AI-assisted document search that helps people find relevant company information, understand the answer and return to the supporting sources—with access boundaries shaped around the organisation.
Policies, internal wikis, technical documents and operational guidance can be spread across systems. People search in several places or interrupt colleagues who know where the information lives.
The impact depends on your business. We establish the baseline before recommending a solution.
Define which documents belong in the experience, who maintains them and how updates are handled.
Help people ask a question and discover relevant passages and source documents.
Design retrieval around document-level authorisation and the organisation’s information boundaries.
Test the intended tasks, support source checking and handle missing or uncertain answers appropriately.
Help authorised users discover relevant information from the agreed document set.
Make approved knowledge easier to find while keeping source checking available.
Evaluate representative questions, retrieval quality and search effort before expanding to more teams or sources.
A colleague searches several locations or asks a specialist where a policy or technical answer is documented.
The assistant retrieves relevant authorised material and supports an answer that the user can check against the source. Missing information should remain explicit.
This describes the intended scoped workflow. Permissions, source references and answer quality must be tested for each implementation; AI answers are not guaranteed correct.
Our experience includes a local-model approach to internal documentation and an approach using a dedicated Microsoft agent. The examples below describe their purpose and access model without exposing confidential documents.
A locally operated AI model was used to help people work with internal documentation. Access was controlled at document level, so the information available through the assistant followed the user’s permissions.
The approach combines a local model with document-level authorisation for internal information retrieval. Model location is one part of the architecture; the complete storage, processing and logging boundaries need to be assessed for each deployment.
A dedicated Microsoft agent was used within an internal architecture to help people work with organisational documents. Document access permissions formed part of the approach, keeping information access aligned with authorised users.
The implementation used a dedicated Microsoft agent and permission-controlled organisational document access. The specific services, identities, licences and integration boundaries for a new project are established during technical discovery.
These descriptions summarise implementation experience without disclosing internal documents or customer identities. Features for a new engagement are scoped and verified individually.
Our separate application in development explores how internal wikis, technical documentation and organisational information can become a more accessible, source-grounded knowledge experience. Planned features are assessed separately from the implementation examples above.
Explore time that could be freed for other work or personal time. Include capture, checking and approval in the assumed time per task. Savings are not guaranteed.
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Adjust the assumptions, then calculate your scenario.
Hours per week = people × tasks per person per week × (current minutes − assumed minutes) ÷ 60. Capacity value = those hours × your hourly assumption. Time freed is not automatically cash saved; implementation, usage and support costs are excluded.
A scoped pilot establishes the document set, permissions, intended users and evaluation criteria. Model choice, hosting, document updates and integrations shape the implementation and ongoing costs.
Retrieval-augmented generation (RAG) brings relevant documents into the answer process. It can improve grounding, but it cannot guarantee that every generated answer is correct. People should be able to check sources and understand limitations.
Discuss a Private AI AssistantRetrieval-augmented generation combines document retrieval with an AI model. It finds relevant information from an approved collection to help the model produce an answer that can be checked against its sources.
A local model can support a controlled deployment, but the complete data path still depends on storage, document processing, integrations, logs and hosting. Those boundaries must be assessed for the actual implementation.
The system should enforce the current user’s access when retrieving documents and producing answers. Access controls need to be tested, including changes to permissions and attempts to retrieve restricted material.
One example uses a dedicated Microsoft agent within an internal architecture. The specific Microsoft services, licences and integrations for a new engagement are confirmed during scoping; no particular product version is implied here.
No. Source links, evaluation, uncertainty handling and appropriate human checking remain important. The level of assurance must match the intended use.
Describe the challenge and what a better outcome would look like. We can work through the technical details together.
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We will respond to your enquiry as soon as reasonably possible.
