one context layer.
every role.
The context layer joins your chat, tickets, wiki, CRM and meetings into one model of the business, and hands every role the context it needs, with the source. Here is what that looks like at their desks.
Know what changed2
No role here reads this source.
Decide2
No role here reads this source.
Ask and act2
No role here reads this source.
Run in your tenant2
No role here reads this source.
Scenes are synthetic: real surfaces, fictional customers, people and records.
Managing directors know by 8:00 what changed.
One short brief at the start of the day: what moved with your customers across chat, tickets, wiki, CRM and meetings, every line with its source. Someone who was not in the meeting learns what was decided without asking anyone who was.
- Sources
- all five sources
- Scene
- the morning brief
- Runs in
- your cloud tenant · EU inference
- Writes
- only after a person approves
Scenes are synthetic: real surfaces, fictional customers, people and records.
Account managers catch every customer change.
What changed since you last looked, folded per customer, the mention cited or the item dropped. 1 day at minimum, 7 at most, so a week away is caught up instead of silently lost. A source that did not answer is named, so a quiet radar and a failed read never look the same.
- Sources
- ChatWikiCRMMeetings
- Scene
- the radar
- Runs in
- your cloud tenant · EU inference
- Writes
- only after a person approves
Scenes are synthetic: real surfaces, fictional customers, people and records.
Project leads leave the call with decisions, not notes.
The meeting is written up while it runs: notes, decisions, who agreed to do what. Each decision lands in your proposals, one row at a time, to accept, edit or decline. Colleagues get the write-up, never the transcript.
- Sources
- Meetings
- Scene
- the meeting room
- Runs in
- your cloud tenant · EU inference
- Writes
- only after a person approves
Scenes are synthetic: real surfaces, fictional customers, people and records.
Sales sees the deal next to the work.
CRM stage and close date right beside the ticket that holds up the go-live. The question no single system answers, answered in one place, with both sources.
- Sources
- TicketsCRM
- Scene
- the pipeline
- Runs in
- your cloud tenant · EU inference
- Writes
- only after a person approves
Scenes are synthetic: real surfaces, fictional customers, people and records.
New colleagues ask the company and get sources.
Ask about a customer, a page, a decision. The answer cites where it comes from, shows only what you may see, and says “not found” instead of inventing one.
- Sources
- TicketsWikiMeetings
- Scene
- the archive
- Runs in
- your cloud tenant · EU inference
- Writes
- only after a person approves
Scenes are synthetic: real surfaces, fictional customers, people and records.
Team leads let AI act and still sign every action.
Comment on the ticket, append to the wiki page, reply in the thread, create a work item, all as yourself. Nothing is written until you tap approve. Every tap leaves a receipt, and a decline is an audited decision too.
- Sources
- ChatTicketsWiki
- Scene
- the approval desk
- Runs in
- your cloud tenant · EU inference
- Writes
- only after a person approves
Helio asked whether the import can take 1,200 legacy items before go-live.
Scenes are synthetic: real surfaces, fictional customers, people and records.
IT leads keep it in their own tenant.
It runs in your company’s cloud tenant, with inference inside the EU. Answers are trimmed per person, no external action runs without authorization, and every step is audited. Production-ready before anyone says yes.
- Sources
- all five sources
- Scene
- the tenant
- Runs in
- your cloud tenant · EU inference
- Writes
- only after a person approves
Scenes are synthetic: real surfaces, fictional customers, people and records.
Engineers plug the same context into Claude and Codex.
The same backend over MCP, inside Claude, Codex and any MCP-enabled app. Same identity, same approval gate, same audit trail as in chat. Nothing new to govern.
- Sources
- all five sources
- Scene
- the editor
- Runs in
- your cloud tenant · EU inference
- Writes
- only after a person approves
Scenes are synthetic: real surfaces, fictional customers, people and records.
What clients say about working together.
René has a strong instinct for identifying operational weaknesses that others often overlook. In our work together, he was always the one questioning existing processes and finding practical ways to improve efficiency. He thinks in solutions and delivers results.
An experienced Enterprise Architect who consistently exceeded expectations. He designed and developed an AI-driven toolset that extended LeanIX, enhancing how enterprise architecture insights are generated and used. I highly recommend him to any organization seeking a forward-thinking and results-driven architect.
René is truly a solution architect. He manages, executes and delivers on key projects with precision, dedication and calmness. He drives large projects and still takes time to help in the details.
Rene's contribution has been fundamental for the successful migration to O365 of all users of the newly acquired group. Always available to help with his wide knowledge, greatly appreciated by users and team members.
I worked with Rene, and found that he is extremely skilled at putting together information for the big picture, coordinating diverse needs and integrating a wide variety of systems. His strategic use of AI makes tasks much more efficient. Highly recommended.
Someone who is keen to help and quick to deliver practical solutions. Willingness to think beyond standards and approach challenges from different angles — a reliable, solution-oriented colleague who brings both creativity and persistence to his work.
What happens next is your call.
One context-to-action workflow, built in your tenant: production-ready, controlled, operational.
30 minutes, booked right here. Your topic, no preparation.
The case study: a context layer for AI agents, with approvals in operation.
Open the case study →No pilot without a path to production. No automation without clear ownership.
Hourly rate agreed up front · Billed for the hours worked