Private AI Deployment Example for a Manufacturer Using Qwen
* Actual cost, timeline, and scope vary by factors such as number of users, existing data conditions, and external system integrations.
This project began when a precision-parts manufacturer asked for a more efficient way to search across quality-control procedures, machining-condition sheets, and equipment-inspection standards. Documents were stored on paper and in shared folders, and finding the right information often depended on knowing which document to open. Whenever field teams asked questions such as, “Which document contains the machining conditions for this material?” or “What should we check first when an abnormality occurs?”, quality-control staff had to find and explain the original source material.
At the same time, the company had internal-policy concerns about sending customer information, manufacturing know-how, and quality documents to external generative-AI services. They wanted a system that could search their documents and provide evidence-based answers inside infrastructure they control, without relying on a cloud AI API.
Proposal and Estimation Highlights
We proposed deploying a quantized Qwen3-8B-family model on a GPU workstation owned by the client, then building a local inference server and internal document-search system using Retrieval-Augmented Generation (RAG). Through a browser-based chat interface available on the internal LAN, staff can ask questions and receive answers that cite the document name, revision, and page used as evidence.
During normal operation, questions, answers, document text, and retrieval embeddings are not sent to an external AI API. When models or dependencies need to be obtained or updated, the operating approach requires administrator approval, temporary firewall permission, and a record of what was retrieved. Rather than broadly claiming “no communication of any kind,” this is a practical design that keeps everyday question answering local while managing the limited communications needed for controlled updates.
To make a four-week delivery feasible, the initial scope assumes one GPU workstation, up to 30 text-extractable PDF or Word files totaling approximately 500 pages, and up to 25 user accounts. On the initial single-GPU configuration, two or three concurrent generations are the working target. It does not include 25 simultaneous users, a response-time SLA, 24/7 monitoring or redundancy, or automatic synchronization with an existing document-management system.
Technical Configuration
| Item | Technology and Rationale |
|---|---|
| LLM Inference Platform | A version-fixed, quantized Qwen3-8B-family model is deployed after confirming compatibility with the client’s hardware. vLLM provides the local inference server and an OpenAI-compatible API for internal systems; this does not connect to the external OpenAI API. |
| Document Search (RAG) | Text is extracted from PDF and Word documents. A locally run embedding model and vector search retrieve relevant passages before the LLM generates an answer. This uses registered documents as a source rather than fine-tuning the model. |
| Internal Chat UI | A browser interface available only from the internal LAN enables staff to view the question, answer, and supporting document name, revision, and page. |
| Network and Authentication | The inference server is not exposed to the public internet. Basic authentication is configured and unnecessary outbound communications are blocked during normal operation. |
| Quality Review | Acceptance testing covers 20–30 representative questions and checks not only the answer but also whether the displayed evidence is consistent with the source document. |
What Is Outside the Initial Scope
OCR for scanned PDFs, high-accuracy interpretation of drawings, text inside images, and complex tables; recovery of password-protected files; document-level fine-grained access control; Active Directory or SSO integration; automatic synchronization with an existing document-management system; model fine-tuning; external web search; mobile applications; and on-site work are outside the initial scope.
AI answers are also not a replacement for formal procedures, quality decisions, or approval records. The system is configured to show supporting documents and to indicate when a topic cannot be confirmed in the registered documents. Final judgment remains with the original source material and responsible personnel.
Deliverables and Feature Scope
The deliverables consist of the Qwen local inference environment on the client-owned GPU workstation, the internal document-search chat, a deployment and operations manual, and a configuration and test-results list. The initial ingestion scope is up to 30 text-extractable PDF or Word files totaling approximately 500 pages.
Main Actions by User
| User | Main Actions | Practical Use Case |
|---|---|---|
| General Users | Ask document-related questions and review evidence | A user enters a question such as, “What precautions should we check when machining SUS304?” and reviews the answer together with the document name, revision, and page. |
| Administrators | Initial document ingestion and updates, user-account management, and basic operational checks | An administrator registers a revised procedure document and verifies the result and cited evidence using representative questions. |
Main Data Managed by the System
| Category | Main Fields |
|---|---|
| Registered Documents | Document file, document name, revision, update date, and page information |
| Retrieval Data | Extracted document text, segmented search data, and locally generated embeddings |
| Configuration Data | User accounts, model and software versions, retrieval settings, and operational check items |
| Test Results | Representative question, answer, cited source, review result, and items requiring adjustment |
Main Implemented Functions
- Qwen-powered Internal Chat: A feature that generates answers locally when staff ask questions through a browser on the internal LAN.
- Document Search and Evidence Display: A feature that retrieves relevant sections of registered documents and displays the document name, revision, and page with each answer.
- Initial Ingestion and Administrator Update Procedure: A procedure for registering source documents as retrieval data and updating revised documents.
- Basic Authentication and Access-Boundary Configuration: A configuration based on internal-LAN use, with basic authentication and network controls.
