Sigilix
AI models that learn your codebase and remembers you
14 followers
AI models that learn your codebase and remembers you
14 followers
Our models ground themselves in your org — an index of your repositories, code and dependency graph, and your team's accumulated review history and decisions. That shared understanding drives every surface: a terminal coding agent that works with your project's real context instead of guessing, deep-research chat over your code, your connected apps, and the live web, code review on pull/merge requests, and agents in Slack and Linear that answer and act with the same knowledge.
This is the 2nd launch from Sigilix. View more
Sigilix
Launching today
Our models build a persistent understanding of each company’s architecture, terminology, engineering conventions, and developer workflows using context from repositories, pull requests, Slack conversations, issue triage, and developer activity. Unlike existing products that treat every prompt or code review as a new interaction, Sigilix carries that organizational knowledge across its CLI, code review, Slack, and workflow tools.
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Launch Team / Built With
Hey Product Hunt,
Most AI companies are building products on top of the same handful of general-purpose models.
We decided to build the models instead.
Sigilix develops and serves its own model family—starting with Boreas, followed by Pyroeis and Astraeus—built specifically for software engineering, organizational knowledge, and long-term context.
The core idea is simple: an AI system should become more useful the more your organization uses it.
Every Sigilix tool feeds the same shared intelligence layer. The CLI, repository tools, pull request workflows, agents, integrations, developer feedback, and team interactions all contribute context to the models.
When an engineer corrects a result, the system learns. When a team establishes a convention, that knowledge can carry into future sessions. When Sigilix discovers how a repository is structured, how services interact, or how an organization prefers to solve problems, that understanding becomes available across the entire product.
The tools are not separate AI features with separate memories. They are interfaces into the same model and memory system.
That means knowledge gained in one place can improve reasoning everywhere else. A decision made during development can inform a later agent task. Feedback from one engineer can improve future outputs for the organization. Repository history, code structure, workflows, preferences, and prior outcomes all become part of a persistent organizational context.
We believe this is the difference between temporarily prompting an AI and actually building intelligence for a company.
Owning the model layer allows us to control how the models reason, retrieve context, verify their work, use tools, and learn from each organization. We are not limited to wrapping another provider’s API or resetting the system every session.
Code review is one place where the models can be used, but it is not the company. The reviewer, CLI, agents, integrations, and future products all exist to strengthen the same underlying models.
Sigilix is building models that learn how your organization works.
Try the models, connect your tools, and tell us what they understand, what they miss, and what they should learn next.
A "show your work" toggle on Slack and Linear answers would be huge, so we can see exactly which repos or past review threads the agent pulled from before trusting the response.