SID.ai

SID.ai trains AI models to find the documents other AI systems need to answer questions. Its SID-1 agentic retrieval model accepts natural-language queries, searches customer-selected document collections and returns ranked results. Developers can integrate it into existing search or retrieval-augmented generation systems, or use it as a search subagent for a larger language model. Returning documents rather than synthesized answers lets the downstream system handle answer generation while SID-1 handles retrieval. SID describes access through an API, AWS Bedrock and self-hosted deployment, with a gradual rollout and a public waitlist.

Founded in 2023 by Maximilian-David Rumpf and Lotte Seifert, SID is led by Rumpf as CEO. Rumpf previously researched AI accelerator design at ETH Zürich; Seifert holds degrees in computer science from ETH Zürich and economics from the University of St. Gallen. The company has a team in San Francisco and Zürich and lists Y Combinator, Canaan, Rebel and General Catalyst as backers.

SID’s retrieval research applies multi-turn reinforcement learning to Qwen3-14B, training the model to search, read results and adjust its approach to each question. Its reward measures both whether it finds relevant documents and how well it orders them, so downstream systems can take the highest-ranked results when context space is limited. SID deliberately favors finding an extra document over omitting a crucial one, accepting some loss of precision. Parallel tool calls reduce round trips to the search backend, while initial searches return short excerpts and a separate read tool lets the model selectively open full documents. This gives it room to inspect more candidates without filling its context with every document’s full text.

SID’s service terms describe self-serve and enterprise access, with pricing options that may include subscription minimums and usage-based charges. Customers retain ownership of their inputs and outputs, including submitted documents and returned rankings. SID commits not to use that customer content to train, fine-tune, distill or otherwise improve AI models; the commitment survives termination of the terms.