Kindo Launches Deep Hat Sift to Cut LLM Costs on High-Volume Data
Los Angeles – October 29, 2026 -- Kindo has launched Deep Hat Sift, a self-improving model that decides which events in alerts, tickets and logs need a large language model and skips the rest. The product targets enterprises that pay for an LLM to review every event or keep AI agents off their busiest data sources altogether.
Sift learns from the verdicts of the agent it serves
Deep Hat Sift is a System One decision model, a fast, inexpensive class of AI that answers yes or no rather than generating text. Fixed-weight filters must be retrained and re-released to improve. Sift instead trains on the verdicts of its target agent, so no labeling project or vendor retraining is required.
Each Sift model is assigned to one data source, one target agent and one filtering task. According to Kindo, a large enterprise can run hundreds of thousands of them at once.