Atlarix
Freemium
Engineering teams needing private/local AI coding assistance
Most automated code review tools post a wall of model-generated comments that reviewers learn to ignore — or worse, suppress real bugs to keep the output tidy. Sigilix is built around the opposite bet: a five-stage pipeline where a finding cannot post unless it cites real evidence and survives execution.
$30/seat/mo + usage · Free tier: 1 repository, capped monthly usage, PR review (full pantheon), CI-failure triage, community Discord · Checked Jul 1, 2026
Each pull request triggers five specialist agents — Metis, Argus, Iris, Eunomia, and a synthesizer named Harmonia — running in parallel, then collapsing into a single structured comment. Every finding carries a proof tier: VERIFIED, GROUNDED, or MODEL, so you know exactly how much to trust it before you act. The output is a ranked file list by risk, an effort estimate, and inline patches ready to apply. The architecture runs as a hosted GitHub App with no self-hosted option, which caps teams that need code to stay inside their own network. It is in private beta, so production reliability data from external teams is not yet available.
Bottom line: Sigilix is the right bet for a team tired of pruning hallucinated review noise from generic LLM tools — but teams with air-gapped repos or hard data-residency requirements will hit a wall immediately, since the vendor currently offers no self-hosted runner.
For solo repos & trying it out
Triage, review, CLI & chat
More usage + frontier models
Largest windows, org controls
Usage scales to your org and needs u2014 contractual, with on-prem / VPC / custom MSA available
View full pricing on sigilix.ai →
Pricing may have changed since last verified. Check the official site for current plans.
Find your fit
More options in Coding Assistants. Compare pricing, platforms, and features side by side.
Freemium
Engineering teams needing private/local AI coding assistance
Freemium
Developers preferring fully local agent execution
PR review tools built on a single model-to-comment loop produce findings that all look equally authoritative, whether they’re grounded in the actual diff or hallucinated from training data. Sigilix addresses this with a five-stage pipeline: deterministic scans and AST analysis run before any model speaks, specialist agents interpret that evidence by citing the manifest rather than reasoning from scratch, provenance contracts and live execution gates drop any finding that cannot be cited, and only what survives posts — tagged VERIFIED, GROUNDED, or MODEL so the reader always knows the proof tier.
The differentiating mechanic is the proof tier attached to every inline comment. A VERIFIED finding survived execution. A GROUNDED finding cites a specific manifest or scan result. A MODEL finding is model-generated without external corroboration — and the system surfaces that distinction rather than hiding it. The vendor states the hallucinated-finding count is zero by construction: uncited findings are dropped before posting, not filtered after. Every dismissal a team records trains a per-repo trust ledger, so the system adjusts to your codebase’s risk profile over time.
Sigilix fits teams that have already concluded that generic LLM review noise costs more to triage than it saves. The Linear integration handles issue triage, and the CLI covers chat-based codebase research and local coding assistance. The hosted GitHub App model means setup is minimal — reviews fire automatically on every PR. The constraint is equally clear: there is no self-hosted option. Teams with data-residency requirements, air-gapped infrastructure, or contracts that prohibit sending code to third-party services cannot use it in its current form. The tool is in private beta, so external production-scale evidence is limited.
For enterprises, the vendor describes audit logs and SSO as available capabilities. The bring-your-own-model option is listed as a supported path, which means teams that need to route through their own API keys or swap providers can do so without being locked to Sigilix’s default model choices.
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