Context is the missing layer in digital work and AI
ānbāsan is built on a new paradigm for work: information is not merely collected, but transformed into context — enabling users and AI to work together more efficiently, while significantly reducing token usage.
// Sound familiar?
Before AI
can help, you do its
prep work first.
Knowledge isn't the problem — missing context is.
Data is scattered across emails, notes, appointments, and tasks. Before your AI can get started,
you're the one left pulling it all together.
Without CDW
One task – many detours
01
You want to give the AI a task
02
First search for a document – context is missing
03
Another document, that's also important
04
Upload everything
05
Explain the background
06
And only then the task you actually care about
➜
With ānbāsan
That's all it takes
➤
// How it works
Context becomes the new work environment
ānbāsan automatically connects your data into context you can access and use at any time — without cumbersome prompts or manual pre-sorting.
01 — Collect organically
Effortless collection
Emails, notes, appointments, and tasks flow automatically into your context as you work — no pre-sorting required.
02 — Organize automatically
Connections emerge
The Context Manager recognizes relationships, assigns data via auto-assign, and forms topic clusters. You always have the final say.
03 — Use via MCP
Every AI gets access
Via the open MCP standard, every AI directly accesses the right excerpt of your context — precise, without copy-paste. And saves tokens in the process.
// Savings
10–55 %
fewer context input tokens per request
No CDW
100 %
With CDW
45–90 %
Fewer tokens.
Same answer.
Instead of sending along everything that might be relevant, each request receives only the right excerpt of its context. This reduces context input token usage by 10–55 % depending on the use case.
And because the context is right from the start, your AI doesn't just need fewer follow-up questions to reach the right answer — it gets to the point faster. That also saves output tokens and entire iterations — overhead that otherwise silently accumulates in every conversation.
Actual savings depend on data situation, model, and use case.
Curious to see ānbāsan live or have questions? Just reach out. Want to dive deeper first? Explore all features here.
// For who
For everyone who wants to work more efficiently
Scattered data, too many apps, rising AI costs — if that sounds familiar, you’re in the right place. ānbāsan connects your workflows into a shared context: so your AI needs fewer tokens, you spend less time on prep, or simply have your data organized in a way that actually works for you.
EU-based, GDPR-compliant
Deine Daten bleiben in der EU. Dein Kontext, deine Daten – dein Eigentum.
No duplicate storage
Inhalte verbleiben auf ihrer Quelle. Nur notwendige Metadaten werden synchronisiert.
Works with Any AI Tool
Über den offenen MCP-Standard nutzbar mit jeder KI-Anwendung, ganz ohne Lock-in.
// ARCHITECTURE
From Concept to Implementation
Context-Driven Work describes how you work. The Context Manager makes it possible.
The Concept: You no longer have to manually structure your data for AI use. Context emerges organically as you work.
The Technical Solution: The Context Manager automatically organizes information, builds topic clusters, and keeps your context up to date.
The Context Manager provides your context as a service — including through MCP servers that can be used directly by any AI tool.
The layer where your context lives — between application data and AI, accessible 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.
// WITHOUT CDW
The challenge of digital work and AI
Scattered data
Today, AI agents operate on fragmented data spread across emails, notes, task managers, and countless other tools.They see pieces of the puzzle, not the whole picture — and poorer results reflect that.
Missing context
AI doesn’t know the context behind the data. The result: incomplete, generic answers — and AI potential left untapped.
Manual overhead
Users are forced to manually piece together data from every app before giving it to AI — wasting time on work that should be unnecessary.
Token usage
The less precise the context, the more irrelevant data is processed — and the more tokens are wasted.
Knowledge is not the problem - lack of context is
Most apps store information. But they don’t connect it in a meaningful way. And that’s exactly where productive AI fails. ānbāsan turns scattered knowledge into a usable work context—so AI doesn’t just respond, but truly helps.
Emails, notes, documents, files, and tasks exist separately from one another. Users hold the connections in their minds—AI does not.”
The answer is Context-Driven Work (CDW): Context doesn't emerge later through organizing and cleanup. It emerges organically as emails, notes, meetings, and tasks are created throughout the normal flow of work.
Learn more about the Context Manager.
// Meet CDW
Work smarter with Context Management
Digital work does not fail because there is too little information, but because information is too dispersed and managed without meaningful relationships. Emails, notes, documents, calendar data, tasks, files, and AI chats together create a valuable information space — yet this space is often highly fragmented and difficult to read or use as a coherent whole.
Context management, through a context-based working model paradigm, starts exactly there. This model describes an approach in which information is not only stored, but, above all, its relationships are captured consistently from the moment it is created. The goal is not order for its own sake, but the best possible usability in everyday work — without adding extra effort.
Benefits
Make smarter decisions, faster.
All relevant information in one place instead of being scattered across five different apps. Easily accessible through contextual navigation.
Less overhead
No data rooms. No manual organization. Your context structures itself automatically based on your rules. MCP provides seamless access to context — without the need for separate data repositories.
More accurate AI responses
AI works with complete, relevant context instead of isolated fragments.
Fewer tokens, fewer steps
Targeted context reduces input token usage. More accurate responses mean fewer iterations and lower output token consumption.
Ready for a live demo or do you have questions about ānbāsan? Then get in touch with us right away! Or if you’d like to learn more first, read more about Context Management here.”
// CONTACT
Ready for the next step?
Ready for a live demo, have questions, or want to learn more? Contact us — or explore Context Management in more detail first.
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