SenangAI — your data, your infrastructure, your control
SenangAI

Sovereign Enterprise AI, governed by design.

Your data. Your infrastructure. Your control.

Connect organisational knowledge, answer with evidence and run approved workflows inside infrastructure you control.

On-premises · private cloud · sovereign region · hosted · multilingual

SenangAI / organisational memoryGovernance active
Request → policy → evidence

The Sovereignty Commitments

01 · Your dataIt runs inside your perimeterYour data centre, your tenancy, or a sovereign region. Retrieval stays local in every mode.
02 · Your modelsNothing runs unless you approve itRegister open-weight models on your own hardware, regional endpoints, or approved commercial APIs. Anything else is absent.
03 · Your rulesScope and approval are enforcedIdentity, scope, budget and human approval are checked before an answer is produced, not applied to it afterwards.
04 · Your proofIt can show what it didQuestion, sources, model, cost, approver and denied attempts — recorded as work happens, exportable for audit.

These four hold in every deployment mode. If any of them stops being true for your configuration, that is a defect — not a setting, and not a tier.

The system

Your rules, enforced by architecture.

Governance is not a policy beside the AI. Approved knowledge enters through governed scopes, becomes organisational memory, and powers answers and action — with identity, scope, approval, budget and audit applied before anything runs.

SenangAI / platform architectureGovernance active
1Data sourcesConnect approved knowledge
Files & PDFs
Drives & suites
Chat & wikis
Databases
APIs & webhooks
2Governed scopesBoundaries at every layer
OrganisationEnterprise memory
TeamspaceShared, role-aware knowledge
PersonalPrivate context
Permissions · provenance · deletion
3SenangAI engineCited knowledge and controlled action
CiteApproveAuditScope
SenangAIAsk · see · connect · automate
4Business outcomesTrusted answers to safe action
Cited answers
Knowledge graph
AI assistants
Agents & workflows
Reports · APIs · institutional memory
Where it fits

You probably already have something.

Most organisations we meet already have a general assistant somewhere and a search tool in the stack. They each do a real job, and they are not the same category of thing — so it helps to be clear about which job you are trying to fill.

Good at draftingGeneral assistants write well and know little about your organisation. Keep them — they are not the system of record.
Good at findingEnterprise search respects permissions and returns ten links. It answers where, not why.
Good in a demoAn in-house RAG prototype ships in a fortnight and stalls on permissions, audit and cost control.
Answers and actsSenangAI reasons across sources, returns the evidence, and runs the work under one scope, approval and audit model.

If a general assistant already solves your problem, we will tell you that in the first call. This is worth deploying when the answer has to be defensible, the data cannot leave, or the AI needs to do something rather than say something. See the full comparison →

The product

One governed system, five moves.

Automate work with a person in the loop. Ask and get proof. Keep every workspace inside its boundary. Build agents out of skills. Then see what the system is actually doing.

01 · Automate

Workflow with human approval

Build the flow. Pause before action. A person decides what runs.

01TriggerSchedule or request
02KnowledgeScoped research
03AgentDraft the work
04Human approvalReview and decide
05DeliverOnly when approved
Ready to run
02 · Ask

Answers with proof

Ask plainly, in any supported language. Get permitted answers with sources you can check.

What changed in Project Atlas?
Project brief · p.8Decision logBudget note
03 · Govern

Governed AI workspaces

Organisation, Teamspace and Personal scopes set what people and agents can see and do.

OrganisationCompany-wide
TeamspaceShared team
PersonalPrivate context
PeopleAgentsTools
04 · Build

Build your own agents and skills

Everyone starts with a personal agent. Teamspaces add as many as they need, and each inherits scope, audit and approval the moment it exists.

Contract ReviewerBorn paused
Read the contract
Compare to playbook
Draft the summary
Legal teamspace · 4 agents · unlimited
05 · Insights

See what the AI is actually doing

What people ask, which sources answer, where the spend goes, and which teams have adopted it.

Questions answered1,284
Answers with citations98%
Spend against budget62%
What an evaluation looks like

One use case. Three weeks.

We do not run six-month discovery. Bring your compliance, risk and technology leads, pick a question your organisation already argues about, and we will show you the governed answer and the evidence behind it.

Day one

First cited answers

Upload a folder or connect a knowledge source, set one teamspace boundary, and ask real questions. No admin project, no data migration, no integration work.

You see answers with sources attached
Week one

Scope isolation proven

Two teams, two boundaries. We show a question that returns nothing because it should, and the audit record the denied attempt produced.

Your risk lead sees it fail closed
Weeks two to three

One workflow in production

Build a workflow with a real approval step, run it against real work, and review the audit trail and the spend it consumed against budget.

You decide on evidence, not a demo
Start with one governed use case

Bring the question your organisation keeps arguing about.

Thirty minutes. We will map one use case, demonstrate scope isolation, and show the audit evidence the platform produces — or tell you plainly that you do not need us yet.