SharePoint and Microsoft 365
Document libraries, project folders and approved records. We also check group permissions, versions and updates.
AImation / AI operating system
An AI operating system for your business. One shared data foundation.
The specification is in SharePoint, the test report on a file server and the project status in a database. We connect the sources you need and prepare their content for chatbots, agents, dashboards and custom applications.
Your sources
SharePointFile serversDatabases
Applications on top
What is an AI operating system?
We use the term AI operating system for a shared data and application layer within a business. It connects selected repositories and systems, organises their content and makes approved knowledge available to several applications. Windows, your servers and your business systems remain part of the existing IT environment.
A chatbot is one possible interface. An agent can also call tools and prepare workflow steps. A dashboard displays structured metrics. Each application needs to use traceable data.
A new interface cannot repair an outdated drawing. Data maintenance and ownership are part of the project.
Start with the repositories you already use.
We begin with sources required for a specific workflow. Access may use a connector, an API or an agreed import. Whether data is copied, indexed or queried directly depends on the source and platform.
File servers and network drives
Drawing repositories, test reports and accumulated folders. File formats, scan quality and access permissions determine the preparation effort.
Databases and business systems
Project, part or change data from existing applications. Interfaces, the data model and read or write access are agreed individually.
Turn scattered files into connected knowledge.
Data mapping defines which fields belong together: project ID, part number, revision and approval status, for example. A test report can then be linked to the correct part and document version. The team reviews conflicting or missing information.
Example using fictional data
- Source
- SharePoint: specification
- Mapping
- Project ID field
- Shared reference
- Project P-104
- Source
- File server: test report
- Mapping
- Part number and revision
- Shared reference
- Part B-217 · Rev. C
- Source
- Database: change order
- Mapping
- Project and part reference
- Shared reference
- P-104 ↔ B-217
Permissions, provenance and freshness travel with the data. Updates, deletions and revoked permissions must also reach the search index, caches and applications.
Several applications use the same foundation.
These workflows illustrate possible extensions. Each application is scoped to your sources, interfaces and review steps.
Chatbots with company knowledge
Answer questions about requirements, lessons learned or test reports. Include the source passage and document version so the team can check the answer.
AI agents and agentic systems
Classify a technical request, find sources and prepare a response. Specialised agents can hand off subtasks to each other. Tool access and approvals are defined in advance.
Dashboards and analysis
Display project status, open changes or test results from structured data. Metrics follow defined calculation rules; AI can explain deviations.
Custom apps and existing systems
Connect a change-management application or an interface to project management. Write actions have separate permissions and human approval where required.
A question from the design team.
“Why was part B-217 changed in revision C?”
Search links the change order to the test report and approved minutes. The assistant drafts an answer with evidence. A dashboard shows the associated change status. An agent can prepare a further review step if that is part of the agreed workflow.
Illustrative target workflow, not a customer reference. Missing evidence or permissions must be surfaced as a gap.
The platform must fit the implementation.
AImation works with your team to define the use case, data mapping and technical review criteria. We select a platform to fit your sources, operating model and intended application.
Platform capabilities are based on vendor documentation. We test the specific configuration against your data; a product description does not replace acceptance testing.
Existing technology partnership
U-KNOW.AI
U-KNOW.AI describes Enterprise OS as a platform that prepares data during connection and inherits permissions from source systems. APIs support custom applications. The vendor also offers deployment in your own environment. We establish the connectors, modules and operating model your project needs before implementation.
U-KNOW Enterprise OS on the vendor website ↗Collaboration planned
amber
According to its product documentation, amber combines enterprise search, chat and agents on a shared data layer. Described integrations include SharePoint, network drives and other business systems, with API and MCP access also available. Collaboration with AImation is planned; scope and availability will be specified once agreed.
Platform and integrations at amber ↗Begin with a task you can evaluate.
Bring a recurring question, its source locations and the responsible team to the initial call. This gives us a useful starting scope.
Check data and permissions
Identify sources, document versions and access groups. Select a task whose output the team can assess.
Test the data foundation and application
Connect the required sources, verify mappings and build a scoped workflow. Test answers, evidence and unauthorised access.
Agree operations and expansion
Assign ownership of data maintenance, updates, monitoring and support. Then add further agents, dashboards or apps.
Before we start
Questions worth asking.
Does all company data need to move to a new location?
No. Depending on the platform, approved content is indexed, selectively transferred or queried directly. Before connecting a source, we establish where its data is processed and stored. Moving every repository is not a universal requirement.
Can an AI operating system use SharePoint and file servers together?
This is a typical use case. We check support for your specific systems and file formats against available connectors and interfaces. Permissions and updates must work for each source.
How does the data foundation differ from a chatbot or AI agent?
The data foundation supplies mapped information with provenance, version and permissions. A chatbot answers questions about it. An agent also uses approved tools and executes defined workflow steps. Several applications can share this foundation.
Are cloud and on-premises deployments possible?
That depends on the platform, models and integrations. We review hosting, storage, model access and contractual requirements with your IT team. A local server alone does not determine where a connected language model receives data.
What does getting started cost?
The initial call is free. The KI-Landkarte workshop provides a structured assessment from €1,900. Platform licences, integrations and operating costs for your AI operating system are itemised in the agreed proposal.
Your next step
Which question costs your team the most search time?
In the free initial call, we look at that question and the sources it needs. You receive an initial assessment of where shared data access could help.
