Client Experiences

Organizations across Thailand share their experiences working with Synth Orbit on AI initiatives.

What Clients Say

Authentic feedback from organizations we've partnered with.

Praew Chaiyarat

Head of Analytics, FinTech Startup

Bangkok

The team's approach to our predictive analytics project was exactly what we needed. They took time to understand our business context and delivered models we could actually explain to stakeholders. The two months of follow-up support helped us integrate the forecasting system smoothly.

January 15, 2026

Sompong Thanarat

CTO, E-commerce Platform

Bangkok

We engaged Synth Orbit for NLP work on our customer support system. Their expertise with Thai language processing was evident throughout. The sentiment analysis they built handles the nuances of Thai text better than anything we'd tried before. Documentation was thorough enough that our team could maintain and retrain the models.

January 22, 2026

Niran Kraisiri

Director of Operations, Manufacturing

Chonburi

The data strategy engagement was invaluable. We'd been collecting data for years but had no clear plan for using it. Their assessment identified immediate opportunities while laying groundwork for future initiatives. The architecture they proposed was practical rather than aspirational, which our IT team appreciated.

December 28, 2025

Areeya Saengtong

VP Marketing, Retail Chain

Bangkok

What stood out was their patience in explaining technical concepts to non-technical stakeholders. The customer churn model they built for us works well, and the dashboard makes it easy to track predictions. They were honest about limitations rather than overselling capabilities, which built trust.

January 8, 2026

Wichai Mongkol

Chief Data Officer, Insurance Company

Bangkok

We've worked with several AI consultancies, and Synth Orbit's collaborative approach was refreshing. They genuinely wanted our internal team to understand the systems being built. The knowledge transfer was comprehensive, and they remained available for questions after the engagement concluded. Would work with them again.

January 18, 2026

Kannika Virojpibul

Product Manager, SaaS Provider

Bangkok

The NLP pipeline they developed for our document processing workflow exceeded our expectations. What particularly impressed us was how they adapted the system to handle both Thai and English documents seamlessly. The API integration was clean and well-documented, making it easy for our development team to work with.

January 5, 2026

Success Stories

Detailed accounts of how AI initiatives delivered measurable value.

Challenge

A regional logistics company struggled with inventory forecasting across their distribution network. Manual estimates often missed actual demand, leading to stockouts in some locations while others held excess inventory. They needed a more systematic approach to allocation decisions.

Solution

We developed predictive models for each distribution center, incorporating historical demand patterns, seasonal factors, and regional events. The system provided weekly forecasts with confidence intervals, allowing the operations team to make informed stocking decisions while understanding uncertainty levels.

Results

Over the three months following implementation, stockouts decreased by 34% while excess inventory dropped 28%. The forecasting dashboard became a central tool for weekly planning meetings. Operations staff reported greater confidence in allocation decisions.

Timeline: 11 weeks from start to production deployment

Challenge

A financial services firm received thousands of customer inquiries monthly through various channels. Manual categorization consumed significant analyst time, and routing to appropriate specialists often involved multiple handoffs. They sought to streamline the initial triage process.

Solution

We built an NLP classification system that analyzed incoming inquiries and assigned them to appropriate categories with confidence scores. The system handled both Thai and English text, learning from the firm's historical categorization decisions to match their internal taxonomy.

Results

Classification accuracy reached 87% on validation data, with the system flagging low-confidence cases for manual review. Processing time for initial triage dropped from an average of 15 minutes per inquiry to under 2 minutes, freeing analyst time for more complex cases.

Timeline: 14 weeks from discovery to production

Challenge

A healthcare technology company had accumulated years of patient data across disconnected systems with inconsistent formats and naming conventions. They wanted to prepare this data for future analytical work but faced significant integration challenges.

Solution

Our data strategy engagement mapped their current landscape, identified critical data quality issues, and proposed a phased architecture for consolidation. We developed governance protocols ensuring consistent data handling going forward while outlining a realistic remediation plan for legacy systems.

Results

The company now has a clear roadmap for data infrastructure improvement. They've begun implementing the first phase of consolidation and report better cross-system visibility. The governance framework has prevented new inconsistencies from emerging.

Timeline: 8 weeks for strategy development