More time for useful work
Reduce the friction of locating relevant information, with the potential to return time to the tasks people were trying to complete.
AI Knowledge Assistants & RAG
Your company has the knowledge.
Your company has the knowledge.
Make it easier to find.
Bring approved documents, internal wikis and operational knowledge into a clearer question-and-answer experience. Help people find useful information and return to its source.
Family-run. Senior-engineer-led. Scotland-based, UK-wide.
- Natural-language information search
- Supporting sources within reach
- Access shaped around permissions
The answer exists. Finding it takes time.
Less searching.
Less searching.
More getting on with the work.
The process is in a policy. The detail is in a manual. The answer might be in a wiki—or with the colleague everyone asks. When knowledge is scattered, even a simple question can interrupt the working day.
Where the cost builds up
- Time disappears into the search.People move between folders and systems to find the relevant passage.
- Experienced colleagues become the search engine.The same questions interrupt people who already know where information lives.
- Onboarding depends on who is available.New team members need a useful route to the organisation’s approved knowledge.
From a question to its supporting source
An information experience built around your organisation.
GoldcrestCode develops knowledge assistants using retrieval-augmented generation (RAG) and related technologies. Relevant source material is retrieved to support an answer, rather than relying on a model’s general knowledge alone.
Explore our implementation experience- Ask in everyday language.Help people locate information without knowing the right document title.
- Keep the supporting material close.Bring relevant passages and source references into the answer experience.
- Build around approved access.Define document permissions, identities and information boundaries as part of the architecture.
- Give uncertainty a useful next step.Design for missing, conflicting or insufficient sources, with a route to the document owner or a human reviewer.
Better use of the knowledge you already have
Fewer interruptions.
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Fewer interruptions.
More information within reach.
More confident onboarding
Give new colleagues a clearer route to approved procedures, manuals and company knowledge.
A source to check
Make supporting material easier to find, so people can review context and resolve uncertainty before acting.
Before
Search several folders, open likely documents, then ask a colleague for help.
With a scoped knowledge assistant
Ask a question, review the relevant answer and follow its supporting source within your permitted information.
The outcome depends on source quality, access design and how people use the tool. The estimator below lets you explore your own assumptions.
Knowledge-assistant engineering experience
Private knowledge.
Private knowledge.
Different delivery approaches.
Our experience includes local-model document retrieval and a dedicated Microsoft agent within an internal architecture. Both approaches included permission-controlled access to organisational information.
Local model. Document-level permissions.
A locally operated model was used to help people work with internal documentation. Document-level permissions controlled which information was available through the assistant.
Architecture considerations
Local inference can form part of a controlled information architecture. Storage, retrieval, logging and access enforcement still need to be designed together for the specific organisation.
Microsoft agent. Internal information access.
A dedicated Microsoft agent was used within an internal architecture to help people work with organisational documents, with access permissions forming part of the solution.
Integration considerations
The appropriate Microsoft services, identities, licences and integration boundaries depend on the organisation’s environment and are established during discovery.
GoldcrestCode is a family-run, senior-engineer-led software house, based in Scotland and working across the UK. We bring software architecture, integration and maintainability into the same conversation as the AI experience. You work directly with experienced engineers, with scope, responsibilities and support agreed before delivery. Meet GoldcrestCode.
Your team. Your assumptions.
How much time does your team spend looking for information?
See the current search effort, then explore the time a different experience could return. Include checking the answer and source in your assumed future search time.
No email needed. Values stay in the page unless you choose to include them in an enquiry. This is a scenario, not a promise of savings.
Adjust the assumptions to explore a search-time scenario.
How this is calculated
Weekly search hours = employees × daily searches per person × working days × minutes per search ÷ 60. Potential time returned is the difference between current and assumed future hours. If you enter a labour cost, the equivalent is time returned × hourly cost. It is a value of time capacity, not guaranteed cash savings, and excludes implementation, usage and support costs.
Start with the information challenge
A useful solution.
A useful solution.
An agreed scope.
Knowledge assistants are individually quoted. Source systems, permissions, model choice, integration, evaluation and support determine the work—not a generic price per chatbot.
Begin with one information problem or a wider organisational requirement. We agree what the assistant should help with and how the result will be reviewed.
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Understand the question
Identify where searching is difficult and what a useful answer looks like.
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Map sources and access
Agree the documents, permissions, integration and scope.
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Build and integrate
Implement retrieval and the answer experience, with appropriate evaluation.
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Support as agreed
Set responsibilities for content, access changes, monitoring and ongoing support.
Useful answers
Knowledge, with context.
What does RAG mean in everyday terms?
The assistant finds relevant information in approved sources and uses that material to support its answer. It can then point the user back to the document, rather than simply answering from general model knowledge.
How are documents and permissions handled?
Source access, user identity, document permissions, storage and logging are considered together. The assistant should only retrieve information authorised for the user. We agree the approach for the systems involved rather than assuming a single access model.
Can it fit our existing tools?
We can assess integrations with your documentation and business environment. Feasibility, licences, access methods and ongoing responsibilities are established during scoping.
What happens when there is no reliable answer?
The experience should make insufficient or conflicting evidence clear and offer a useful next step, such as opening the source or contacting its owner. AI output still needs checking before an important decision.
Does a local model keep everything on our hardware?
Local inference is one architectural option. The full information boundary also depends on document storage, retrieval, logging and integrations. We agree those details before making a deployment-specific data-handling commitment.
Start a Conversation
Make more of what
Make more of what
your organisation knows.
Tell us where information is difficult to find and what a useful answer would look like.
Or email info@goldcrestcode.com.
Thank you.
We will respond to your enquiry as soon as reasonably possible.