Models for every depth of engineering work.
Sigilix routes the right model around your codebase, memory, and workflow context. The difference is not just what the model knows in general. It is what it can keep learning about how your engineering system actually works.
Light, base, premium, and frontier paths share the same product thesis: your repo graph, review history, Slack decisions, Linear context, and CLI sessions should make the model better at helping you without forcing you to repeat the same context every time.
Boreas
Fast code work that still understands the repo around it.
Boreas is the light route for quick review, CLI help, and small code changes where speed matters but generic answers are not enough. It is trained around codebase context and memory standards, so everyday interactions across your tools can help it remember how your work is shaped without treating each request like a blank prompt.
Best for fast review loops, local CLI questions, routine bug triage, and small edits that need codebase grounding.
Uses memory to preserve recurring patterns, naming decisions, common failure modes, and the habits your team keeps reinforcing.
The Boreas page covers its flash-tier comparisons, effort points, and memory-driven coding benchmark framing.
Pyroeis
The base route for everyday engineering work.
Pyroeis is the default model for most product teams: grounded enough for multi-file work, fast enough for daily use, and broad enough for the small tasks that keep engineering moving. It carries repository context, user memory, and workflow history into the answer so you can ask for help without restating how everything fits together.
Best for everyday implementation, review follow-up, small refactors, product questions, and work that spans a few files.
Designed for users who need a steady model more often than a frontier one: reliable, practical, and memory-aware.
Its section links directly into the Pyroeis release notes, benchmark framing, and deeper model context.
Astraeus
Repository-scale reasoning for higher-risk work.
Astraeus is the premium route for the work where the cost of missing context is highest: security changes, architecture shifts, complex repairs, and long-running investigations. It keeps PR history, issue trails, CLI sessions, and team decisions available as memory so the model can reason with the system you actually have.
Best for hard bugs, cross-service changes, security-sensitive reviews, and repair loops where the answer depends on history.
The goal is high-effort reasoning that can see the context behind the task, not a detached score chase.
The Astraeus page covers benchmark framing, token-curve context, and the model release story.
Phanes
The private path for guardrails and memory integrity.
Phanes stays private while the release criteria mature around disclosure, provenance, data integrity, and guardrails. The hard part is not only making a stronger memory-native model. It is making sure the model explains when memory shaped an answer, keeps user-visible evidence honest, and never hides context simply because it can.
Best understood as a guardrail and release-readiness case study, not a public model tier you should route normal work through today.
Focus areas include memory disclosure, data boundaries, provenance, and the difference between helpful context and context that should be surfaced.
The Phanes announcement explains why guardrail and data-integrity work happens before a broader release.
One model line. One memory layer.
Sigilix creates models, review surfaces, CLI workflows, and memory-aware tools around the same engineering context. You choose the reasoning depth for the job; the product keeps the context consistent.
That is the difference between asking a model to guess and asking a system to remember how your codebase works.