Problem

Compresr addresses the cost, latency, and context-window bottleneck in LLM applications. Developers routinely send models far more context than is necessary to answer a particular question, yet still pay for those input tokens and wait for them to be processed. Naive truncation can remove the answer, while question-agnostic compression may preserve irrelevant text and discard important evidence. Compresr’s website claims reductions of up to 90% in tokens, with corresponding savings in cost and latency.

The clearest use case is retrieval-augmented generation over long, dense documents: compress retrieved passages before they reach the model, so applications pay only for answer-bearing content. The same pain appears in long-running agent conversations, noisy tool outputs, coding assistants, and document analysis. Compresr illustrates the economics with a Boeing 10-K example in which 112,552 tokens become 498, producing an advertised 86% lower per-query cost; its FinanceBench example reports roughly 47% savings at light compression while improving accuracy from 73% to 77%.

Product / Service

Compresr is a query-aware context-compression API. An application sends the original context together with the question or instruction it needs answered; Compresr identifies and retains the spans relevant to that task, drops the rest, and returns a shorter context for the application’s existing LLM. It is designed as a layer in front of an existing model stack rather than a replacement for the downstream model. The company offers the stable latte_v1 model and a newer latte_v2 beta, including dynamic selection of the compression ratio.

The delivery model combines a drop-in SDK and API with an open-source Context Gateway proxy for agent workflows such as Claude Code and OpenClaw. Integrations cover common orchestration frameworks including LangChain, LangGraph, LlamaIndex, and LiteLLM. The hosted product uses per-million-token pricing and offers free signup credits, while an on-premises option runs inside a customer’s VPC or data center with custom throughput, latency SLAs, and direct support. The benefit is a tunable trade-off between context reduction and answer quality, with the company positioning light compression as accuracy-preserving and more aggressive compression as a way to prioritize cost.

Market

Compresr competes in the emerging LLM context-compression and context-management infrastructure category, serving RAG systems, agent platforms, coding assistants, and other high-token workloads. Its alternatives include in-house truncation, summarization, or retrieval filtering; open-source research approaches such as LLMLingua, RECOMP, and AutoCompressor; and native context-compaction features from model providers such as Anthropic, OpenAI, and Google. Its differentiation is query-conditioned extraction delivered as an API and middleware layer, rather than generic truncation or provider-specific compaction.

The company is an active YC Winter 2026 startup founded in 2026 with a four-person team, and it has publicly launched both its API and open-source proxy. Public materials provide early adoption signals, including free API credits and a third-party report of 595 GitHub stars for Context Gateway, but they do not disclose customer counts or revenue. The most accurate characterization is therefore early commercial traction with revenue and scale undisclosed, rather than a company with publicly demonstrated material revenue.