Case studies.
A stack of point tools sees one slice of the work each, so teams re-explain the same context at every step. These case studies show what changes when one org-aware platform runs review, triage, and repair on a shared memory index: fewer repeated tokens, fewer vendors, answers closer to the repo.
TL;DRHow one org-aware platform on a shared memory index changes review, triage, and repair in real workflows.
How Sigilix fits real engineering workflows
Token reduction through memory
How codebase context and vectorized workspace memory reduce repeated context by 63% across review, triage, Slack, Linear, and PR workflows.
Review accuracy at scale
How Sigilix reports recall, precision, F1, and false positives with the fixture boundaries visible.
Apps that keep engineering moving
How Sigilix apps fit across GitHub, Linear, Slack, CLI, and chat without asking teams to rebuild the way they already work.
Frequently asked questions
- What do the Sigilix case studies show?
- How one org-aware platform runs review, triage, and repair on a shared memory index in real workflows: grounded review accuracy on disclosed fixtures, and a tested 63% reduction in repeated context compared with rehydrating the same background from scratch.
- Is the 63% figure a claim about every request?
- No. It applies to repeated background such as repository conventions, prior comments, and workflow context, not to the current diff or the evidence needed to answer safely. Actual usage still depends on the task, plan, and how much useful memory exists.
- Are the benchmarks independent?
- The review-accuracy figures are measured on our own disclosed fixtures, and we label them as such rather than presenting them as a neutral third-party leaderboard. The point is to show how the system behaves, with the sourcing kept explicit.