About Sybill

Sybill is a conversation intelligence platform that transcribes sales calls, detects buyer sentiment, and auto-generates follow-up actions, all streamed back into the team's CRM so reps can focus on closing deals.

The Problem

To keep shipping weekly without outages, Sybill needed a fail-safe way to review every pull request across its Python / TypeScript services

Sybill's product roadmap moves fast, with new features going live each week. Every release, however, widened the surface for regressions:

• Un-awaited async calls – leaving connections open and exhausting resources under load

• Misconfigured CI pipelines – a single indentation error in GitHub Actions could halt every deployment

• Type inconsistencies and logic errors – wrong return signatures or misplaced workflow registrations caused runtime failures that unit tests missed

Catching these issues manually was consuming 5–6 engineering hours per day, yet defects still slipped through and triggered late-night patches. As a five-engineer team, Sybill couldn't keep adding review rounds without stalling feature work.

The Solution

Sybill integrated Entelligence to automate all PR reviews in one day

When Sybill evaluated Entelligence, the decision was simple. The engineering team needed two guarantees:

• Drop-in setup. The reviewer had to plug into existing GitHub workflows without extra CI work or new tooling to learn.

• Signal, not noise. Comments must highlight real defects, resource leaks, type mismatches, CI mis-configs so developers trust and act on them.

Entelligence met both requirements:

• Entelligence was installed and active in under 10 minutes

• First run analysis flagged issues static linters had missed, proving its selectivity

By the end of day one, every repository was covered. From that point on, 100% of pull requests received Entelligence reviews, giving Sybill continuous, automated scrutiny without adding review hours.

Outcome

Thanks to Entelligence, Sybill prevented 5 production-level bugs and saved ≈ 350 engineering hours while maintaining a 73% developer approval rate

The impact showed up immediately. During the eight-week trial, Entelligence flagged issues that would have caused connection leaks, CI pipeline failures, and runtime crashes—none of them reached production. Avoiding those incidents and the follow-up debugging cycles freed roughly 350 engineer hours, time the team redirected to feature work on Sybill's call-analysis platform.

Review throughput improved as well. Median PR turnaround dropped from 5h 12m to 1h 47m once automated comments handled first-pass checks. Because Entelligence's feedback was targeted, developers reacted positively→55 👍 vs →11 👎 building trust in the system and keeping manual reviews focused on design rather than syntax or hygiene.

With incidents at zero and review latency cut nearly in half, Sybill can now release weekly with confidence that small oversights won't escalate into late night pages.

Results That Matter

• Zero critical issues reached production during the trial

• Engineers reclaimed hours previously lost to post-merge firefighting

• Review quality improved without adding headcount—developers called Entelligence "another set of senior eyes"

What's Next

Looking ahead, Sybill will turn on three additional Entelligence modules to keep pace as the codebase and team grow:

1. AI Documentation – auto-generated, searchable docs so new engineers can get context and start contributing on day one

2. Team Insights – live metrics on review latency, comment acceptance, and ownership drift to help leads balance workload and catch process slow-downs early

3. Code Overview – file-level risk heat-maps that highlight fragile areas before they cause incidents

Together, these add a continuous feedback layer—spanning onboarding, day-to-day engineering, and management visibility—that lets Sybill maintain release speed without compromising reliability.