Sine is a context optimization engine for AI applications. Instead of sending an entire repository, document, or knowledge base to a language model, Sine identifies only the information necessary to answer the prompt. The result is smaller prompts, lower token usage, faster responses, and more efficient AI workflows.
Join Early AccessMany AI applications send thousands of unnecessary tokens with every request. Large repositories often include unrelated files, duplicated documentation, generated code, boilerplate, and historical context that provides little value for the question being asked. Those extra tokens increase cost, increase latency, and make it harder for language models to focus on the information that matters.
Understand the user's intent and determine which concepts are actually relevant.
Locate only the files, functions, documentation, and dependencies related to the request.
Remove duplicated, irrelevant, or unnecessary context while preserving important relationships.
Return a minimal context package that is ready for any supported language model.
Analyze projects beyond embeddings by understanding functions, classes, dependencies, imports, and relationships.
Combine semantic search, keyword search, repository graphs, and reranking to identify the most relevant information.
Reduce unnecessary prompt content while preserving the information required for accurate AI responses.
Designed to integrate with existing AI providers and workflows without changing your application architecture.
Ideal for engineering teams and organizations using AI every day. Unlimited projects with predictable monthly billing.
Designed for applications with variable traffic. Pay only for the context optimization your application uses.
As AI systems continue to grow in capability, efficiently managing context becomes increasingly important. Sine is built to help developers and organizations use language models more effectively by sending only the information that is relevant to the task at hand.
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