Nuraline

Introducing Nuraline

Roland Gavrilescu • November 24, 2025

Legacy software, for all its complexity, usually failed in ways you could trace. Something broke, you reproduced it, you patched it, and the system went back to steady state.

AI systems don't behave that way. They don't fail like software; they fail like organisms. They get confused without signaling it, degrade at different context lengths, and suffer from harness design flaws in ways that are hard to diagnose.

We believe learning shouldn't stop at the model weights; it should be built into how AI systems self-improve with feedback from production.

As AI roadmaps go live, teams are discovering the hardest work starts after launch. New models require continuous tuning, integrations introduce subtle quirks, and edge cases appear faster than anyone can anticipate. We've lived this firsthand while scaling AI agents at Superhuman and xAI: late nights debugging model behavior, implementing hotfixes in prod, and watching initial evals fall behind.

Reliability in AI isn't just about metrics and dashboards; it's about steering a non-deterministic system that must adapt to new models, edge cases, and constraints over time. Yet most AI infrastructure is still designed for a static SDLC.

Adaptation as Infrastructure

AI-native software needs AI-native infrastructure, one that doesn't just observe deployments but participates in them. AI systems should be able to study their own traces, learn from real-world feedback, and update how they work without requiring a human-in-the-loop for every new edge case.

At Nuraline, we're building the missing adaptation layer for AI-native software — a system that converts real-world feedback into new evals and architectural changes, a forward-deployed AI agent that reasons over your code, telemetry, and real user interactions to continuously improve the reliability and capabilities of the system end-to-end. We're closing the feedback loop by:

  • Surfacing failure modes from user feedback, tool-use errors, reasoning traces, task outcomes, and interaction logs — tying together what users say what actually happened in the traces.
  • Diagnosing issues at the system-level: orchestration, integrations, tools, prompts, models, and upstream data sources to identify true root causes.
  • Expanding evaluations with new failure cases as they happen, co-designed with experts in the loop as needed.
  • Proposing improvements across the architecture, grounded in production data, updated evaluations, and emerging best practices.

Nuraline acts as the proactive layer in the AI stack, one that turns the constant stream of real-world feedback into a continuous adaptation loop. Instead of relying on ad hoc reviews and hot fixes, your AI system can learn from every mistake and improve its reliability over time.

The Decade of Agents

Model capabilities are improving faster than most organizations can adopt them. On paper we have systems that can reason at a human level. In practice, they are held back by flawed implementations, underwhelming observability, and high maintenance costs.

We believe an adaptation layer will become standard for every agent deployed in the enterprise. The next decade of agents won't just be about better models — it will be about systems that can understand their own mistakes, write their own evaluations, and update their architectures from real-world feedback. This new layer in the AI stack turns user feedback and emerging best practices into new evaluations, improved architectures, and continuous reliability in production.

We're currently running a closed beta with teams operating at the frontier of agentic capabilities. If you're running AI in production and living this pain, we'd love to hear from you.