ASE Holdings · Semiconductors
AI integration case
ESG disclosure navigator
Answers only from published reports, citing year, section and entity; drafts customer ESG questionnaires internally
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SERVICE · RAG KNOWLEDGE
Internal knowledge with sources you can check
Find the relevant contract, procedure or support record, then review the evidence behind the answer. Keep document versions and access rules visible.
- Answers point back to evidence
- Cited
- Retrieval follows permissions
- Access-aware
- Quality is tested with your experts
- Evaluated
Brands the team has shipped for over 15 years
Digital products and AI integration cases- Shipping digital products
- 15+ yrs
- Brands served
- 300+
- Projects delivered
- 600+
- Senior team, close to the work
- Taipei
Make the source easy to verify
Retrieval-Augmented Generation (RAG) retrieves relevant material and provides it as context for a model’s answer. An enterprise knowledge system also needs source evidence, access controls, document updates and evaluation. RAG helps make answers inspectable; it does not guarantee correctness.
Useful knowledge is often spread across document systems with different versions and permissions. We define the sources, users and questions that the first knowledge system must support.
RAG retrieves relevant material before an answer is generated. We add source evidence, test permissions and evaluate retrieval and answer quality with your experts. Missing, conflicting and stale information need explicit handling.
Capabilities
- Source passages
- Link answers to the supporting document and passage so users can check them.
- Permission checks
- Apply agreed access rules during retrieval and test that restricted material stays restricted.
- Freshness and evaluation
- Define source synchronization, superseded-version handling and a representative set of test questions.
- Deployment and parsing
- Assess document formats, hosting options and model-provider data handling against the project’s requirements.
Use cases
- Contract and policy search
- Find the relevant clause or policy version and prepare a cited summary for professional review.
- Equipment manuals and procedures
- Retrieve the applicable work instruction, maintenance guidance or fault reference with its version.
- Support knowledge
- Prepare sourced answers from current product documentation, policies and approved ticket history.
Delivery cadence
01
01 · SCOPE
Set the sources and baseline
Inventory formats, versions and access rules, then agree on test questions with domain experts.
02
02 · BUILD
Index and retrieve
Build parsing, synchronization, permission checks and answers with source evidence.
03
03 · EVALUATE
Test real questions
Inspect retrieval coverage, citation accuracy and unsupported answers against the baseline.
04
04 · OPERATE
Keep knowledge current
Confirm source updates, monitoring, evaluation ownership and the operating runbook.
FAQ
Does RAG prevent hallucination?
No. It gives the model relevant sources and makes supporting evidence easier to inspect. Retrieval can miss information and answers can still be wrong, so evaluation and defined failure handling remain necessary.
How are document updates handled?
Agree on synchronization and version rules for each source. Test how updated, deleted and superseded documents affect retrieval, with freshness monitoring where required.
Can we keep access permissions intact?
Permission-aware retrieval is an explicit requirement, not a property of RAG by itself. We map the access rules and test permitted and denied queries before launch.
Can it run in our own environment?
We assess VPC, on-premises and designated-cloud options. Hosting, model choice, data transfers and retention are documented against your requirements.
SCOPE · CHOOSING THE APPROACH
Where do RAG, search and model answers fit?
Search finds material to read. A model generates an answer. RAG combines retrieval with generation; the right choice depends on whether the workflow needs documents, an answer or both.
Search or model response
RAG implementation
Output
Search returns material; a model can draft text
Retrieve material and generate an answer from the supplied context
Evidence
Depends on the product and configuration
Design citations and test whether they support the answer
Permissions
Inspect the source and product access controls
Enforce and test access at retrieval and answer time
Quality
Evaluate the task and failure modes
Evaluate retrieval coverage, citations and answer correctness
KNOWLEDGE BASE · FURTHER READING
The RAG knowledge system knowledge base
Selected guides and external perspectives for planning this workflow.
Tenten AI whitepapers & perspectives
- RAG at Enterprise ScaleEvaluate chunking, retrieval, citations and operating tradeoffs on your own workload
- Fine-Tuning vs RAGA Decision Framework for Enterprise AI: When Each Approach Earns Its Cost
- Enterprise Knowledge StructuringPreparing Documents, Versions, and Access Rules for Retrieval
- All whitepaper resourcesGuides for models, architecture and operations.
TENTEN × DIFY
AI connected to existing systems, with clear working boundaries
From Tenten’s October 2026 consulting deck: five client platforms, their AI integration workflows, data sources and human review.
CEC · Civil & building engineering
AI integration case
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Parse tender requirements → match archive and internal register → list projects with evidence → draft credentials section
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Parent care assistant
Model comparison, setup and Wi-Fi help, warranty and returns; verified order lookup; handoff with summary
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