Custom AI development company TMA Solutions: Your innovative AI development company

Many businesses realize that pre-packaged AI solutions often cannot effectively handle complex and specific business problems. To create real value, businesses need AI systems that are "tailor-made" for their needs. With experience as a custom AI development company, TMA Solutions helps you get the most value from your internal data and turn it into a long-term competitive advantage.

1. Custom AI Development Capabilities of TMA Solutions

TMA Solutions has comprehensive capabilities that cover the entire development lifecycle of an AI solution. This allows TMA to fully meet all requirements, from in-depth data analysis to process automation, helping businesses effectively solve their specific business problems.

1.1. Math & Statistics ML

Applications: Market demand forecasting, customer segmentation, credit risk scoring, and operational process optimization.

Capabilities: TMA implements traditional statistics and ML models (like regression, time-series) and integrates them into the data pipeline. This helps businesses make decisions based on near-real-time data directly on their operational dashboard.

1.2. Deep Learning

Applications: Image recognition in video, anomaly detection on production lines, and analysis and recognition of voice or sound.

Capabilities: TMA develops and optimizes Deep Learning models (CNN, RNN) for both edge devices (Edge AI) and the cloud. Notably, TMA has practical experience deploying Edge AI for security monitoring and industrial systems.

1.3. Smart Camera

Applications: Public safety monitoring, road infrastructure quality inspection, or building security solutions for factories and smart buildings.

Capabilities: TMA designs AI camera systems that process in real-time, with the ability to provide on-site alerts (edge inference). Additionally, TMA has experience with deep hardware/IoT integration (like Jetson, Intel NCS) and video management platforms (VMS).

TMA designs real-time processing AI camera systems

1.4. MLaaS/Mlops

Applications: Providing model training, deployment, and monitoring as a service (MLaaS); building automatic CI/CD pipelines for AI models; tracking quality and performance decay (drift).

Capabilities: TMA offers MLaaS on major cloud platforms (Azure, AWS, GCP). TMA also designs the complete MLOps lifecycle, from data versioning to model monitoring.

1.5. Predictive Analytics

Applications: Forecasting consumer demand and inventory levels, predicting equipment failure (predictive maintenance), or scoring for credit risk and fraud.

Capabilities: TMA builds forecasting models by combining multiple data sources (transactions, operations, sensors). We also integrate these predictions directly into the workflow as alerts or automated tasks.

TMA builds forecasting models

1.6. NLP | OCR 

Applications: Building chatbots/voicebots, automatically extracting data from contracts, invoices, and prescriptions; classifying and summarizing large blocks of text.

Capabilities: TMA has multi-language OCR technology (Vietnamese, English, Japanese, Korean). We also build complete NLP pipelines to extract, standardize data, and automate processes (RPA+AI), especially in the healthcare and logistics fields.

TMA builds chatbots

1.7. Generative AI | LLM 

Applications: Developing internal virtual assistants, building AI agents to automate processes, summarizing documents, and creating Q&A systems based on a company's own knowledge base (RAG).

Capabilities: TMA provides a no-code AI Agent/Agentic framework to build workflows quickly. We have special experience deploying chatbots for finance and e-commerce, ensuring personalization and system integration.

TMA AI Agent Factory

2. TMA Solutions’s Collaborative Development Process

TMA Solutions' collaborative process is built to turn complex AI ideas into practical, operational solutions. Each stage focuses on precisely identifying the business's core problem. This allows us to develop an AI system that delivers clear and measurable value.

2.1. Step 1: AI Strategy Consulting & Problem Definition

Activities: In this step, TMA Solutions works with your business to set a clear direction for your AI strategy. Specifically, TMA will:

• Understand your business goals and the problems you want AI to solve.
• Analyze your existing data to see if AI can be applied.
• Suggest suitable use cases and prioritize the solutions that offer the highest value.

Outcome: The client receives a strategic roadmap (including use cases, KPIs, proposed architecture) and a clear project scope (POC/Pilot).

2.2. Step 2: Data Assessment & Preparation

Activities: The TMA team surveys data sources and assesses their quality and availability. Then, TMA designs the data lake/warehouse architecture. We build pipelines to collect, clean, and transform (ETL/ELT) data on a modern platform.

Outcome: After this step, the business will have a complete data platform. The system is standardized, clean, and organized, making the data easy to use for training AI models, creating BI reports, or for real-time analysis.

2.3. Step 3: Model Development & Training

Activities: In this stage, TMA's AI engineers develop Machine Learning (ML) and Deep Learning (DL) models suited for specific problems. These can include forecasting, risk classification, Computer Vision, or Natural Language Processing (NLP). This process includes training, iterative tuning, feature engineering, and in-depth model evaluation.

Outcome: The client receives a quality-approved AI model (validated with real data), along with a report comparing its performance to baseline models and recommendations for deployment.

