Models built for code. Grounded in your reality.
Raw scale loses to context. Every Sigilix model is grounded in your codebase graph and sharpened by your team's memory. A smaller, grounded model beats a bigger, blind one on your code—every time.
Generic models guess. Sigilix models know.
Before a single token is generated, every Sigilix model reads your codebase graph and loads your team's learned memory. Org memory is what the whole team has taught it — every dismissed finding and every fix, shared across your repos and used on every review. Seat memory is your own, carried through your CLI sessions. Both are loaded before the first token, so the model starts from what your team already knows, not a blank slate — and the more you use it, the sharper it gets. That is why a Sigilix model out-performs a generic bigger one on YOUR code.
Every model reads the callers, imports, and dependents before it reasons — the same code graph the review pipeline builds and keeps current.
Org and seat memory accumulate with every dismissal and every fix. What your team already taught it is loaded before the first token.
The synthesis of graph and memory — code-aware answers instead of a plausible-sounding guess about a generic repo.
Boreas
The everyday model.
Boreas is the model behind the everyday loop — fast enough to sit behind every keystroke in the CLI and every pull-request review without making you wait. It stays sharp by leaning on your codebase, not raw scale: every answer is grounded in your repo's graph and your team's memory, drawn from your real architecture instead of a guess. 'Light' means low-latency, never less capable — it keeps the routine instant and the fundamentals right.
Pyroeis
The balanced model.
When a task needs more than a fast pass — multi-file changes, agentic steps, deeper refactors — Pyroeis brings heavier reasoning while staying fully grounded in your architecture and your team's accumulated memory. It's the tier for work that has to hold more of the codebase in mind at once, and act on it, without ever losing the thread of how your repo actually fits together.
Astraeus
The deepest-reasoning model.
Astraeus is the tier for the changes that carry the most risk. It spends maximum reasoning on the hardest problems — subtle logic, security, cross-file blast-radius — with total codebase grounding and your org's memory behind it. It surfaces the edge cases and architectural violations that a faster pass, or a bigger blind model, would miss: when a bug hides in the interaction between three services, it has the context to connect the dots.
Phanes
The next model in the line.
A new Sigilix model, arriving soon. Built on the same foundation — codebase grounding and learned memory — to push further into autonomous engineering.
One moat. Four models.
Grounding and memory aren't features. They are the foundation. You pick the tier of reasoning you need; the context is constant. Step into the private beta.
JOIN THE PRIVATE BETA