Drive business growth through custom AI development and smart automation solutions.
We offer bespoke and easy-to-scale AI development services,
catering to startups, SME and large-scale enterprises alike.
- Consulting for data analysis strategies
- Design & construction of Data Lake/ Data Warehouse (compatible with AWS, Azure, GCP)
- Development of ETL/ELT pipelines
- BI report development using Power BI/ Tableau
- Business trend forecasting using statistical models and machine learning
- Data collection and annotation for machine learning
- Application of ML for demand forecasting, customer segmentation, and behavior analysis
- Image processing (OCR, classification, detection, tracking...) and speech processing (STT, voice cloning...)
- Natural Language Processing/ LLM: chatbots, document classification & analysis...
- Enterprise-focused generative AI utilization and LLM fine-tuning
- Automated training & deployment: SageMaker, Azure ML, Vertex AI
- Model monitoring: Model Monitor, Azure Monitor, Vertex AI
- Version control: S3, GCS, Blob + ML Registry
- Stable operation: Docker, Kubernetes & CodePipeline, DevOps, Cloud Build
- Retail AI: Recommendation systems, AI cameras, sales forecasting
- Medical AI: Image diagnosis support, diagnosis probability estimation
- Finance AI: Risk prediction, fraud detection
- Generative AI (GenAI): Automated content generation, enterprise AI assistant, multilingual response solutions
Harness advanced language models to convert legacy codebases (e.g., VB6) into modern frameworks such as VB.NET. The system performs deep code understanding, structure preservation, and syntax translation—minimizing manual effort while ensuring high code fidelity. Ideal for enterprises aiming to modernize legacy systems with speed, precision, and minimal risk.
An advanced Retrieval-Augmented Generation solution designed for large-scale document intelligence. HBLAB RAG processes structured and unstructured files (PDF, Word, Excel), enabling real-time Q&A, semantic search, and contextual content retrieval. Built with scalable architecture, API-ready integration, and granular data control, it’s engineered for enterprise-grade security, multilingual performance, and seamless deployment across industries.
The numbers speak for themselves – here’s why leading companies choose HBLAB.
Meet our trusted leaders in technology and AI strategy
Customer: A Japanese Heavy Industries
Problem: The customer wanted to use AI to predict the operating condition of bearings in industrial fans
HBLAB’s solution:
Analyze and process data from bearing sensors (~ 300 sensor from 64 fans) such as temperature, vibration, amount of lubricant used…
Train an AI (time series) model to understand historical data and predict the future performance of bearings (temperature and vibration)
Project Details:
■ Team size: 2 AI Engineer + 0.5 AI PM
■ Duration: 3 months
■ Technology: Time series model (PatchTST, TFT)
Result:
For temperature (range value 20 -> 60 Celsius): MSE ~ 2
For vibration (range value ~0.7 -> ~1.2 mm/s): MSE ~ 0.05
*MSE: Mean squared error
Customer: A large corporation in Japan in the field of providing business operation services, accounting – finance & real estate management.
Problem: The customer assigns about 200 employees the task of data entry – which is high cost, time consuming & there is still a risk of Human errors in the data entry process.
There are 2 types of documents with a large amount of information to enter: Insurance Card & Bill. They need to find a faster & more cost-effective method for this process.
HBLAB’s solution: Combine multiple AI Models to create a total solution:
1. Use Image Processing to re-edit the image (shooting angle, photography direction, blur, shadow …)
2. Use Object Detect to catch the area containing the information needed
3. Read information:
– Insurance card: Use OCR to read
– Bill: Use OCR to read both print & handwriting. Use Logo searching to collect & classify data about suppliers information.
Customers demand to collect data that change over time and for different work purposes. As a result, HBLAB proposed different AI models to solve changing problems for customers.
Project Details:
Insurance card information Reader:
■ Development Team: 5 AI engineer + 0.5 PM + 0.5 Comtor
■ Duration: 1 year
■ Technologies: Image Processing, Object Detect, OCR, DewarpNet, Text Detection
Bill Reader:
■ Development Team: 2 AI engineer + 0.5 PM + 0.5 Comtor
■ Duration: 9 months
■ Technologies: Image Processing, Object Detect, OCR, Logo Searching
Result: The logo & store name accuracy is 93%, the other information accuracy is 95%-97%.
The AI model built by HBLAB helps to save 20% of the customer’s initial data entry, optimize costs & speed up work.
Customer: A company in Japan specializing in smart electronic devices for pets
Problem: The client wanted to use an AI model to detect abnormal behaviors in dogs based on sensor data collected from smart collars.
The goal is to identify unusual activity (e.g., excessive scratching) and provide personalized recommendations via a pet care app.
HBLAB’s solution:
■ Extracted key features from sensor data (accelerometer & gyroscope) embedded in the dog’s collar
■ Trained an AI model using anomaly detection techniques to identify irregular behaviors
■ Deployed the AI engine to AWS SageMaker as an endpoint for integration
Project Details:
■ Development Team: 2 AI Engineer + 0.5 AI PM
■ Duration: 5.5 months (3 phases)
■ Technologies: Auto Encoders, OneVSRest SVM
Result: F1 scores ~ 85%
Customer: Real estate trading & leasing floor with 50 years of experience in Japan
Problem: The system suggesting similar products is not working properly. The click-through rate of suggested products is low.
HBLAB’s solution:
Develop an AI Model to optimize the suggestions close to customer needs, increase the rate of meeting customer needs on web.
1. User-provided information: House direction, price, area, distance to the station, available for pets, etc.
2. The AI model system with XGboost technology is tested on many hypotheses and algorithms, which helps to increase the ability to filter and search for products with high matching with customer requirements.
Project Details:
■ Development Team: 1 AI engineer
■ Duration: 6 months
■ Technology: XGBooth
Customer: Japanese Technology Company in the field of Game, Sport DX, AI & Marketing
Problem: The developing game on the baseball team management simulation lacks reality experiences in the content because there is no information to predict and simulate the development and change of the team in 1, 2 years.
HBLAB’s solution:
1. Statistics of baseball teams in 3 years time
2. Building simulation models of up-down trends of baseball players
Basis information: age, competition record, current running speed, technical score, fitness score.
Assessments and predictions: Running speed, technical score, fitness score in the following year.
3. Applying Model AI technology on game to create genuine experience for gamers.
Project Details:
■ Development Team: 1 AI engineer
■ Duration: 2.5 months
■ Technologies: Random forest, XGboost
Result: The game has now reached 10,000 players in Japan.
Customer: A leading educational corporation in Japan.
Problem: Customers have many documents to guide teachers in using the tools. The customer wants to build a chatbot to answer and guide teachers about the above information.
HBLAB’s solution: Use RAG + OpenAI API (ChatGPT 3.5) technique to create chatbots
Project Details:
■ Team size: 1 LLM Engineer + 0.5 Backend + 0.5 Front end
■ Duration: 2 months
■ Technology: OpenAI API, RAG, Langchain
Our diverse knowledge & skills includes state-of-the-art technology, platforms, and languages to ensure the quality of each software development project.
Hear directly from our clients and partners on how we’ve helped bring their visions to life with reliable, impactful technology solutions.
You can reach us anytime via lp_gl@hblab.vn
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