When off-the-shelf AI doesn't fit your problem.
Build bespoke machine learning models tailored to your unique challenges. From NLP and computer vision to recommendation engines and anomaly detection.
Generic AI tools solve generic problems. Your business has specific data, specific constraints, and specific outcomes to optimise. We build custom models trained on your data, validated against your business logic, and deployed into your existing systems — giving you a competitive advantage that can't be replicated by buying the same SaaS tool your competitors use.
Every engagement is scoped to your specific situation. These are the capabilities we bring to every custom ai/ml development engagement.
Document extraction, sentiment analysis, chatbot engines, classification, and summarisation — built on your industry-specific language and terminology.
Visual inspection, defect detection, OCR, object recognition, and image classification systems deployable on cloud or edge hardware.
Personalised product, content, or service recommendations that improve engagement and cross-sell revenue using collaborative and content-based filtering.
Real-time detection of fraud, equipment failure signals, and operational outliers that rule-based systems routinely miss.
Demand forecasting, price optimisation, and capacity planning models that account for seasonality, external signals, and business-specific patterns.
Deployment pipelines, monitoring, automated retraining, and drift detection so your models stay accurate as the world changes.
Translate your business challenge into a precise ML problem statement with success criteria, constraints, and evaluation metrics agreed upfront.
Source, clean, label, and augment training data. This phase determines 80% of model quality — we take it seriously.
Train, evaluate, and iterate on candidate models. Interpretability and bias checks are built into every experiment.
Integrate the model into your systems via API or embedded deployment. Set up monitoring dashboards and automated retraining triggers.
Real engagements. Real numbers. Every result quoted here was achieved in a production environment.
Food Manufacturing
The Challenge
A food manufacturer's manual visual QA process was catching only 72% of packaging defects — leading to customer complaints and retailer penalties. Hiring more inspectors wasn't scalable as production volumes increased.
Our Solution
AntInsight developed a computer vision model trained on 50,000 annotated product images covering 14 defect categories. The model was deployed on an edge device at the production line exit, providing real-time reject signals without slowing throughput.
Results Achieved
97.4%
Detection Accuracy
Up from 72% manual inspection
60%
QA Cost Reduction
Labour cost saved in inspection
<0.3s
Inference Speed
Per unit at full production speed
Financial Services
The Challenge
A lender processing 400+ loan applications per month required each application to be manually reviewed by an analyst — taking an average of 3 working days and creating a bottleneck that was limiting growth.
Our Solution
We built an NLP pipeline to extract and validate 23 document fields automatically, combined with a risk scoring model trained on 5 years of historical loan performance. Analysts now review only edge-case applications flagged by the model.
Results Achieved
89%
Automation Rate
Applications processed without analyst
4 hours
Processing Time
Down from 3 working days
2.1%
Default Rate Drop
Improved risk scoring accuracy
Talk to our AI Consultant for an instant assessment, or book a discovery call with Rattapon directly for a tailored proposal.
Based in Khon Kaen, Thailand · Working with clients across Southeast Asia