Deployment & Data Sovereignty
Deploy AI you’re actually allowed to deploy.
Clarity runs on-premise, inside your border, on your hardware, with the documentation your regulator, your board and your own security team will accept.
Your infrastructure
Your models
Your data, never ours
A paper trail that survives review
When the regulator decides.
Not the budget.
Grouped by mandate, not geography, because the mandate is what the deal turns on. Anonymised.
Capital markets & central banks
A financial services firm is replacing its incumbent platform outright because the regulator requires data to sit in-country. A central bank rejects any solution where data leaves the country, even anonymised.
Banking supervision
PDPL and SAMA documentation gates the deal before commercials are discussed. The security review is the sales cycle.
Government & data classification
Tier-one classified data, with hosting gated on national AI-authority compliance before any pilot can begin.
Group data governance
“Data is very sensitive and cannot be shared with any AI.” “One mistake costs a lot of money.” The objection is rarely technical.
Clear rules, strict guardrails.
Human oversight.
Reference Architecture
The same topology everywhere.
Component view, identical across SaaS, in-region cloud and fully on-premise. The components don’t change between deployment modes. Only where the boundary sits.
Same topology everywhere. The components don’t change between SaaS and on-premise, only where the boundary sits.
Model & Data Control
Your models. Or ours. Your call.
GPU inference in-house on open-weight models, a cloud provider, or your own endpoint. The orchestration layer doesn’t care which you choose, and neither does your architecture.
Open-weight models, in-house GPU
Your own hosted endpoint
In-region managed endpoints
No third-party model dependency required
AI Agent Hub
Models
Tools
MCP and function calling
Gates
confidence threshold, handover
Retrieval
Vector store
Storage
Audit
Topology unchanged
PII is removed before anything is stored.
Not masked in the UI. Not redacted on export. Removed at ingestion, before classification, before embedding, before it touches a database.
Inbound
Hi, this is Dana Al-Rashid, ID ID-4471-0392, phone +00 555 0142. My card hasn’t arrived.
Ingestion
Hi, this is ██████ ⟨name⟩, ID ██████ ⟨national-id⟩, phone ██████ ⟨phone⟩. My card hasn’t arrived.
classified
sentiment
embedded
Storage
Hi, this is ██████, ID ██████, phone ██████. My card hasn’t arrived.
Raw PII never crosses.
Synthetic example. Removal happens at ingestion, so the unmasked text has no path to a database.
A paper trail that survives review.
Every AI and human action logged, timestamped and exportable, in the format a reviewer asks for.
Audit trail
09:14:02
system
Conversation ingested, PII removed
#a91f
09:14:03
AI
Intent classified: card delivery
#b7c4
09:14:06
AI
Answer grounded in KB article 4471
#c02e
09:15:41
agent
Response approved and sent
#d55a
09:16:10
AI
Conversation scored against rubric
#e18b
09:16:11
system
Trail sealed and exported
#f3d0
Exportable, timestamped, immutable
Compliance matrix
What your reviewer will ask for
SOC 2
AES-256 at rest
GDPR & HIPAA ready
TLS 1.2+ in transit
PDPL aligned
Role-based access
NIST and ISO/IEC 42001 alignment
Full audit logs
99.9% uptime SLA
DR plans tested
Cyber insurance
Published subprocessors
How a deployment actually runs.
Four phases, and an honest statement of where the time really goes.
Phase 01
Scoping
We do: architecture review, data-flow mapping, prerequisite form.
You provide: classification policy and security contacts.
Phase 02
Provisioning
We do: deployment manifests, model serving, hardening guide.
You provide: hardware or tenancy, GPU capacity, network access.
Phase 03
Integration
We do: channel and CRM connectors, ingestion, PII rules.
You provide: service accounts and access approvals.
Phase 04
Validation
We do: audit-trail walkthrough, DR test, sign-off pack.
You provide: reviewer time and acceptance criteria.
Deployed, not roadmapped.
Scoped and priced on-premise deployments exist today, in regulated financial services. We’ll walk your team through one under NDA.
The questions your security team will ask.
Is on-premise the same product as your SaaS?
Yes. Same components, same orchestration, same audit trail, the deployment boundary moves, the topology does not. That is the point of the reference architecture: an architect who sees the diagram restructure between modes should conclude they are being sold a different, less-tested product.
Can we run without any third-party model provider?
What exactly leaves our network?
Where is PII removed?
What standards do you hold or align to?
What does the audit trail actually contain?
What hardware do we need to provision?
How long does a deployment take?
What happens if we want to leave?
Clarity
The question was never whether the AI is good.
It was whether you’re allowed to run it. And whether the answer holds up when someone asks you to prove it.
Talk to our experts