Timeline and Delivery Process
This project was completed in approximately four weeks on the assumption that administrator access to the target GPU workstation, hardware specifications, the internal-network policy, the initial source documents, and representative questions were available at the start. Confirming hardware and network compatibility and fixing the initial document scope and acceptance criteria early reduced late-stage rework.
| Phase | Timing | Work Completed and Client Participation |
|---|---|---|
| Step 1: Hardware, Network, and Source-Document Review | First half of Week 1 | We reviewed GPU, memory, free SSD capacity, OS, and internal-network conditions. The client shared source documents, revision information, 20–30 representative questions, and an administrator account. |
| Step 2: Qwen Inference Platform and Startup Test | Second half of Week 1 | We deployed the quantized Qwen3-8B-family model and vLLM, then tested GPU startup, context length, and limited concurrent use. |
| Step 3: Local Document Search and Chat UI | Weeks 2–3 | We implemented text extraction, segmentation, indexing, local retrieval, the evidence-display chat UI, and basic authentication. Retrieval performance was adjusted using representative documents and questions. |
| Step 4: Acceptance Testing and Operations Procedure | First half of Week 4 | We reviewed answers, source documents, page display, the document-update procedure, and the approach to outbound-communication control using representative questions. |
| Step 5: Limited Launch, Delivery, and Payment | Second half of Week 4 | Internal use began, and we delivered the operations manual, configuration list, and test-results list. The full amount was invoiced after acceptance. |
Payment Terms
As a general rule, we do not require an upfront payment. There was no advance payment at the time of contract; the full amount was invoiced only after all development work and the client’s acceptance were complete.
Cost Breakdown
The following costs cover a Qwen-family local inference platform, initial document search, the internal chat UI, acceptance testing, and implementation support. They do not include the purchase of a GPU workstation, OS licenses, commercial software, certificates, hardware upgrades, on-site work, ongoing maintenance, or external AI API charges.
| Item | Cost | Scope |
|---|---|---|
| Requirements Alignment & Basic Design | $400 | Confirmation of operating conditions, source documents, network boundary, acceptance criteria, and configuration |
| Inference Platform Setup | $800 | Quantized Qwen model, vLLM, GPU startup testing, and local API configuration |
| RAG Setup and Initial Data Ingestion | $950 | Local extraction, embeddings, retrieval index, and initial ingestion of up to 30 source files |
| Chat UI and Basic Security Configuration | $800 | Internal-LAN interface, basic authentication, evidence display, and review of outbound-communication controls |
| Testing and Tuning | $700 | Answer and evidence review using 20–30 representative questions, plus initial configuration adjustment |
| Documentation and Handover | $550 | Operations and re-ingestion procedure, configuration and version list, test-results list, and administrator briefing |
| Total | $4,200 | Initial-scope delivery |
Currency and Invoicing
All amounts on this page are approximate U.S. dollar (USD) figures, converted from the original Japanese yen (JPY) estimate at an assumed exchange rate of around JPY 150 per USD and rounded for readability. Quotations and invoices can be issued in USD, JPY, or other currencies upon request; the applicable exchange rate, conversion basis, and any bank or payment-provider fees will be confirmed in advance for each project. For clients outside Japan, payments are generally handled as transactions exempt from Japanese consumption tax.
Supplement: Estimated Costs After Launch
The standard configuration does not use a pay-per-use external AI API or cloud LLM. When the client’s existing network and GPU workstation are used, third-party cloud-service fees generally do not apply. Electricity, hardware maintenance or replacement, optional backup or monitoring services, and support where needed remain separate considerations.
| Category | Estimated Monthly Cost | Nature of Cost and Main Inclusions |
|---|---|---|
| External AI API and Cloud LLM Charges | $0 | Not used in the standard configuration. The intended operating model does not send questions, answers, or document data to an external AI API. |
| Optional Backup and Monitoring Services at Cost | $0–$20 | Third-party charges if encrypted backup or monitoring notifications are added beyond the existing internal backup process. |
| Minimum Maintenance and Operational Support | $100–$170 | Review of OS, dependency, and model-update policies; basic operational checks; and minor configuration consultation. Major rebuilds or bulk document additions are quoted separately. |
| Estimated Monthly Total | $100–$190 | An estimated support range based on the above. Electricity, hardware maintenance, and replacement costs in the client environment are additional. |
Staff Comment
For a private AI and local LLM deployment, it is important to decide not only that cloud AI will not be used, but also which documents are in scope, who will use the system, which environment will host it, and what evidence answers must show. In this case, a quantized Qwen3-8B-family model was run on the client’s GPU workstation, while the initial scope was limited to internal document-search support. By using RAG to reference registered documents rather than fine-tuning the model itself, we defined a scope that could be delivered in approximately four weeks for $4,200.
Even in a configuration that does not send business data to an external AI API, it is important not to treat AI output as a formal procedure or quality decision. Showing the supporting document, revision, and page with each answer—and indicating when a subject cannot be confirmed in the documents—reduces search time while keeping final confirmation with the original material and responsible staff.
A private AI and local LLM deployment using Qwen-family models can sometimes use PCs or servers the client already owns. Learn more about our Private AI & Local LLMs service. The model and configuration are selected individually based on CPU, GPU, memory, document condition, network policy, and required response speed.
Typical Scope for Common Add-Ons
Once use is established, OCR processing, document preparation, and accuracy validation may be added to include scanned PDFs, drawings, or text inside images. Depending on the state of the source material, this typically requires an additional $2,000–$4,000 and approximately 4–6 weeks.
Active Directory or SSO integration, document-level viewing permissions, and detailed audit logs require review of the existing authentication and document-management environment. A typical estimate is an additional $2,700–$5,300 and approximately 4–8 weeks.
Starting with a focused set of text-extractable, high-value documents and evidence-based internal search balances confidentiality with practical use. The environment can be expanded in stages into OCR, permission integration, document synchronization, larger models, or multi-GPU configurations based on actual usage.
How It Started