Your company knowledge may live across policy documents, product guides and internal notes. EidenTech can scope a retrieval-augmented generation assistant that finds relevant material and prepares an answer grounded in that material. The project includes content ownership, document ingestion, permissions and evaluation, because a useful knowledge assistant depends on the quality and availability of its sources as well as its model.
How we approach your project
Select trustworthy material and its owners
We inventory the information the assistant is allowed to use and identify the person responsible for each source. Public product guidance and private staff policies need different access rules. Duplicates, obsolete versions and contradictory instructions should be resolved or clearly labelled before ingestion. A support assistant, for example, needs to distinguish the current returns policy from an archived draft. The scope defines supported file types, the update process and what happens when a document is removed. Content quality is an explicit workstream rather than an assumed result of uploading files.
Design retrieval around real questions
Relevant information has to be found before an answer can use it. We test representative questions against document structure, useful metadata and the retrieval approach. A product code may need an exact match, while a process question may need context from several related sections. The design can combine appropriate search methods and preserve source locations. We evaluate whether the retrieved passages actually support the requested answer and examine questions for which no suitable source exists. This separates a retrieval failure from a response-generation problem, making improvements more targeted.
Carry access rules into the answer workflow
A knowledge assistant should not expose an internal source merely because its text resembles a public question. Where authentication is required, the backend filters available sources according to the user’s authority and retains that boundary through retrieval and response preparation. We consider permissions when content is indexed, updated and removed. Instructions inside retrieved material remain content to examine, not authority to change the assistant’s tools. Source references help a reader inspect important information, while missing or conflicting evidence leads to clarification or a documented handoff rather than an invented answer.
Maintain the assistant as the knowledge changes
Evaluation includes questions with known source answers, ambiguous wording, unavailable information and restricted content. We review source support as well as readability. Updates may need reprocessing and a check of affected questions, so the operating plan includes freshness expectations and a content publishing owner. Usage signals can reveal repeated unanswered questions without exporting private document text to general analytics. A focused release may cover one document collection before expanding to other teams. EidenTech can connect the assistant to a website or internal application after reviewing the required user access and source systems.
What we can deliver
We agree the requirements and responsibilities before development begins. Your project scope can include:
- An approved source inventory with ownership, version handling and content-update responsibilities.
- Document ingestion, indexing and retrieval appropriate to the agreed question set.
- Server-enforced source access and answer references where supported by the workflow.
- A representative evaluation set, publishing process and operating handover.
Technology choices for your requirements
The stack follows your integrations, operating model, and maintenance needs. These are options to review during discovery, rather than a fixed stack promised for every project.
- Supported document parsing and source metadata
- Search or vector retrieval selected after source review
- Server-side generation with relevant source context
- Authenticated application APIs and permission-aware retrieval
Planning questions, answered
Is RAG the same as training a model on our documents?
A retrieval-based design finds relevant source material when preparing an answer. Model training changes a model through a separate process. We review your task and source collection to determine whether retrieval, training or a simpler search experience fits the requirement.
Can the assistant use private company documents?
Yes, with an access design suited to the document owners, users and provider arrangement. We define who may see each source and enforce important boundaries on the server. Retention, deployment and data-use requirements must be reviewed before using private material.
How are new or corrected documents added?
The project defines a supported ingestion and publishing process. That may be a reviewed upload or a connection to an approved source system. We agree freshness expectations, obsolete-version handling and which evaluation cases should be rerun after a material update.
Will source references make every answer accurate?
References help readers inspect evidence, but they do not establish that the answer interpreted it correctly. We evaluate retrieval relevance and response support separately. Missing evidence, conflicting sources and consequential questions need an appropriate clarification or review route.
Tell us what needs to work better.
Share your idea, existing workflow, and the outcome you need. We will help shape a practical starting point.
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