Company Performance Metrics
- Ayman Nadeem: CEO
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 prod, 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.