TMA's engineers develop ML/DL models for specific problems

2.4. Step 4: Integration & Pilot Testing

Activities: TMA integrates the trained model into the business's application or workflow (via APIs, event-driven methods, or AI Agent workflows). The solution is then pilot-tested in a near-real environment to gather user feedback.

Outcome: After this step, the business will have a stable pilot version of the AI solution running in a near-real environment.

2.5. Step 5: Full-Scale Deployment & Optimization

Activities: In this stage, TMA Solutions deploys the AI solution at full scale. We put the system into official operation on TMA's or the client's platform (on-premise or cloud). TMA's engineering team will:

• Optimize performance and operational costs, ensuring the system runs stably and saves resources.
• Set up an MLOps process to monitor, update, and retrain the model when new data is available.
• Continuously monitor operations, detecting and handling technical issues or data drift promptly.

Outcome: The business receives a complete AI system that operates stably in real-time. It can also scale flexibly as data volume or business scale increases.

3. Why choose TMA Solutions as Your Technology Partner

With over 28 years of experience in the IT industry, TMA Solutions is a trusted partner for over 30 countries worldwide. TMA provides complete technology solutions, from consulting, design, and software development to operations management. We help businesses speed up their digital transformation and achieve sustainable innovation.

• 28 years of IT experience, with 4,000 software engineers in 7 countries (Vietnam, USA, Canada, Australia, Japan, Singapore, and Europe).
• A specialized team in AI, Data, Cloud, IoT, Fintech, and Healthtech. We can handle the entire end-to-end process, from research and consulting to development and operation.
• Multi-disciplinary R&D Centers: Data Solution Center, AI/RPA Center, HealthTech Center, IoT Center, Automotive Center, Robotics Center, and more.
• Compliance with international standards like CMMI Level 5, ISO 9001, ISO/IEC 27001, Agile, and RUP. This ensures a professional development process and data security.
• Over 600+ projects for clients in 30+ countries, including the USA, Australia, Japan, Korea, and Singapore.
• Skilled in modern technologies: Azure, AWS, GCP, Databricks, Power BI, Hadoop, Spark, Kafka, TensorFlow, Pytorch, and OpenAI.

TMA Solutions' Commitment:

• Top Quality: Every product is tested according to international standards.
• Total Security: Client data and information are fully protected.
• Sustainable Partnership: We don't just provide solutions; we grow with our clients long-term to optimize their business efficiency.
• Continuous Innovation: We always update to the newest technologies to create outstanding value.

4. Featured Custom AI Projects

TMA's capabilities are best shown through our real-world projects, where we turn AI technology into measurable business value. Here are some outstanding custom solutions that TMA has deployed:

4.1. Boost productivity with AI-powered Smart Call Center Assistant

Introduction: This project deployed a Smart AI Assistant for a contact center. The goal was to increase agent productivity and standardize the customer experience for a service agency in Canada that serves the E-commerce, Retail, and Logistics industries.

Problem: The client faced a growing volume of calls, inconsistent answers from agents, and pressure to optimize operational costs. They needed a tech solution to handle the overload and standardize support quality.

Solution: TMA built an AI assistant that uses real-time Speech-to-Text technology. The solution includes a knowledge base powered by AI (GPT and LlamaIndex). This helps analyze customer questions and suggest accurate answers for the agents.

Results: The solution helped increase call center agent productivity, reducing the average call handling time by 20%. The client also optimized their staff by 20%, leading to significant cost savings and ensuring consistent interaction quality.

>> Read more: Case Study: AI Smart Call Assistant Increases Productivity

Case Study: AI Smart Call Assistant Increases Productivity

4.2. Cloud-based and AI-driven solution for seamless operations and enhanced client experience

Introduction: This project focused on building a pet care e-commerce marketplace for a startup in Australia. The core goals were scalability, seamless operation, and a personalized experience.

Problem: The client's existing system lacked scalability and could not handle traffic spikes during peak hours. Integrations with third parties (payment, logistics, CRM) were fragmented. Customer support was slow, which affected the user experience.

Solution: TMA deployed a cloud infrastructure with auto-scaling and load balancing. The engineering team seamlessly integrated payment gateways and shipping carriers. They also built a 24/7 support portal with AI assistance and personalized product recommendation features.

Results: The new solution automated and optimized operational processes. The system can scale flexibly to handle high traffic. It also provides a personalized shopping experience, which significantly improved customer satisfaction.

>> Read more: Case Study: Cloud-based and AI-driven solution for seamless operations and enhanced client experience

Case Study: Cloud-based and AI-driven solution for seamless operations and enhanced client experience

4.3. AI sound-based fault detection system in manufacturing

Introduction: This project applied AI sound analysis to detect product defects (water valves) in real-time on the production line for a building materials manufacturer in Vietnam.

Problem: The traditional manual quality control (QC) process could not keep up with the production scale. This led to inconsistency, missed defects, and a heavy reliance on staff, which affected the brand's reputation.

