gapped — you choose the legal and physical boundary, not a vendor’s default region.
Can we prove what the AI did?
Every agent action can write to a hash-chained, tamper-evident audit table. VERIFY TABLE proves the record was never altered.
How many vendors are now in scope?
One engine instead of a database, an embeddings API, a vector store, a graph database, and an LLM API — one accreditation boundary, not five.
Built for the industries where sovereignty is non-negotiable
Sovereignty pressure is highest where the buyer is a CTO, CISO, CDO, Head of AI, DPO, or enterprise architect in a regulated vertical — not a general-purpose SaaS team.
Run it yourself
Every claim above is a runnable recipe against the free Community Edition — certified on the latest release.
The data engine for sovereign AI
- SQL, vectors, graph, ML, agents, and local inference in one deployable — one accreditation boundary, not five vendor contracts.
- No mandatory external AI API means no forced cross-border data transfer for AI operations to work.
- Deploy on-prem, private cloud, EU sovereign cloud, customer VPC, edge, or air-gapped — the engine behaves the same everywhere.
- Every agent action can land in a tamper-evident audit table, verifiable with VERIFY TABLE, for oversight and review.
5-email course · free
Ship an agent with persistent memory in 5 emails
A 5-email course where you build a real agent with persistent memory — one runnable SQL recipe per email, no LangChain, no Pinecone, no Redis. Lesson 1 hits your inbox the second you sign up. After that: a new recipe every Monday. No spam.
Free forever. We'll never share your email.
The EU AI Act does not require self-hosting, and SynapCores does not certify AI Act compliance. What SynapCores provides is a set of technical controls — local inference, no mandatory external AI API, one self-hosted engine — that support the sovereign AI architectures European policy is now pushing toward. Confirm your specific obligations with your legal and compliance team.
Your AI shouldn’t require exporting your data to somebody else’s AI infrastructure
SynapCores runs the intelligence next to the data. Request a sovereignty briefing, or self-host the free Community Edition today.