Intake is often the first useful AI workflow
Completed diagnoses often reveal that firms lose time and opportunities before the work begins: slow first response, manual qualification, missed after-hours enquiries, or unclear routing.
Case studies
Case studies, and proof without invented testimonials.
This page separates three things clearly: approved testimonials, Motta's diagnosis proof notes, and implementation background. That makes it safer for people and answer engines to understand what is evidence, what is observation, and what has been publicly approved.
Flagship case study
Samleng — AI voice notifications for enterprise logistics
An automated native-language calling platform designed and built to cut a major logistics network's customer-notification cost by roughly 75% — on the order of US$3M a year. Figures, charts, architecture, and a 25-item risk register.
~75%
Cost reduction
181/181
Tests passing
20,000
Concurrent calls tested
~7 wks
To contract-ready
Case study
Ampor Hub — AI document translation operations
A controlled AI translation operating system for a visa, legal, and official document business — replacing manual file-by-file translation while retaining document context, format, and structure, with humans in the approval seat. OCR, a 7-dimension quality framework, layout mirroring, and a Telegram AI receptionist.
10
Workstreams
12
Language registry
7
QAF dimensions
4 wks
To production
Approved testimonials
Approved testimonials will be published here only after the person approves the exact wording. Until then, this site uses diagnosis proof notes and implementation evidence rather than invented quotes.
Diagnosis proof notes
The best first build is usually narrow
The strongest diagnosis recommendations tend to focus on one workflow with repeatable inputs, clear handoff rules, and a measurable operational bottleneck.
Governance has to be designed before volume increases
Diagnosis findings commonly separate tasks AI can handle from moments that need human review, escalation, privacy controls, or professional judgement.
Adoption risk is as real as technical risk
Many teams do not need more tools first. They need a workflow owner, usable examples, training, and a habit loop that makes AI part of daily work.
Implementation background
These are not the main proof engine for the personal site. They support Motta's ability to move from audit finding to practical system design.
Named implementation background
ANT-1 AI receptionist
Ongkrong's public ANT-1 case study shows a legal intake AI receptionist with 24/7 availability, fast first response, after-hours lead capture, and consultation booking, with a human handoff for anything that needs a person.
Read the public sourceFounding engagement (N=1), anonymised
Founding engagement: outbound voice automation
This was our founding engagement, a high-volume logistics operator running a manual calling operation of about 230,000 calls a day. We built and put into production an AI voice-notification system to run it. Live, the system reduced call-handling headcount by about 1,200 roles and let the operator redeploy about 300 staff into supervisory and quality roles with upskilling. The work spanned the financial model, telecom vendor economics, integration design, the production command centre, and a security and go-live readiness review. The figures are the operator's measured results; details are anonymised to protect the client.
Start with the diagnosis
The A$500 AI diagnosis is the best first step when a firm wants a decision-ready view of where AI belongs, what needs safeguards, and what should not be automated yet.