Caique Mariño
May 6, 2025
4 min read
Digibee is an AI-native integration-platform-as-a-service (iPaaS) that lets engineering teams link applications, data, and AI workloads without the heavyweight cost of legacy vendors.
"Entelligence latched onto our GitLab projects in minutes and started flagging the edge-case errors we'd normally catch only post-release."
Digibee needed reliable, automated reviews and up-to-date docs for more than twenty microservices that deploy many times a day.
Every GitLab merge request can modify Java, Go, and TypeScript code across several repositories. That velocity surfaced four recurring pain points:
Manual review cycles consumed about twenty minutes per merge request yet still let edge cases through, while senior engineers lost focus answering architecture questions.
"We spent more time chasing review oversights and explaining internals than building new pipelines. We needed a reviewer and living docs we could trust."
Digibee integrated Entelligence across every GitLab repository in under ten minutes.
A single App install brought three capabilities online the same day:
No pipeline files were touched and developers kept their existing workflow.
Once enabled, every new merge request received Entelligence scrutiny and every service gained living documentation. The Slack-based code chat eliminated ad-hoc "where does this function live" pings, letting senior engineers stay focused on feature work.
"Entelligence plugged in and started flagging race conditions and CI blockers on the first scan. The auto-docs quickly became our go-to reference."
Entelligence inspected 191 merge requests, caught 164 real defects, and saved about 110 engineering hours, with zero production incidents.
During the eight-week trial Entelligence surfaced issues that would have leaked threads, blocked pipelines, or triggered runtime panics. Fixing them pre-merge eliminated late-night patch cycles and freed the team to focus on new pipeline features.
Metric | Value | Impact |
---|---|---|
Merge requests reviewed | 191 | Full coverage across all services |
Substantive issues flagged | 164 (86% hit-rate) | High signal, minimal false positives |
Production incidents | 0 | Reliability maintained at release pace |
Engineering hours saved | ≈ 110 h | Less manual review and post-mortem debugging |
Repositories documented | 22 | Onboarding time reduced |
Slack bot usage | 2,210 queries answered | Fewer context-switch interruptions |
With critical defects caught early and living docs in place, Digibee's engineers now ship changes multiple times a day, confident that small oversights won't escalate into production outages.
To keep pace as the platform grows, Digibee will enable three additional Entelligence modules:
Cross-repository references let engineers jump straight from a service call to the source implementation.
Live metrics on review latency, comment acceptance, and ownership drift help leads rebalance workload before bottlenecks form.
File-level risk scoring highlights fragile areas so refactors happen before incidents occur.
These capabilities add a continuous feedback layer that spans onboarding, day-to-day development, and engineering management, allowing Digibee to maintain release speed without sacrificing reliability.
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