A scanner matches patterns. It has no idea what your code is trying to do, which is why it misses the broken authorization check and flags the string concatenation that was never reachable. Reading intent is the whole job, and until recently it was the part only a person could do.
CodeSightAI is the engine we built to do that work at scale. It uses a language model's reasoning to understand how data moves through your codebase - what a function intends, where the trust boundaries actually sit, which entry points reach which sinks - and then holds that reasoning to account.
Every finding has to carry written evidence: source location, CWE, OWASP category, a CVSS vector, the MITRE ATT&CK technique it would enable, and a narrative explaining how it would actually be exploited. No finding is marked complete until that evidence exists and passes an audit gate.
It runs entirely on our own machines, on a model backend we control, and your source never leaves that boundary. Everything the tool reads from a repository is treated as data rather than instruction, so a file that tries to steer the reviewer gets flagged instead of obeyed.
Today we run CodeSightAI as part of our own engagements rather than selling it. We are weighing up licensing it to teams who want to run it in-house - if that would be useful to you, tell us and it will inform where this goes.
A pattern scanner
Sees syntax.
CodeSightAI
Sees intent - the flow, the function, and what the author meant the code to do.
