Grounded contextfor AI coding assistants

We feed your LLM a live call graph and symbol links, so suggestions compile, tests pass, hallucinations vanish, and prompts use 30% fewer tokens.

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semantic_analysis.py
1  import nuanced
2  from my_llm_api import call_llm
3  
4  # Extract semantic understanding from entire codebase
5  context = nuanced.analyze_codebase("./my_project")
6  
7  # Pass to LLM for intelligent code understanding
8  response = call_llm(
9      prompt = "Add retry logic to every external API call",
10     context = context
11 )

Get started

Try our open source version to experience Nuanced's core capabilities, or request access to our enterprise offering for advanced features and support.

Try Nuanced Open Source

How it works

1

Scan repo → build call graph

2

Select the slice your prompt needs

3

Pass to any LLM for accurate answers and code that compiles

Proven impact

33%
reduction in token spend
Higher first-pass build success
(customer reports)
Large drop in hallucinated helpers

Local-first, runs anywhere

Universal compatibility

Works with any LLM or coding workflow: OpenAI, Claude, Cursor, VS Code, even your CI pipeline.

Privacy-first

Analysis never leaves your machine.

Built by developers who've been there

We're ex-GitHub engineers and researchers who've scaled some of the world's largest developer platforms. We've seen AI's potential—and its pitfalls. We're dedicated to advancing AI coding tools by addressing their limitations, and ensuring they truly empower developers.

For tool builders

Creating a software‑engineer agent or automated code reviewer? Nuanced pipes a precise map of your codebase into every LLM call, so the model writes code that builds, tests green, and stays free of make‑believe fixes.

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