On-premise
Your data and models stay on your servers.
Private AI infrastructure · installed where you work
Your servers.
Your AI.
We install AI on your organization's own infrastructure. We handle the architecture, the infrastructure and the solution together, and run it in the cloud if you prefer.
- On-premise
- Approval first
- Sourced answers
- Turkish
Architecture. Infrastructure. Real solutions.
The non-negotiables
Trust is an architecture
Not a promise in a policy document. Three controls are designed into the system before a single user arrives.
Approval first
AI prepares the work, you approve it.
Fits your organization
Your services, rules and permissions are defined in the admin panel.
The real starting point
Trying AI is easy.
Running it inside your
organization is hard.
Organization boundary
01 / Data
Data cannot leave
Public tools turn every question into an external transfer. Regulated work needs a closed loop.
02 / Control
AI must not write on its own
Generated work needs a source, a policy and a named human decision before it changes anything.
03 / Coherence
Every team builds its own tool
Isolated pilots create scattered models, permissions and costs nobody can see.
What Bizz AI is
Products and services for AI that runs inside your organization.
01 / Workflow
From question to action
AI prepares the steps, tools and draft. Nothing moves forward until the right person approves it.
02 / Evidence
Sourced answers
Every claim can point back to the document, record and permission that produced it.
03 / Governance
Your own permissions
Roles, models, data sources and actions reflect the organization you already run.
04 / Improvement
Measured updates
Usage, quality, cost and approval patterns are visible before the next release.
The platform
Four layers, one
infrastructure.
Stack view / inside your perimeter
One boundary.
Every layer.
Data, models, agents and the employee experience remain under one operational and security model.
How we work
Assess → install → operate.
A short path from architecture to a system your teams use every day.
01 / Understand
Assess
We map your data, rules, systems, risks and the use cases worth doing first.
Architecture brief
02 / Deploy
Install
We configure the AI server, models, permissions, connections and workspace.
Production-ready
03 / Improve
Operate
We monitor quality and usage, train teams, and release measured updates.
Continuous ownership
Services
Ten services.
Four disciplines.
Service atlas
One team around the full AI stack.
From the first server drawing to the last permission rule, every piece is designed to work together.
01 / 3
Infrastructure
The private foundation.
- AI server architecture
- Model & GPU operations
- Security & networking
02 / 3
Solutions
Products shaped around real work.
- Sourced knowledge
- Agent workflows
- Employee workspace
03 / 2
Integration
Connected to your systems, not beside them.
- Enterprise connectors
- Identity & permissions
04 / 2
Advisory
Direction, adoption and measurable value.
- AI roadmap
- Training & adoption
The decision
Three paths,
three outcomes.
Path 01 / Convenience
General AI tools
Fast to try, but data leaves, permissions are shallow and every team creates a different habit.
OutcomePilot fatigue
Path 02 / Control
Build in-house from scratch
Full control, but your team also owns every model, queue, connector, interface and failure mode.
OutcomeSlow time to value
Path 03 / Bizz AI
Bizz AI
A private, integrated stack installed on your infrastructure — with approval and evidence built in.
OutcomeAI in production
FAQ
Questions,
answered plainly.
No. In the standard on-premise architecture, models, documents, embeddings, prompts and logs stay inside the boundary you define. Cloud deployment is available when you explicitly prefer it.
Yes. The runtime can be configured without external model or data calls when your environment requires a fully isolated deployment.
Policies identify which steps require a named reviewer. The AI prepares the work and an authorized person releases the action.
Yes. Answers can link to the source document, record, permission and workflow evidence used to produce them.
Yes. Connectors and identity mapping are designed around your existing enterprise systems, not alongside them.
The same governed architecture can be deployed into a private cloud when that is your preferred operating model.
Your infrastructure is the starting point
Want to see it on
your own servers?
We'll map your architecture, show the products in your environment, and define the shortest safe route to production.