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.

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.

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.

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.

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.