Neural Inverse x Solana Foundation | Efficiency &  Security layer in workflow

AIR is our live threat intelligence engine. It continuously scans a developer's active code against real-world data from GitHub, Stack Overflow, and on-chain activity to identify complex vulnerabilities, logic flaws, and business-risk loopholes that traditional static analyzers cannot detect.

Harvey is an intelligent co-pilot for on-chain development. It goes beyond simple code suggestion to orchestrate developer tools, automate complex multi-step tasks like writing and running tests, and assist with project-wide refactoring, acting as a true automated assistant.

AIR's dynamic multi-model architecture works by having a lightweight routing model evaluate code via token embeddings to score task categories (e.g., logic, security, gas optimization), then compute cosine similarity against specialist profiles to select and activate the best match for real-time inference against live external feeds like Chainlink oracles and internal sources like commit histories, yielding annotated outputs with confidence scores.

Harvey is a specialized Qwen 2.5 Coder model 32B, fine-tuned within Azure using a $20,000 compute budget already trained in Solidity data and $35,000 compute budget for Solana(Rust/Anchor) . We train it on a proprietary, curated dataset of 50 million tokens focused on secure on-chain development patterns and orchestration, via the Reasoning Trace Method over 4 epochs. This equips Harvey with domain-specific expertise to assist with complex, practical tasks with zero hallucination in domain-specific tasks.