Context grows.The load doesn't have to grow.
From AI assistants to RAG systems, from agent workflows to document analysis, Aether Compress can be adapted to different use cases where you can reduce the unnecessary input load sent to the LLM.
Applications are different.The context problem is the same.
As AI applications grow, so does the information sent to the model. grows. Conversation histories, retrieval results, tool outputs, documentation and system instructions with each new request. It becomes part of the input cost.
Aether creates a separate layer where this load can be optimized before reaching the LLM.
Anywhere that uses contextThere may be room for optimization.
The field of use is more than the sector, it is the input sent to LLM. It's about the structure.
Carry long conversations lighter.
As AI assistants grow, conversation history, system instructions, and additional context may be carried over in every request. Aether Compress helps reduce input load by optimizing the context sent to the model.
You don't have to move the entire fetched context.
The retrieval layer can return numerous document pieces for a single query. Aether helps you create a smaller input by optimizing the retrieved context before an LLM call.
Don't resubmit the same load for every support request.
Support bots can carry customer history, product documentation, previous messages, and support rules in the same request. Aether ensures that this context is optimized before the LLM.
As the agent grows, the context does not have to grow as well.
In agent systems, tool results, task history, and intermediate steps can quickly accumulate within the context. Aether can be used to reduce the input load carried over before the next model call.
Send large technical context more efficiently.
Code assistants and technical AI tools can carry source code, error logs, configurations, and documentation within the same context. Aether can optimize this input before the model call.
Do not re-move long documents for each question.
In AI applications working on reports, contracts, manuals and corporate documents, the required context can be optimized according to the query and smaller input can be sent to the model.
According to context.According to the query.
Optimizing content without tying it to a specific question It can be used in the streams you want. conversation histories, Systems that carry agent state or general context are examples of this.
If the user's query is known in advance, Aether can include the query in the optimization process and process the context focused on that request.
When the Retrieval is overoptimization can begin.
Aether does not replace the retrieval system. It is added between the retrieval result and the LLM call.
Your app takes the question.
Your existing RAG infrastructure fetches the relevant document fragments.
The fetched context is optimized considering the user query.
The optimized context is sent to your current model provider.
The difference in tokens in a single request may seem small. Same optimization in thousands or millions of LLM calls When repeated, the difference in total input volume grows.
Aether is not a model selection. Since it works during the context preparation stage, it can be added in front of your current AI flow.
If the problem is tokens,The sector is in the background.
Aether is not an optimization layer designed according to the terminology of a specific industry. Therefore, the same architecture can be used in different AI applications.
Software products that use AI features.
Product, support and shopping assistants.
Document and knowledge-based AI applications.
Long document and contract analysis.
Internal knowledge bases and employee assistants.
Applications that use code, logs and technical context.
Get context.Or get the answer.
Optimized context returns directly to your application. Your existing infrastructure makes the next LLM call itself.
The context is first optimized with Aether Compress, then sent to the AI provider you choose, and the final answer returns to your application.
Not every request needs to be compressed.
If the context is already very small, there may be limited overhead to optimize.
The total economic impact may naturally be lower for applications that call for very few LLMs.
If the input has already been heavily optimized, the scope for additional reductions may be limited.
The most accurate decision is obtained by testing Aether on your own actual inputs.
Best use caseyour real data.
Try Aether Compress on your own context and measure how much space it takes in your input load.