Archynt vs CAST Imaging

CAST has been mapping software architecture from source code for longer than most of the AI-native tools in this space have existed, and CAST Imaging's coverage — over 150 languages and frameworks, including plenty of genuinely old enterprise stacks — is not something we're going to claim to match. If an organization's real problem is a 20-year-old COBOL-to-Java-to-whatever estate that nobody fully understands anymore, CAST has almost certainly already built the extractor for whatever's in there. That is a hard, unglamorous, valuable thing to have built, and it's a legitimately different scale of static analysis than what Archynt does today.

What that graph is missing

CAST Imaging's model, as far as we've been able to verify from what's publicly documented, is static: it maps what the code says, including hidden links between code and data most engineers don't know exist. It doesn't observe a running system. That means it can tell you what should be connected to what, but not whether that connection is still real, whether something new got wired up outside of what the static model captured, or whether a documented dependency is actually dead. In our experience that runtime side is where a surprising share of the risk sits, precisely because it's invisible to any tool that only reads source.

Archynt's agent watches live TCP connections, container topology and — through an eBPF sensor — real request/error/latency rates, with zero application instrumentation, and tags every dependency edge by whether it was seen in code, in traffic, or both. That third category, the mismatch between the two, is the whole point for us.

One place we're genuinely tied, not ahead

CAST already exposes its knowledge graph to AI agents through an MCP server, which puts it in the same small group as us on that front rather than behind. If agent-navigable architecture context is what you're evaluating for, both products can serve that need today — the deciding factor is more likely to be whether you need CAST's technology breadth on a legacy estate, or Archynt's runtime correlation on a live, actively-deployed system.

Who should pick which

A large enterprise doing a multi-year legacy modernization across dozens of unusual, older technologies is better served by CAST's coverage than by us — we'd say that to a prospect directly. A team running a live, actively-deployed system in Java/Spring, Node, Python or Go, where the open question is whether production still matches the code, is the case Archynt was built for.