Improve discovery in Rust/Go crate docs via AI semantic search

Developers using Rust and Go routinely spend minutes to tens of minutes chasing authoritative answers across crate and module documentation, source examples, and issue threads because keyword search returns noisy, context-free results. This problem hits individual contributors, onboarding engineers, and teams at companies that rely on these ecosystems—part of the ~25M developers who make up a $12.0B annual market for developer tools (roughly $480/yr per developer). You could build a semantic-search platform plus IDE integration that indexes crates.io, pkg.go.dev, and repository docs, computes code-aware embeddings (signatures, examples, type info), and serves retrieval-augmented answers with exact citations and runnable snippets; offer hosted SaaS, self-hosted vector stores, and org-level controls with per-seat and enterprise pricing. The market score for this idea is 92/100 and revenue potential is 80/100 given willingness to pay for productivity in critical engineering flows. This is attractive now because retrieval+LLM fusion has matured, vector stores and efficient embeddings have driven down inference costs, and Rust/Go ecosystems expose structured docs and stable registries that are straightforward to index. To stand out, specialize: invest in language-specific tokenizers and signature-aware embeddings, expose provenance and executable examples, maintain a public benchmark of doc Q&A accuracy, and optimize for low-latency editor embeddings and local fallback stores; competitors are medium—generalist search and code-assist products exist but typically sacrifice precision and language-specific signals. Be honest about challenges: keeping indexes fresh at scale, handling varied licensing, controlling hosting costs for embeddings, and proving answer correctness to earn developer trust.