How to buy private, permission-aware enterprise AI.
Plain-language notes for teams that need AI without sending sensitive knowledge outside the infrastructure and regions they control. Keep the distinctive product. Pair it with the words buyers actually search.
Sovereign AI for regulated enterprises
Sovereign AI is not a model brand. It is a control question: who holds the data, where the models run, who may see an answer, and whether a person must approve an action. Banks, insurers, healthcare groups and public institutions already know the failure mode. A capable assistant that cannot show its sources, or that trains in a region they do not control, is not usable.
SenangAI is built for that buyer. Data stays in your data centre, your tenancy, or a sovereign region. Models run only if you register them. Rules are checked before an answer is produced. Proof is recorded as the work happens. That is the difference between a chatbot you cannot take to a regulator and a knowledge layer you can.
If chat already drafts well enough, keep it. Read where SenangAI fits next to chat, search and custom apps.
Private and on-premises AI knowledge base
A private knowledge base is not a folder of PDFs with a chat box on top. It is enterprise knowledge that stays related: people, projects, decisions, contracts and obligations, connected as they actually relate at work. Ask “why” or “who decided” and those links are the answer, with sources.
On-premises, private cloud and hosted deployments share that same memory model. The knowledge layer moves with you. It is not rebuilt when you change where the compute sits. Retrieval stays local. Indexes stay with the knowledge. See how Understand works on the platform and how deletion and regions are designed.
Permission-aware enterprise search
Most enterprise search finds documents and, at its best, respects the permissions on those documents. That is necessary. It is not sufficient when the job is an answer. Permission-aware retrieval in SenangAI checks identity and scope before anything is read. A person in finance does not receive a legal memo as a “helpful extra”. A denied request produces an audit event, not a leaked paragraph.
Search still has a place: finding a known file. SenangAI sits under that when the question spans sources, the permissions must hold, and the answer must arrive with citations. Legal, finance and IT jobs on the use cases page are written that way on purpose.
AI governance and audit trails
Governance here is not a policy PDF. It is identity, scope, budget and human approval, checked in that order, on every request. The audit trail can show what was asked, which sources were used, which model ran, what it cost, who approved it, and which attempts were denied.
That record is exportable. It is produced as work happens. If your risk lead cannot see a question fail closed, the product is not ready. Week one of an evaluation is designed to prove exactly that. Read the Govern capability.
Human-in-the-loop AI workflows
Agents in SenangAI are assembled from skills you tick. Unticked skills do not exist for that agent. Workflows can pause before action. A person decides what runs. Every agent is created paused, inside the scope of the people allowed to use it.
That is human-in-the-loop without a custom app for each job. The person who understands the work writes the instructions. Engineering is not the bottleneck. See how agents are built.
Three-week evaluation checklist
Use this if you are not ready for a founder call, or if you want a shared list before one.
- Name one question the organisation already argues about.
- Name the two teams who must not see each other’s knowledge.
- Bring the compliance, risk and technology leads to the same session.
- Day one: connect one source, set one boundary, ask real questions, inspect citations.
- Week one: prove a denied request and export the audit event it produced.
- Weeks two to three: run one workflow with a real approval step and review spend against budget.
- Decide on that evidence. Do not decide on a slide.
When you want us in the room, book a demo. If you need the control model in writing first, start with security and deployment.