People search across too many places
Policies, manuals, case records and product information may sit in shared drives, business systems and separate databases.
Enterprise RAG & AI Knowledge Systems
Connect approved documents, databases and internal systems to AI assistants that retrieve relevant evidence, cite their sources and respect existing access permissions.
RAG can improve source grounding, but it does not guarantee that every answer is correct. We define citation, refusal, evaluation and human-review controls according to the workflow.
Illustrative evidence flow
Only authorised evidence should enter retrieval and the model context for this request.
The request carries an authenticated identity and role.
Source and tenant filters are applied before retrieval.
Semantic and lexical search find and refine relevant passages.
The assistant answers from retrieved evidence and shows its sources.
The current procedure requires approval before release. [1] Exceptions follow the escalation path. [2]
Controls across the full flow
Response policy
Answer · Decline · Escalate
Company knowledge is usually spread across files and systems with different owners, update cycles and access rules. A useful knowledge assistant has to work with those operational realities.
Policies, manuals, case records and product information may sit in shared drives, business systems and separate databases.
An answer can look convincing while relying on an old file unless deletion, replacement and re-indexing are part of the design.
A general search index is not enough when teams, customers or tenants are allowed to see different information.
Without representative questions and expected evidence, teams cannot tell whether retrieval improved or simply changed.
Start with a workflow where the source material, users, permissions and review owner can be defined. These are common starting points, not pre-built products.
Help staff find the current instruction and open the cited source before acting.
Retrieve approved troubleshooting, service and product information while retaining escalation paths.
Answer detailed questions from maintained catalogues, specifications and approved commercial material.
Guide staff through role-relevant procedures without exposing unrelated internal content.
Bring together permitted documents, database records and API results for a specific operational question.
Evaluation covers the evidence selected as well as the final wording. We agree the test questions, failure policy and review owner before a pilot is treated as production-ready.
Check whether retrieved passages support the answer and whether citations point to the right section.
Test routine, ambiguous, outdated and adversarial questions drawn from the intended workflow.
Require the assistant to decline or escalate when approved sources do not support an answer.
Confirm that users cannot retrieve or infer content outside their role, organisation or record scope.
Test updates, deletions, replacements and re-indexing so superseded content is removed from use.
Monitor retrieval quality, response time, model usage and cost within the agreed service boundary.
Illustrative access scenario
Identity, source ownership and query-time filters work together. The model should receive only the evidence this user is permitted to retrieve for this request.
Operations manager · authenticated staff role
Current operating procedures and approved product manuals
HR files and customer records outside the assigned scope
An answer based on allowed passages, with citations and an audit record
Architecture detail
The exact components depend on document quality, update frequency, language, scale, latency and deployment requirements. The following patterns can be combined where they fit the use case.
Automated ETL can parse PDF, DOCX, JSON and API payloads, apply layout-aware OCR, extract metadata and use recursive or sliding-window chunking. Each source keeps ownership, version and permission metadata.
Dense vector retrieval can use HNSW or Flat indexing with cosine or inner-product distance. BM25 adds lexical matching, with Reciprocal Rank Fusion combining the candidate lists.
A secondary model, such as Cohere Rerank or BGE-Reranker, can reorder the top-k candidates before prompt assembly. This can reduce irrelevant context and unnecessary token use.
Enterprise identity can be connected through OAuth2, SAML or Active Directory. Query-time metadata filters apply tenant, role and document rules before retrieved text is added to the model context.
Versioned test sets, retrieval traces, citation checks, audit logs, source refresh jobs and usage monitoring make changes visible and reviewable after release.
OpenAI, Claude, DeepSeek and Qwen can be assessed as generation or reasoning options. The decision is based on the selected deployment, provider terms and measured performance on the agreed question set.
Tooling, model capability and enterprise controls
Long-context and document-heavy workflows
Cost and deployment options to assess
Chinese-language and deployment options to assess
Selection criteria
A pilot should test one useful workflow with real permissions and representative questions. Broader access comes after the evidence, failure handling and operating owner are understood.
Map users, questions, systems, data owners, sensitivity and update paths.
Ingest an approved source set and connect one practical user journey.
Measure evidence relevance, citations, refusals, access rules and response quality.
Connect identity, applications, APIs, review queues, logs and support processes.
Review source changes, failed questions, usage, latency and cost before expanding scope.
RAG changes how evidence is found and presented. It does not remove responsibility for source quality, permissions or decisions made from an answer.
FAQ
Retrieval-augmented generation, or RAG, retrieves relevant information from approved sources and supplies it to an AI model as context for an answer. It can make the answer easier to trace, but it does not guarantee correctness.
The agreed scope can include PDF and DOCX files, structured data, databases, APIs, and existing knowledge repositories. Each source needs suitable parsing, metadata, update, deletion, and access-control handling.
Yes, when identity, source permissions, metadata, and index boundaries are mapped and tested correctly. The exact approach depends on the organisation's identity provider, repositories, tenant model, and deployment environment.
No. Source quality, parsing, retrieval settings, permission data, and model behaviour can all affect an answer. We define citation, refusal, evaluation, logging, and human-review controls around agreed scenarios.
A system can be designed for providers such as OpenAI, Claude, DeepSeek, or Qwen. Model selection and availability are confirmed against language, data handling, deployment, latency, cost, and integration requirements.
Bring a small set of representative questions, the source systems involved and the users who should have access. We can clarify the data boundary, pilot scope and evidence needed to judge the result.
The first step is a practical scope discussion, not a promise that AI can replace the workflow owner.
