Internal knowledge assistants
Answers across policies, procedures, and technical documentation for staff, available in Teams or a web app.
Solutions · AI & Automation
Generative AI Apps & Copilots
Your policies, contracts, manuals, and tickets hold the answers your staff and customers keep asking for. Symetriz builds assistants on Azure OpenAI that find those answers, cite where they came from, and only show people what they are allowed to see.
The problem
The answer exists. Nobody can find it.
Knowledge buried in documents
Staff spend hours searching file shares and wikis, or ask the one colleague who knows.
How we fix it: An assistant that searches and answers across your content.
Public chatbots can't be trusted with it
Generic tools don't know your business, and pasting company data into them is a risk.
How we fix it: A private assistant in your tenant, on your data.
Answers nobody can check
A confident wrong answer does more harm than no answer at all.
How we fix it: Cited sources and tested answer quality.
The solution
Assistants that answer from what you already know
- Answers grounded in your own documents and data, with sources cited
- Built in your Azure tenant, with your data kept out of model training
- Permissions respected, content filters on, and quality measured before launch
What we build
Each one starts from a specific audience and question set, not from a generic chat window.
Customer-facing assistants
Website and support chat that answers from approved content and hands over to a person when it should.
Document processing & drafting
Extracting fields from contracts and forms, summarising long documents, and drafting first versions for review.
How we make it trustworthy
The model is the easy part. Grounding, access control, and testing decide whether people rely on it.
Retrieval over your data
Azure AI Search indexes your content so answers come from your documents, with the source shown alongside each answer.
Security & access
Private networking, Entra ID sign-in, and document-level permissions, so the assistant never reveals what the user couldn't open.
Evaluation & fine-tuning
Answer quality measured against a test set before launch and monitored after. Where retrieval isn't enough, models are fine-tuned on your domain.
Ready when you are
Which questions does your team answer over and over?
Bring one set of documents and one audience. We will show you what an assistant built on them can do, and how well it does it.