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In this work we present our synthetic data generation pipeline, agent harness, and training methodology alongside a comprehensive evaluation of Context-1 across a range of retrieval benchmarks. Our results demonstrate that a purpose-trained 20B model can reach the Pareto frontier of retrieval performance with respect to cost and latency, matching or exceeding frontier models that are orders of magnitude larger at a fraction of the compute.