Independent Analysts Say We Are Solving the Right Problem

Most of the AI coding tools market has been racing toward faster code generation. Our launch of Cielara Code reframes the conversation entirely. The binding constraint on AI coding agent effectiveness is not how fast code gets written. It is whether the agent can find the right place to write it in the first place.

Our research found that 56.8% of agent actions in real coding sessions were file reads, and another 24.2% were grep searches. Less than 1% were actual edits. When a fix required changes across more than six files, compute in failed attempts increased by a factor of four. At enterprise scale, that is not a performance issue, it is a direct infrastructure cost that compounds every sprint.

Cielara Code addresses this by building a Code Dependency Causal Graph of the entire codebase before the agent touches anything, encoding what code does, why it exists, who owns it, and how it behaves at runtime. The result is an agent that navigates like a senior engineer who already knows the system. On independent benchmarks it outperformed both Claude Code and OpenAI Codex on localization accuracy, ran 10% faster per task, and used 30 to 40% fewer tokens. We are already running with 11 Fortune 100 and over 40 Fortune 500 companies.

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