The work we cover
A knowledge assistant helps staff find an answer in approved company information and inspect the evidence behind it. We build enterprise search and AI-assisted retrieval around the questions people need to answer, the sources they can use, and the consequences of getting the answer wrong.
The work can cover policies, procedures, product documents, service instructions, approved research, and account records. We define a useful first collection and user group. We examine who owns each source, which version is current, and whether its content is appropriate for the intended users.
Retrieval-augmented generation, or RAG, supplies relevant source material to a model before it drafts an answer. It is one option. A direct search result can be sufficient for a known document, while a live query is more suitable for current stock or order status. We choose the approach for each question instead of treating all business information as a document-search problem.
How we deliver
We collect real questions with their expected sources and user roles. Some have clear answers, some need several records, and some cannot be answered from the available information. These cases establish what the assistant needs to retrieve, when it must ask for clarification, and when it must decline to answer.
We inspect the source collection for duplicate versions, stale policy, conflicting definitions, missing metadata, and access problems. Content owners resolve authority. We build the required ingestion, search, and retrieval path with update and deletion handling. The assistant needs to stop using a withdrawn document as well as discover a new one.
Identity and source permissions apply before information reaches the answer. We test users with different roles and check indirect leakage through summaries, snippets, and conversation history. Text inside a retrieved document cannot instruct the application to disclose another source or change its access rules.
Evaluation separates finding the right evidence from using it correctly. A citation must support the statement it accompanies. We test unsupported answers, conflicting policies, missing documents, ambiguous questions, and misleading content. Staff can inspect a source, report an issue, and continue their task when the assistant cannot help.
An illustrative engagement
This is an illustrative design, not a claim about a delivered client result. A multi-site company has operating procedures in several repositories. New managers ask experienced colleagues which policy applies, and an earlier chatbot often returns a superseded version.
We agree a first collection of approved operating procedures, with a named owner and effective date for each. The assistant retrieves relevant passages and explains which procedure applies to the stated location and date. Conflicting versions return a visible issue for the content owner instead of a confident compromise.
Staff access follows their existing role. A manager who can read local procedures does not gain access to private employee records through the assistant. The release review includes source changes, revoked permissions, missing answers, and the effort required to verify the response during ordinary work.
How we measure value
We compare the time and effort required to reach a correct, usable answer. The baseline includes searching, asking colleagues, opening sources, and verifying the result. The new process includes reading the generated answer, checking citations, and escalating a failed case.
Quality measures include retrieval of the correct source, answer accuracy, citation support, appropriate refusal, and access isolation. We review performance by question type and user role. A high average can hide poor results on an important restricted or complex category.
We also measure source operations: freshness, failed ingestion, deletion delays, and the time owners spend correcting content. Better retrieval cannot make an ambiguous policy authoritative. Where content maintenance becomes the main constraint, the next improvement may be clearer ownership and document review rather than a different model.
Personal information needs explicit handling decisions. The OAIC guidance on commercial AI products highlights oversight and information access considerations. We work with your advisers and IT team on the design appropriate to your sources and users.
What your team owns
The handover includes the source register, ingestion and retrieval design, access rules, question set, evaluation results, and operating instructions. We document how to add a collection, withdraw a document, inspect a citation failure, and release a changed retrieval or answer configuration.
Content owners remain accountable for correctness and currency. The technical owner maintains the service and monitors the feed. Staff feedback enters an improvement queue with enough context to reproduce the issue. We agree continuing support and the evidence needed before expanding to another department or external audience.
Questions before you start
Can we use an assistant we already license? We assess its source connections, identity model, evaluation options, and user fit first. Configuration and content work may meet the need without a separate custom interface.
Do we need to train a model on our documents? Many question-answering tasks can use retrieval or live lookup. Fine-tuning is a separate design choice with different data and maintenance needs. We assess it only where the task provides a reason.
Can this answer customer questions? An external audience needs its own source boundary, identity design, service policy, and escalation route. Our customer service automation service covers that wider workflow; data integration covers the connections behind current business records.