Solution: TMA designed a sound-based fault detection system. A sound sensor captures signals as the valve is tightened. An AI model then classifies the sound as "normal" or "faulty." When a defect is found, the system automatically signals a robot arm to remove the faulty product.

Results: The system fully automated the QC process, greatly improving quality and reliability. The faultless product rate reached an impressive 99%. It also increased the production line's efficiency and scalability.

>> Read more: Case Study: AI Sound-Based Fault Detection for Manufacturing 

Case Study: AI Sound-Based Fault Detection for Manufacturing

4.4. Machine Learning & AI techniques for credit risk analysis

Introduction: This project focused on using AI and Machine Learning to improve the accuracy of a credit scoring model for a finance industry client in Australia.

Problem: The client struggled to assess credit risk accurately. Their data was scattered across different systems (internal and third-party), making analysis difficult. They needed a better way to measure risk in a volatile market.

Solution: TMA built a data integration platform to automatically merge the data sources. On that platform, engineers applied AI/ML techniques to fine-tune the credit scoring algorithm. This helped the model make better predictions based on historical data patterns.

Results: The new solution significantly improved accuracy in risk assessment. This helped the financial organization make smarter, more strategic lending decisions and allowed them to manage credit risk proactively.

>> Read more: Case Study: Machine Learning & AI techniques for credit risk analysis

Case Study: Machine Learning & AI techniques for credit risk analysis

4.5. A recruitment solution for employers with Automatic CV Input

Introduction: This project provided an end-to-end recruitment solution that fully automates the CV processing workflow for an HR client in Australia.

Problem: The client had to manually process over 1,000 CVs every week. This created a serious bottleneck, slowed down hiring, and increased the risk of errors when entering data into the Applicant Tracking System (ATS).

Solution: TMA developed a scalable solution combining RPA, AI/OCR, and Web Automation. The system automatically downloads CVs. It uses AI/OCR to accurately read and extract data (even handwritten text) from complex layouts, then inputs it directly into the recruitment system.

Results: The solution almost completely eliminated manual data entry, saving the HR team over 8,000 work-hours per year. The hiring process was shortened, bottlenecks were reduced, and the new-hire onboarding experience became smoother.

Case Study: A recruitment solution for employers with Automatic CV Input

>> Read more: A recruitment solution for employers with Automatic CV Input 

5. Frequently Asked Questions (FAQs) About Custom AI Development Company

Q1: What kind of data do we need to start a custom AI project?

The quality and scope of your data are key factors for the project's success. Basically, you will need:

• Relevant Data: The data must be directly related to the business problem. Examples include transaction logs, customer records, or unstructured data (text, images, sound). For supervised learning models, this data needs to be labeled.
• Quality Data: The data must be complete. We will perform a Data Assessment step to check its quality, legal status, and potential risks (like bias) before we start training.

Q2: How long does it take to develop a custom AI solution?

The time to develop a custom AI solution can range from a few weeks to several months. It directly depends on the problem's complexity and how ready your data is.

To give you an idea, we usually classify project sizes as follows:

• Small-scale (2-4 weeks): Usually focuses on a simple use case, such as a chatbot that answers common questions.
• Medium-scale (1-3 months): For solutions that need complex logic and basic integration, like a system that extracts data from invoices.
• Large-scale (3-6+ months): For core AI systems that are deeply integrated into many business processes (e.g., a custom risk prediction system).

Q3: How do you ensure the confidentiality and security of our data?

Data privacy and security are TMA's top priorities. We apply an Information Security Management System (ISMS) that strictly follows the ISO/IEC 27001 standard.

• Technically: We use multi-layered security measures. This includes data encryption (in transit and at rest), access control (RBAC/least privilege), and techniques like data anonymization.
• Procedurally: We use risk management frameworks (like NIST AI RMF) to control AI-specific risks. When using third-party APIs, we promise to configure the highest security settings (e.g., zero data retention) to ensure your data is not used for training.

Q4: What is the difference between custom AI and using an API from OpenAI/Google?

These are two different strategies, depending on your needs:

• Using an API (OpenAI/Google): This allows for fast deployment and reduces operational work. It is suitable for common tasks, as long as your partner (like TMA) ensures data privacy settings are configured (like zero data retention).
• Building Custom AI: This gives you complete control. You can fine-tune models or build RAG systems based on your internal knowledge. This helps optimize costs, reduce latency, and ensure compliance with industry-specific security rules (e.g., deploying on-premise).

Data privacy and security are TMA's top priorities

Choosing an AI partner isn't just about technology; it's also about execution capability and professional processes. With 28 years of experience, diverse skills from Deep Learning to Generative AI, and a proven development process, TMA Solutions has the capability to turn the most complex AI ideas into practical, operational solutions.

>> If you are ready to build high-performance AI solutions, contact experts of TMA Solutions for a consultation!

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