Success Stories

Proven enterprise deployments delivering measurable operational excellence.

Lark Enterprise System

PT Anugrah Alam Mulya

End-to-End Digital Transformation

Read the full case study

Project Snapshot

Platform

Lark Enterprise

Time to Deployment

3 Weeks

Efficiency Gained

40+ Hours/Week

Want similar results?

Start a Consultation

The Challenge

PT Anugrah Alam Mulya was previously relying on Google Sheets which became increasingly messy and disorganized. The owner considered using Notion but realized it lacked the deep integration required. They needed a system where every new project detail could be seen in one single page view, without scattered files.

The Solution

Botkilat provided a robust Lark Suite solution. We established a "Project Base" where every project detail lives on one page. Crucially, we automated file organization: when income or expense receipts are attached in the Base, automation creates a new folder in Lark Drive using the Project Name, and sorts the files into "Account Payable" or "Account Receivable" subfolders via the Lark API.

Unified Base
Finance
HR Core
Inventory
Approvals

Efficiency Gain

83%

Example Workflow

Project Created in Base -> Automation creates Drive Folder Structure -> Receipt Attached in Base -> API moves file to "Account Payable" folder -> Drive Link updated in Base.

Business Impact

Administrative chaos was completely eliminated. The owner now has total visibility over project status and financial documents in one integrated view. File retrieval time dropped from hours to seconds due to automated archiving.

Next Steps: Module expansion to fleet management and warehouse IoT integration.

This system is not just an admin tool, it is our new operational backbone. We moved from guessing to knowing.

Budi Santoso, Operations Director
Big Flow Automation + AI

Duta Cemerlang Motor

Multi-Branch AI Compliance System

Read the full case study

Project Snapshot

Platform

n8n + AppSheet

Time to Deployment

1.5 Months

Efficiency Gained

2 Hours/Day per Branch

Want similar results?

Start a Consultation

The Challenge

With 23 branches, Duta Cemerlang Motor used AppSheet for inspections, but the data was not integrated in real-time. Central management had to wait for inspectors to manually report findings, leading to delays in critical issue resolution.

The Solution

We connected AppSheet directly to n8n using Webhooks. The moment an inspector finishes an inspection in AppSheet, the data payload is sent to n8n. The workflow processes the data, logs it to the central database, and triggers immediate alerts for failed checks.

AppSheet Input
n8n Processing
System Alert

Inspection Report Received

98%

Example Workflow

Inspector Submits in AppSheet -> Webhook Triggers n8n -> Parse JSON Data -> Log to Central DB -> IF [Critical Failure] THEN -> Send WhatsApp Alert to Regional Manager.

Business Impact

Standard compliance rose sharply to 98% in the first month. Central managers get real-time incident reports without waiting for daily manual recaps.

Next Steps: Implementation of customer queue detection for service optimization.

Automation transformed how we monitor branches. Inspection data is now alive and actionable the second it happens.

Hartono, Regional Manager
AI OCR + Python Automation

PT Prakarsa Mitra Andalan

OCR Automation & Vehicle Document Processing

Read the full case study

Project Snapshot

Platform

Lark + n8n + Python

Time to Deployment

4 Weeks

Efficiency Gained

6 Hours/Day

Want similar results?

Start a Consultation

The Challenge

The team had to manually read scanned vehicle documents, rename files to match document numbers, and compress each file one by one. It was slow, error-prone, and delayed document submission.

The Solution

Botkilat built an OCR-first automation: key text is extracted from scans, filenames are auto-renamed using detected numbers, critical fields are pushed into a sheet, and a Python job performs bulk rename and resize without manual intervention.

Upload Documents

Process time <5 minutes/file

OCR & Extraction

OCR Accuracy >95%

Rename & Sheet

Auto Resize

Process time <5 minutes/file

OCR Accuracy >95%

Example Workflow

Upload documents to folder -> OCR extracts fields (chassis number, plate number, owner) -> n8n renames files based on detected number -> Data sent to Sheet -> Python job batch-resizes and compresses files.

Business Impact

Document prep time dropped from hours to minutes. Filename accuracy improved because it follows OCR data, and the team only needs to verify the sheet output.

Next Steps: Cross-check with Samsat database and automated client notifications.

No more guessing or retyping. Files come in clean, correctly named, and already optimized in size.

Andi, Operations Lead