Why companies can't compare features alone
Most enterprise AI platforms can chat, answer questions and summarise documents, so their feature lists look much the same. The real differences lie in the constraints of the enterprise environment: whether data can be sent outside, what people in different departments are allowed to see, whether you can find out why the AI gave a wrong answer, and whether costs can spiral. These issues are not obvious during a trial, but they often block a platform just before go-live.
8 evaluation criteria and the questions to ask vendors
| Criterion | Questions to ask the vendor |
|---|---|
| 1. Deployment model | Is fully on-premises deployment supported? When a cloud model is used, which data is sent outside? |
| 2. Permission control | Can access to knowledge bases and features be set by role and by individual? Does revoking access take effect immediately, and are existing conversations controlled too? |
| 3. Cited answers | Do knowledge answers cite the document, page and paragraph? When the evidence is insufficient, does the AI say so clearly rather than filling the gap itself? |
| 4. Traceability | Can you see who used which model, when, and what they did? Can the records support an audit? |
| 5. Tool control | Must external tools the AI calls be approved first? Can they be limited to certain roles or scopes of action, or set to read-only? |
| 6. Usage control | Can quotas be set per system and per user? Are you notified as limits approach? Can costs be traced to features and models? |
| 7. Chinese-language and local support | How well does it handle Traditional Chinese documents, tables and scans? Is local deployment and technical support available? |
| 8. Rollout approach | Can you adopt just one theme and run a PoC on real data? When expanding, is the same governance standard reused? |
What Taiwanese companies should watch for
- Traditional Chinese documents and scans: many policies, specifications and forms are still scanned PDFs, so confirm OCR and table-handling capability.
- Personal data protection: check whether personal data can be masked when importing into the knowledge base, and whether conversation retention periods and deletion records can be set.
- Integration with existing systems: check whether ready-made integrations exist for ERP, MES, CRM, phone systems (PBX) and LINE.
- Local support: on-premises deployment needs installation, operations and troubleshooting, so check whether the vendor has a local team.
Recommended evaluation process
- List your most urgent business use cases, such as audit preparation, document comparison or repair request intake.
- Take stock of the data sources, data quality and permission requirements for that use case.
- Pick one use case for a PoC, validate it with real data and agree success metrics in advance.
- Check governance: whether permissions, records, tool approval and usage meet company policy.
- Once the PoC meets its targets, expand to the next use case, reusing the same governance standard.
How NOVARK helps
NOVARK is an enterprise AI agent platform. Against the 8 criteria above: the platform supports fully on-premises deployment; permissions are set by role and by individual, and revocation takes effect immediately; knowledge answers cite the document, page and paragraph; a single trace ID links user actions, model calls and execution records; external tools must be approved first; and usage quotas can be set per system and per user. The platform offers six themed applications: ISO management system and audit, Intelligent document management, Intelligent inspection, Intelligent finance, Intelligent customer service and Intelligent war room, and each theme can be adopted on its own.
NOVARK Audit
NOVARK Doc
NOVARK CommandFAQ
Does an enterprise AI agent platform have to be deployed on-premises?
Not necessarily. If your data is highly sensitive or regulations prohibit it from leaving the company, on-premises deployment is the better fit; for general office work, cloud models can also work. What matters is knowing exactly where data flows under each deployment model.
What should a PoC validate?
Validate three things together: answer quality on real data, the feasibility of integrating with existing systems, and success metrics agreed in advance, such as how much processing time is cut or how far error rates fall.
Can we adopt one feature first and expand later?
Most companies start with a single use case. When choosing a platform, confirm that each theme can be adopted independently and that the same permission and governance settings carry over as you expand.
This article is general guidance. Adapt the approach to your organisation's size, industry regulations and data. NOVARK is an enterprise AI agent platform.