NAD designs and deploys AI Agent running on enterprise infrastructure to support document lookup, Q&A on internal data, edit content, automate processes and connect existing systems. Data, conversation history, access rights and agent configuration are all within the company's control.
✓ Internal deployment · ✓ SSO connection & decentralization · ✓ There is censorship and audit
Suitable for
The entire AI Agent system is built on On-premise infrastructure and can operate in an Air-gapped model. Contextual training data, sensitive documents, user conversations, prompts, connection tools, and audit logs are saved in the internal system.
AI only creates value when it understands business data, complies with access rights, and can manipulate real processes.
Processes, contracts, quotes, human resource policies and technical documents are located in many places, causing personnel to waste time searching and easily use the wrong version.
Customer data, finances, contracts, operating formulas and internal information need to be tightly controlled and are not suitable for sending via third-party platforms.
Many chatbots only respond in general terms, do not know user rights, cannot call internal APIs, and do not have an approval mechanism before performing important tasks.
The solution focuses on operational usefulness: document inquiries, process automation, system connectivity, security control and performance measurement.
Agents find information in files, wikis, processes, contracts, policies and decentralized knowledge data.
Create your own agent for sales, customer service, HR, accounting, IT helpdesk or operations with your own set of tools and context.
Agents can call APIs, look up databases, create tickets, compile reports or activate workflows according to granted permissions.
Sensitive actions such as sending emails, creating orders, updating data, or exporting reports require human confirmation.
Set limits on data, topics, tools, users, and actions to reduce the risk of incorrect answers or information leaks.
Track popular questions, successful response rates, automated tasks, and user feedback.
SMEs do not need to implement full-scale AI from the beginning. Can start from a department or a group of high-value documents.
Look up policies, orders, warranties, FAQs and interaction history to suggest quick responses to the customer service team.
Find product information, create draft quotes, synthesize customer needs and standardize consulting content.
Instructions on how to handle common problems, create tickets, look up operating documents and remind the authorization process.
Respond to HR policies, leave procedures, onboarding, training and internal forms in the correct version.
Synthesize data from Data Lake, ERP or database to create draft reports and explain business fluctuations.
Look up SOPs, checklists, error logs, maintenance instructions, and troubleshooting reminders by shift.
Paid AI platforms are often quick to deploy, but SMEs need to weigh costs by user, data location, control, and deep integration.
| Criteria | AI Agent On-premise | View features → | System connection |
|---|---|---|---|
| Select use case | Located in the company infrastructure | Depends on supplier policy | Usually saved outside the internal system |
| Usually limited | Pilot and expansion | Calculated by user, token or monthly package | Calculated by package/feature |
| Internal system integration | Customization by API and process | Need connector or high package | Internal HR Agent |
| Basic decentralization | deploy once | Depends on service pack | Limited by platform |
| Air-gapped | Compare solutions | Rare or very high cost | ||| is not normally supported Standardizing knowledge |
| Initial deployment time | Need to survey and configure | Fast if using default | Fast but little customization |
| 1 business agent | Highest | Depends on contract and data area | Vendor dependent |
NAD deploys in small rounds to control risk, measure effectiveness and gradually expand according to actual data.
Identify the first agent's departments, tasks, data, risks, and success criteria.
Collect documents, decentralize, clean data, create knowledge sources and test access.
Connect SSO, APIs, workflows, guardrails, audit logs and monitoring dashboards.
Test run with small groups, measure quality, refine prompts/knowledge and then replicate to other departments.
Start with a pilot to demonstrate value, then expand the agent by department and process.
Suitable for testing a use case with clear data, few integrations, and a small user scope.
For SMEs that need real agents for operations, with SSO, audit, internal API and human-in-the-loop.
For businesses that need many agents, many data sources, complex workflows and long-term operations.
Schedule a free consultation for NAD to choose the appropriate use case, evaluate internal data and propose an AI Agent On-premise architecture for SMEs.