German Startup GDPR compliant EU-based Open MCP standard

ānbāsan - work with | in context

One app.
Context for all.

Data from mail, calendars, notes, tasks, and files becomes usable context for users and AI. Results flow back automatically to where they belong.

Manage context across all your sources

File data back into the right context, automatically

Ready for AI via MCP

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ānbāsan Context Management turns app data into intelligent context for users and AI.
// Sound familiar?

Gathering files for the AI — then filing the results away yourself again

The brains of the work – sidelined by operational errands.

Accepted until now, because there was no better way.

Apps & Data
AI Chat
01 — The gap
A gap yawns between apps & data and the AI.
02 — Bridging – by hand
The data is gathered and loaded into the chat.Extra work that just tags along.
03 — The AI works
Now the AI's strengths come into play. Good results – and a few new files.
04 — Same gap, bridged again
The same gap once more – now in the other direction: download files and file them away.The same work again, this time back toward the apps.
05 — Until now
That's a thing of the past. From now on it's simpler: no gathering, no bridging.
// Our solution

We make context the new work environment​

The Context Manager enables Context-DrivenWorking (CDW): context emerges organically in the course of work, instead of being gathered anew every time. ānbāsan automatically brings data together across all apps into usable context – available at any time, without copy-pasting or manual pre-sorting. And the AI works with and within this context.

01

Build context — effortlessly across all apps

Emails, notes, events, tasks, and files flow into the context automatically, as a by-product of working – without pre-sorting. Simple, organic growth.

02

Manage context — retain it, effortlessly.

The Context Manager recognizes relationships, assigns data automatically, and forms topic clusters. The final decision always stays with the user. Relationships can be changed and adjusted at any time.

03

Use context — every AI gets access

Via the open MCP standard, every AI works with and within the context: load the data it needs and store new data right where it belongs.

Interested in a live demo or have questions about ānbāsan?

Feel free to get in touch. Or dive deeper first – here are all the features.

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Learn more
// Example

What Context-Driven Working looks like in practice

The video shows what Context-Driven Working can look like – one of many possibilities. The data in the example was consolidated and linked by the Context Manager in the background.

01

Choose a data set

Select a data set – here, our sample data set.

02

Copy the knowledge graph

Copy the knowledge graph to the clipboard: which data is in the context and how it connects.

All at the push of a button, system-generated.

03

Paste into the AI chat

Paste it into the AI chat and get started right away.

04

The AI works within the context

From now on the AI works within this context too: searching data, retrieving knowledge graphs or saving results in the right context. In the example it summarizes the relationships, loads an email, and creates a note.

05

Save new data

The AI files the note back into the context.

// What has changed

No more searching, copying and re-saving files. Just paste the context and get going. No more errands – no more fetching and filing data, just stating what should happen.

A seamless workflow without friction. That is exactly what Context-Driven Working makes possible.

// For who

For everyone and every company that wants to work more efficiently

Context-Driven Working works at every level – from the individual user to entire organizational units. The context stays the same, only its scope grows.

01 · Individual

AI users

Work with the AI directly in the right context – without searching, copying or filing data.

02 · Mehrere Personen

Teams

One shared context for everyone on the team – everyone works from the same level of knowledge.

03 · Mehrere Teams

Departments

Contexts linked across team boundaries – knowledge flows between areas instead of fading out.

EU-based & GDPR-compliant

All data stays in the EU. Your context, your data – your property.

No duplicate storage

Content stays at its source. Only the necessary metadata is synchronized.

Open to every AI tool

Usable with any AI application via the open MCP standard, entirely without lock-in.

Interested in a live demo or have questions about ānbāsan?

Get in touch right away.

Book a demo
// Architecture

From Concept to Implementation

Context-Based Working describes how work is done. The Context Manager implements it technically.

CDW

Context-Driven Working

The concept: data no longer has to be structured by hand for AI use. Context emerges organically, as a by-product of working.

CM

Context-Manager

The technical solution: assigns data automatically, forms topic clusters, and keeps the context up to date.

CaaS

Context as a Service

The Context Manager provides the context as a service – among other ways via MCP servers, directly usable by any AI tool.

CL

Context Layer

The layer where the context lives – between app data and AI, reachable from both sides.

AI tools & apps

Access via MCP

Context-Layer

Maintained by the Context Manager (CM)

App data

...

MCP (Model Context Protocol) is an open standard that enables AI applications to access external data and tools.

Each request receives only the relevant slice of the context – not indiscriminately everything that might somehow fit. This lowers input token usage. And because the context is right from the start, fewer follow-up questions are needed to reach the desired answer: this saves output tokens and iterations.

// Without CDW

The challenge of digital work and AI​

Analyzing the cause and consequence: AI requires precise context, shifting the burden of manual data preparation onto the user.

The Origin of the Challenge
Fragmented data sources and missing context

Data is scattered across apps for emails, notes, tasks, and files. As a result, AI lacks the necessary connections. Result: missing context leads to generic or incomplete answers. The true potential of AI remains untapped.

Token usage

The less precise the context, the more irrelevant data is processed – and the higher the token usage.

The Impact on the User
Manual overhead

Users have to gather the data themselves, for every app, before handing it to an AI. That costs time and is avoidable effort. In both directions.

Knowledge isn't the problem – missing context is

Most apps store information but don't connect it meaningfully. That is exactly where productive AI fails. ānbāsan turns scattered knowledge into a usable working context – so AI doesn't just answer, but helps right on the spot. Emails, notes, documents, files, and tasks exist separately from one another. Users hold the connections in their heads – AI doesn't.

The solution is Context-Driven Working (CDW): instead of requiring afterthought organization, context develops natively alongside your daily workflows — seamlessly integrating with emails, notes, events, files, and tasks.

More about the Context Manager
// The CDW solution

Achieve more with Context Management

Digital work doesn't fail for lack of information, but because that information is managed too dispersed and without relationships to one another. Emails, notes, documents, calendar data, tasks, files, and AI chats together create a valuable information space – which is, however, often scattered and hard to use as a coherent whole.

Context Management with a context-driven working model starts exactly there: information isn't just stored, its relationships are consistently captured as it is created. The goal is not order for order's sake, but the best usability in everyday work – without extra effort.

Decide better and faster

All relevant information in one place instead of spread across five apps. Easily reachable by navigating within the context.

Less effort

No extra data room, no pre-sorting: the context organizes itself automatically according to your own rules. MCP makes access easier.

More precise AI answers

The AI works with complete, fitting context instead of fragments.

Fewer tokens, fewer steps

Targeted context lowers input token usage. Accurate answers save iterations and output tokens.

// Savings

Fewer tokens. Same answer.

10-55 %

fewer context input tokens per request
Without CDW100 %
With CDW45–90 %

Instead of indiscriminately sending along everything that might fit, each request receives only the relevant slice of its context. Depending on the use case, this lowers input token usage by 10–55%. And because the context is right from the start, fewer follow-up questions are needed to reach the right answer – the AI gets to the point faster. Proportionally, that also saves output tokens and entire iterations.

The actual savings depend on the data situation, the model and the use case.

// Contact

Ready for the first step?

Live demo, questions or want to dive deeper? Get in touch – or first read more about Context Management.

For new features and learnings around Context-Driven Working, there's also the newsletter.

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