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How Digibee merged 191 PRs in 2 months, without a single production defect using Entelligence AI

Caique Mariño

May 6, 2025

4 min read

191
PRs reviewed
164
Issues caught (86% hit-rate)
0
Escaped defects
110+
Engineering hours saved
22
Repos auto-documented
About Digibee

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 logo
Caique Mariño
Principal Software Engineer, Digibee
The Problem

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:

  • Unawaited calls and thread leaks: leftover goroutines and open database handles slowly exhausted resources during peak traffic
  • Faulty CI YAML: one misplaced indent in the GitLab pipeline file stopped every deployment until someone noticed
  • Type mismatches and missing nil checks: code that compiled cleanly still crashed in production when unexpected values slipped through
  • Stale documentation: new hires spent hours jumping between services to understand data flows that were never captured in a single source of truth

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 logo
Caique Mariño
Principal Software Engineer, Digibee
The Solution

Digibee integrated Entelligence across every GitLab repository in under ten minutes.

A single App install brought three capabilities online the same day:

  • Automated, line-level reviews on every merge request
  • AI-generated documentation that refreshes on each commit
  • A Slack code-chat bot that answers repository questions in real time

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."
Digibee logo
Caique Mariño
Principal Software Engineer, Digibee
Outcome

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.

MetricValueImpact
Merge requests reviewed191Full coverage across all services
Substantive issues flagged164 (86% hit-rate)High signal, minimal false positives
Production incidents0Reliability maintained at release pace
Engineering hours saved≈ 110 hLess manual review and post-mortem debugging
Repositories documented22Onboarding time reduced
Slack bot usage2,210 queries answeredFewer 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.

Looking Forward

To keep pace as the platform grows, Digibee will enable three additional Entelligence modules:

1. AI Documentation

Cross-repository references let engineers jump straight from a service call to the source implementation.

2. Team Insights dashboards

Live metrics on review latency, comment acceptance, and ownership drift help leads rebalance workload before bottlenecks form.

3. Code Overview heat maps

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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