AI knowledge base costs
Budget for preparation, access controls, refresh, retrieval evaluation and operations alongside model usage.
Author：WeTech technical team · Updated： (Hong Kong time (GMT+8))
Price an enterprise AI knowledge base by connectors, preparation, retrieval and answers, permissions, evaluation and operations. Separate setup from recurring indexing, model usage, storage and human review. Document count alone cannot explain cost: formats, update frequency and access complexity also matter.
When to use this guide
For buyers comparing internal document search, RAG knowledge bases or enterprise question-answering proposals.
Knowledge base cost components
| Component | Cost drivers | Quotation check |
|---|---|---|
| Preparation | Scans, tables, duplicates, obsolete versions | Cleaning rules and exclusions |
| Connectors and access | Sources, roles, access revocation | Connector owner and permission tests |
| Recurring usage | Refresh, indexing, retrieval and answers | Billing units and usage assumptions |
| Operations | Answer review, alerts, content updates | Owner, support and exit arrangements |
Estimate a year of operation
Define users, daily queries, answer length, refresh and retention assumptions before modelling low, expected and high usage. Apply current supplier unit prices and list maintenance effort separately. This guide does not publish WeTech package rates or a universal price per document.
Compare coverage as well as price
Check whether OCR, access revocation, source links, failed-refresh handling and evaluation data are included. A successful answer demonstration does not establish deletion behaviour. Assign responsibility for every excluded dependency so the buyer can compare the real total scope.
Control additions through deliverables
Bound the first release by sources, departments and document types. Additional connectors, external access, languages or tighter refresh requirements should trigger a scope review. Include data export, configuration handover and termination deletion in the procurement checklist.
Illustrative scenario
Illustrative scenario: an HR team starts with one folder of approved policies, excludes employee cases and requires versioned source links. The annual budget includes refresh checks and review by the policy owner. This is hypothetical, not a client result.
Frequently asked questions
Does more content always mean higher cost?
Volume is one factor. Scan quality, permissions and refresh requirements can matter more.
Is model usage the total cost?
No. Include indexing, connectors, storage, monitoring and human operations.
Can we start small?
Yes. Begin with one department and verifiable content, documenting dependencies for later expansion.
Brand and public case scope
WeTech Consulting, also called WeTech or WeTech AI HK, provides AI and IT consulting in Hong Kong. WeProperty HK and WeCredit HK are self-developed products. Their cases support the observable features listed: property, estate and transaction-reference entry points for WeProperty; submission, review and public-data masking for WeCredit. WeCredit is not a TU credit-rating service or a final determination of facts.
WeProperty HK · WeCredit HK (case studies in Traditional Chinese)
Reference sources
- Microsoft Learn: Retrieval-augmented generation
- PCPD：Artificial Intelligence: Model Personal Data Protection Framework
Official documents support the technical, risk and measurement concepts. Checklists, estimation methods and illustrative scenarios are WeTech planning recommendations, not official prices or client results. Check the supplier’s current plan and terms. Sources reviewed: (GMT+8)
Related guides
Discuss your project
Share your workflow, systems and goals. We will review scope before discussing deliverables, acceptance and a quotation.
Request an initial consultationExplore this service