The DBIZ Agentic AI product ecosystem
Introduction: from “AI that answers” to “AI that works”
Most enterprises are familiar with AI in the form of a chatbot: you ask, it replies. Stopping there makes AI little more than a more capable reference tool.
Agentic AI — AI that decides and acts on a person’s behalf within a process — is a different proposition entirely. Rather than waiting for instruction at every step, each agent is given a specific business task: it gathers the data, analyses it, carries out the work and raises an alert when something is wrong.
A sales chatbot only answers “this product is in stock”. A smart order agent, by contrast, looks at the customer’s purchase history, anticipates what they will need next, drafts a proposed order and prompts a member of staff to approve it. One is a gatekeeper that can speak; the other is an assistant that can work.
This is the direction DBIZ — with more than 18 years delivering digital solutions to over 100 enterprises — is building into a full product ecosystem.
The overall picture: four pillars plus a device layer
The DBIZ ecosystem is not a single AI product but a set of products with AI agents built into each business area:
- DBIZ ERP + AI Agent — enterprise-wide management (finance, inventory, production, HR). ERP (enterprise resource planning) is the backbone holding all operational data.
- DBIZ CX + AI Agent — customer experience management, comprising 11 modules: CDP, sales forecasting, omni-channel selling, customer service, marketing automation and more.
- DBIZ Data + AI Agent — intelligent data analysis, turning raw figures into insight that can be acted on.
- DBIZ Autonomous — the command layer, bringing the 97 AI agents from all three pillars into one unified system.
Alongside these sit microSME (a compact package for small businesses), together with the IoT device layer and robotics — reaching the plant and the shop floor, not only office software.
Data lives in the ERP and CDP → agents read that data to make decisions → the results are written back to the system. Agents are not a separate AI layer bolted on for appearance; they live within the operational data flow itself.
The heart of the ecosystem: DBIZ Autonomous — 97 AI agents
This is the product where the agentic character is clearest. DBIZ positions it as a complete AI agent ecosystem for the enterprise, with 97 agents organised into nine specialist groups, each covering one domain:
| Business area | Agents | What the agents do (examples) |
|---|---|---|
| Finance & accounting | 12 | Invoice processing, cash-flow forecasting, payment reconciliation |
| Supply chain & logistics | 11 | Inventory optimisation, supplier assessment, delivery route optimisation |
| Production (MES/APS) | 6 | Production scheduling, preventive maintenance, OEE optimisation |
| HR & payroll | 7 | CV screening, payroll calculation, performance assessment |
| Sales & marketing | 13 | Sales forecasting, lead scoring, price optimisation |
| Customer experience (CX) | 11 | 24/7 chatbot, sentiment analysis, churn prediction |
| Data governance & analytics | 27 | Data quality control, predictive analysis |
| Integration & automation | 9 | Data synchronisation, API monitoring |
| General & other | 12 | Access control, audit logging, regulatory updates |
One neat detail in how DBIZ names its agents: the name tells you the agent’s level. The suffix Bot denotes basic automation (for example InvoiceBot for invoice processing); Pro denotes a professional level (CashFlowPro for cash-flow forecasting); Master denotes the senior level (AuditMaster for audit); the Scout / Guard / Watch family handles monitoring and alerting; and Optimizer / Wise agents specialise in optimisation. The name alone tells you whether the agent’s work is simple or complex.
The core difference: AI running on your own infrastructure
This is the most notable aspect, and a deliberate strategic choice by DBIZ. Most AI solutions today call out to an external cloud service (such as ChatGPT or Google AI) — meaning enterprise data must leave the organisation, and cost is billed by usage.
DBIZ Autonomous takes the opposite approach: it uses local models running on internal servers, deployed on-premise on the enterprise’s own infrastructure. In practice this means:
- Data never leaves your system — which matters in sensitive sectors such as finance, healthcare and manufacturing.
- No usage-based API fees — a fixed cost that is straightforward to budget for. DBIZ estimates that for 500 users a year, a cloud solution costs in the region of USD 45,000–60,000, whereas the on-premise model keeps the cost fixed.
- It works offline — with no dependency on connectivity or an external provider.
In short: you trade the plug-and-play convenience of the cloud for control of your data and predictable cost. For many Vietnamese enterprises, that is a trade worth making.
Outcomes and the delivery roadmap
DBIZ cites expected outcomes of a 60–80% reduction in manual processing time, a productivity gain of around 40%, and a return on investment within 6–12 months.
Delivery is contained within roughly three to four weeks, in four straightforward steps:
- Weeks 1–2 — Assessment: analysing current processes, selecting the right agents and designing the integration architecture.
- Weeks 2–3 — Installation: building the internal infrastructure, integrating with the existing ERP and configuring the AI models.
- Weeks 3–4 — Go-live: user training, user acceptance testing (UAT), then formal operation.
- From month 2 — Extension: performance tuning, adding new agents and rolling out across the organisation.
This step-by-step approach reflects the nature of an ecosystem: there is no need to replace everything at once. Choose a few agents that address a real pain point, get them running reliably, then extend.
In closing: an ecosystem, not a set of isolated features
What gives this approach its value is not the figure of 97 agents, but the fact that they share one data foundation (ERP plus CDP) and one operating philosophy: run internally, and your data stays yours.
A single agent is only a utility. But when the sales forecasting agent can talk to the inventory optimisation agent, and that agent in turn connects to the production scheduling agent, the enterprise gains a digital workforce operating continuously behind the scenes, rather than a handful of disconnected smart features.
That is the difference between buying an AI tool and building an Agentic AI ecosystem — and it is the problem DBIZ is solving.
Find out more and request a demo at dbiz.com.