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Sigilix
Let AI map your codebase first for cheaper safer coding tool
Sigilix indexes a company’s codebase once, then continuously learns from every PR review, CLI session, chatbot conversation, bug fix, and developer correction. That persistent memory powers Empyrean, our AI engineering layer, and Boreas, our first codebase-aware model. The result is AI code review and coding agents that are cheaper, more accurate, and easier to trust.
Hey everyone — Daniel here, founder of Sigilix.
We built Sigilix because AI is making it easier to write code, but harder to know what code is actually safe to ship.
Most AI coding tools start from the prompt. Sigilix starts from the repo.
When a team installs Sigilix on GitHub, we index the codebase and build a private memory layer from PR reviews, developer feedback, CLI sessions, repo chat, and bug fixes. That memory then powers our PR reviewer, CLI agent, chat, triage workflows, and our Empyrean model line, starting with Boreas.
The goal is simple: help engineering teams trust AI-assisted software development by grounding every review, fix, and decision in real codebase context.
We’re currently looking for design partners who want to test Sigilix on real workflows, especially teams dealing with noisy PR review, expensive AI coding sessions, or tools that forget too much context.
Would love feedback, questions, or intros to engineering teams that feel this pain.
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About Sigilix on Product Hunt
“Let AI map your codebase first for cheaper safer coding tool”
Sigilix was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #90 on the daily leaderboard. Sigilix indexes a company’s codebase once, then continuously learns from every PR review, CLI session, chatbot conversation, bug fix, and developer correction. That persistent memory powers Empyrean, our AI engineering layer, and Boreas, our first codebase-aware model. The result is AI code review and coding agents that are cheaper, more accurate, and easier to trust.
Sigilix was featured in Developer Tools (515k followers), Artificial Intelligence (472.4k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 201.5k products, making this a competitive space to launch in.
Who hunted Sigilix?
Sigilix was hunted by Daniel A Martinez Julio. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Sigilix stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Daniel A Martinez Julio