SubQ

Founded by Alexander Whedon

Execute fast structured decisions across long context windows for automated agents

Pricing model

Paid

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use SubQ Snap to execute fast, structured decisions across massive context windows within agentic workflows. Instead of relying on expensive frontier models for routine logic, Snap handles tasks like selecting tools, routing requests, and verifying data across up to 384K tokens of codebases, documents, or interaction histories. By isolating critical token relationships with sparse attention, it delivers fixed outputs such as scores, labels, and classifications in seconds. This reduces operational inference costs while preserving precision across complex multi-step automated software environments.

What SubQ does

Route API and tool calls within autonomous agent workflowsClassify documents across extended multi-hundred-thousand token inputsScore retrieval accuracy for RAG pipelinesExtract structured categorical labels directly into software codeFilter irrelevant codebase files before passing data to generative modelsTriage incoming queue items using fixed decision criteriaProcesses up to 384K tokens of multimodal context in a single callEmploys Subquadratic Sparse Attention to reduce computational overheadOutputs directly parseable structured answers including labels and scoresDelivers inference latency measured in seconds rather than minutesMaintains zero cost for output token generationBypasses dense attention by targeting only relevant token connectionsIncludes initial free quota of 100 million tokens for early adopters