Your once manageable microservice has grown into a sprawling system of 50+ services, with new features shipping faster thanks to AI. What used to take a month of careful coding now happens in days through AI-assisted development and automated code generation.
But this acceleration comes with a cost. The AI output isn't consistently reliable—what works in testing might fail in unexpected ways in production. As codebases get larger and more complex, the gap between what exists in your systems and what any developer can comprehend keeps widening.
Picture this: At 2 AM, you're trying to understand AI-generated code that's causing production issues. A database connection has timed out in your distributed system. The stack trace leads you to unfamiliar connection handling code from a recent AI-assisted refactor. This code seems to be missing appropriate types, and is difficult to understand. The AI that helped write it can't explain it because it lacks semantic understanding of how the code actually behaves—its explanations are now as confused as you are. This is where better tooling makes the difference: tools that understand not just what code looks like, but how it works.
Gaps in AI coding today
A few weeks ago, our team spent a day chasing down a production issue that should have been a quick fix. The culprit? A seemingly innocent database query, introduced by AI, that looked pristine in code review but harbored a subtle flaw. The AI had defaulted to querying a database replica instead of the leader—a choice that's usually correct most of the time, since we want the code to query the replica for better read performance, but certain operations—like audit trails for real-time detection systems—need the consistency of the leader. This kind of institutional knowledge about when to query the leader may live in repo docs, commit messages, infrastructure configuration files, and documentation rather than the code itself. This is context that humans have, machines don't yet.
The AI did everything "right"—proper typing, async/await patterns, parameterized queries, error handling. But it missed crucial context scattered across our system: a two-month-old commit message specifying "detection results require leader reads", infrastructure configs marking detection history as audit critical, and an architecture doc stating "image classification verdicts must use primary DB."
Dealing with this issue got us thinking about other gaps in AI coding tools today:
- Complexity at scale: As AI writes more code, systems are growing faster than humans can comprehend them.
- Reliability: AI tools are making decisions with limited context, leading to unreliable or inconsistent outputs that can cause unexpected issues.
- Tool fragmentation: Developers switch between several tools to write, maintain, and understand system behavior.
These gaps exist for several reasons. First, AI coding tools excel at generating code but underperform in contexts that extend beyond a single repository, where multiple services and projects are involved. Second, since they’re trained on large corpuses of code, they inherit the flaws of their training data without true semantic understanding needed to ensure code correctness. Third, and perhaps most frustratingly, their output style and choices often vary between invocations—you might get TypeScript with strict types one day and loose JavaScript the next—creating codebases that feel like they were written by different teams following different standards. This inconsistency makes maintenance even harder.
Introducing Nuanced: semantic understanding for AI tools
We're building tools that combine static analysis with AI to obtain a deeper understanding of code behavior. By doing so, we help both humans and machines understand code behavior at a fundamental level.
We’re starting by building an AI-powered Python language server that goes beyond checking types—it understands patterns across your entire codebase, and can suggest fixes based on your team’s established patterns. For example, if your system uses optimistic locking as a preferred concurrency control pattern, it will flag deviations from that pattern. It does this by analyzing your entire system—code, configs, logs, and deployment patterns—to build a comprehensive understanding of how your services actually behave. This context can be used by any AI coding tool to generate more reliable code.
Nuanced’s purity analysis can help AI code assistants (like Aider/OpenHands) more effectively identify potential mutation-related bugs by providing them with explicit information about:
- Which functions modify state
- What state they modify
- The call graph showing how state modifications propagate
Our technology integrates with existing AI coding tools or your preferred editor—whether that’s VS Code, PyCharm, Vim, Emacs, or Cursor—and provides insights about your code, from state mutations and side effects, exception paths and error handling, or data flow and dependencies.
Our vision: beyond static analysis
While AI has transformed how we write software, we believe its true potential lies in bringing deep understanding to the development process. We're starting with static analysis today, but our vision extends to creating a complete semantic understanding layer for AI tools.
Our roadmap:
- Enhanced code generation: Help AI tools make better decisions by understanding code implications before generating new code. Connect static analysis with runtime behavior to give AI tools comprehensive insight into how code actually works in order to write better code.
- System-wide context: Help AI reason about interactions across services by synthesizing information from different tools, logs, metrics, and deployment environments.
- Semantic debugging: Give AI tools the context they need to understand code behavior, leading to clearer explanations during debugging sessions and more transparent reasoning about their choices.
We're building these capabilities because we believe code you can't understand is code you can't trust. As AI accelerates development, the tools for understanding and maintaining code must keep pace.
Built by developers who understand the problem
We're ex-GitHub engineers and researchers who've scaled some of the world's largest developer platforms. I'm Ayman, and before founding Nuanced, I spent seven years at GitHub where I helped build Semantic—an open-source library for parsing and analyzing code across languages—and scaled security systems to detect anomalous code patterns across millions of repositories. Our team’s deep experience in static analysis and large-scale system design shapes our approach to the AI reliability challenge today.
We've all been on-call at 2 AM, untangling complex service dependencies, and more recently, we've seen firsthand how AI accelerates development—both the wins and the wounds. While everyone else races to ship new features with AI, we're focused on addressing the gaps in AI coding tools, and ensuring those features are reliable and maintainable rather than code that works today but becomes a liability tomorrow.
Join the future of coding
We're creating components that make AI coding tools more reliable and effective. Whether you're building the next-generation IDE or AI coding assistant, our technology helps your tools make better decisions. Join the waitlist, drop us an email at ayman@nuanced.dev, or DM us on twitter.