Nuanced

nuanced.dev

AI coding tools can write and refactor code, but drop them into a complex repo and they struggle. The result is often hallucinated helpers that don't exist, tests that pass yet break in production, and hard-to-follow edits scattered across files with no clean code separation. The issue isn’t a lack of model horsepower; it’s context. Code isn’t just text; it’s the source of truth for how a system actually behaves. At runtime, that truth lives as a graph of calls, types, and side effects, yet today’s LLMs still see it as a flat wall of tokens. Nuanced fixes this context gap. We give AI coding tools an on-demand, compiler-grade map of the codebase: calls, types, and side effects—so they always know what’s safe to read, write, or refactor. We’re tackling this because AI coding won’t scale until models can reference the codebase’s ground truth as confidently as a compiler does. Probabilistic guesses are fine for drafts, but production systems need deterministic answers: Which function really owns this side effect? What breaks if I rename this type? Nuanced answers those questions by generating and feeding that map into any SWE agent, review bot, IDE plug-in, or CI auto-fixer, letting them reason and modify code against the real structure instead of guesses. As context windows grow, this structural map becomes even more critical: bigger prompts still need the right information, grounded in the canonical source of truth. The result: AI coding agents that stay grounded, stop hallucinating, and safely handle the complexity of modern codebases.