AI integration
Add summaries, classification, and assistance to current systems.
- Model APIs
- RAG
- Workflow
AI integration · AI Agent · On-premise
AI assists the right work. People retain decisions.
Integrate AI into existing operations with explicit data, access, evaluation, and approval points.
0115 years
Founder experience
0210 years
ZiniSoft serving clients
0360 / 40
Vietnam / international clients
AI integration · AI Agent · On-premiseLive system
Operational overviewAI assists the right work.
Founder experience15 years
ZiniSoft serving clients10 years
Vietnam / international clients60 / 40
✓ Business problem before technology✓ Standardize before automation✓ Human control where it matters
Solution capabilities
Do not start with a chatbot. Start with the work to improve.
Every use case needs explicit inputs, outputs, and ownership.
AI Agent
Coordinate tools and tasks inside controlled boundaries.
- Tool access
- State
- Human approval
AI on-premise
Deploy around internal data, infrastructure, and operating constraints.
- Private data
- Model serving
- Monitoring
Evaluation and guardrails
Measure quality, cost, exceptions, and unwanted behavior.
- Test sets
- Observability
- Fallback
One unified system
AI operates inside an observable workflow.
Inputs, tools, outputs, and approval points each have explicit roles.
01Context
Data · Permission
→02Reasoning
Model · Retrieval
→03Action
Tools · Workflow
→04Control
Review · Audit
Delivery scope
Four conditions before automation.
A stable process
Known inputs, steps, and exceptions.
Usable data
Sources, access, quality, and retention.
Evaluable outcomes
Explicit criteria and representative test sets.
Control mechanisms
Permissions, limits, approvals, and fallback.
How ZiniSoft works
From a real problem to an operable system.
Three phases control scope, risk, and outcomes before expansion.
Goals and current state
Clarify what must change, along with current processes and data.
Output · Bottleneck mapPriorities and operating model
Align responsibilities, work states, and data flows.
Output · Delivery scopeAn improvable implementation
Build, integrate, and measure results before scaling.
Output · An operable systemDelivery evidence
Verified results instead of oversized promises.
One F&B operating system for 28 stores and more than 120 regular staff users serving the Vietnamese community in Australia.
About USD 2M in revenue in 2025Frequently asked questions
What to clarify before we begin.
Can internal data remain private?
Private or on-premise options exist, subject to model, infrastructure, and operating cost assessment.
Will an AI agent be fully autonomous?
Not by default. Autonomy depends on the risk and testability of each action.
How do we know AI creates value?
Compare time, quality, cost, and exceptions before and after on one defined use case.
Start with a real problem
You do not need to know exactly what to build.
Start with the process causing friction. ZiniSoft will help identify the bottleneck, priority scope, and an appropriate first experiment.