Make company knowledge accessible and bring AI into everyday work.
Typical challenges
Sounds familiar?
Knowledge is spread across SharePoint, file shares and e-mails
Public AI tools are not allowed for company data
Teams are unsure how to use AI productively and safely
What we deliver
Enable natural-language access to business data and company knowledge
Explain existing code and data flows, and create supporting documentation
Connect AI agents to business systems to perform defined tasks with agreed controls
Support application development, code review and testing with AI
Train teams to apply AI through practical workshops and guided examples
Chatbots that answer frequently asked questions around the clock
How it works
1. Use-case & data-protection check
Workshop to select use cases, define permitted data and agree success measures.
2. Architecture in your environment
Azure OpenAI in your Azure tenant; retrieval-augmented generation (RAG) over approved sources; access rights follow your Microsoft 365 permissions.
3. Answers with sources
Every answer cites the documents it is based on, so users can verify it.
4. Agents with guardrails
AI agents get defined tools and tasks; critical actions require human approval; all actions are logged.
5. AI-assisted engineering
GitHub Copilot and Databricks Genie support code analysis, documentation and development; engineers review and release.
6. Evaluation & enablement
Answer quality and time saved are measured; teams learn in hands-on workshops.
Technologies & business value
Technologies: Azure OpenAI, Microsoft 365, GitHub Copilot, Databricks Genie AI, Python, FastAPI, LLM APIs & RAG pipelines, Model Context Protocol (MCP)
Business value: Faster access to knowledge and less manual effort in daily work.
Related success stories:
Frequently asked questions
Does our data leave our environment?
We design solutions so that data is processed in the environment agreed with you, for example your own Azure tenant. Tools, data and tasks are agreed before AI is used.
How do you measure the benefit?
Against success measures agreed upfront, such as search time, processing time or error rate, together with quality and usability.