FAST, GREEN, ACCURATE
DeepMentor aims to unlock the full potential of Edge AI and make it universally accessible!
Training & On-prem Implementation Solution
We assist clients in launching highly competitive proprietary domain-specific generative conversational AI services or achieving internal AI data-driven training. We offer end-to-end integration services to ensure smooth deployment from the cloud to on-premises environments. Leveraging DeepMentor’s exclusive LLM automated training process, we can expedite the launch of your new AI services, allowing you to seize market opportunities.
Mentor-100 provides secure yet budget-friendly LLM Al model training.
It boasts a formidable capability to support more than 180 billion LLM parameters.
Designed for business, it’s portable, secure, and constantly learning from your meeting content.
Ensure the privacy and security of corporate confidential information & user personal data
Compared to expensive cloud inference costs, on-premise solution can greatly reduce operational costs
real-time synchronization integration of enterprise databases and backend AI service systems
LLM trained for a given task and dataset can be optimized for higher accuracy in generating targeted content
Six functional modules developed exclusively by deepmentor: Document /Customer Service / Code / Image / Meeting / Fine-tune for customization and function optimization
Our Amazing Partners
DeepMentor is a team of experts with over a decade of theoretical research and practical experience in Edge computing IC design and system development.
DeepMentor Patented Miniaturization Technology
We successfully keep the advantages of Cloud AI on Edge AI!
As a result,
Deepmentor Edge AI can accurately recognize objects from different angles, day & night, and in adverse weather conditions.
Other Edge AI needs to deal with the inaccuracies of small AI algorithms and the cumbersome optimization.
DeepMentor Patented Automated AI IC Design
We automate the creation of circuit diagrams for AI models and their implementation on various FPGAs, significantly reducing the time and effort required to design AI IC chips.
DeepMentor’s exclusive automated technology offers “miniaturization” and “hardening” capabilities for multiple large-scale professional AI algorithms, tailored to meet customers’ specific functional requirements. This innovative process results in the creation of an FSM (DeepLogCore AI IP).
One key advantage of DeepMentor’s technology is its ability to eliminate the need for compilers and SDKs, avoiding the challenges related to the instruction set conversion difficulties and the risk of invalid circuits in chip design. Consequently, we are able to develop dedicated AI IP with a computing power utilization rate exceeding 90%, resulting in compact die areas and significant energy savings.
DeepMentor’s automated solution enables clients to easily and efficiently upgrade to next-generation AI solutions, ensuring faster time to market (within 3 months). The avoidance of software integration issues and concerns about labor shortages further simplifies the productization process for clients.
Our Latest AWARDS
- 2023 AI Neo Star award
- 2023 AI Taiwan 2023 future commerce award
- 2022 Taipei Prominent Enterprise Award–R&D Excellence Award
- 2022 Taiwan Innotech Expo Award
- 2021 Subsidies & Incentives for Taipei Industry-Miniaturization Development Plan
- 2021 The Small and Medium Enterprise Administration, MOEA 5th G Camp Double Award
- 2021 The Industrial Development Bureau, MOEA AI Company Certificate–AI Chip/Software/Hardware Co-Design and Optimization
- 9 national patents granted
"I hope that AI can truly be applied and popularized in our daily lives, and help various industries reduce their costs."
- R&D Supervisor at Realtek and Zyxel
- Over 20 years of IC and embedded system design development experience
- Professional in interdisciplinary integration, high-performance system design, and EDA
- 2 papers published in IEEE TCAD Journal, and 23 articles published in international A-class journal conference
- Received the SASIMI Best Paper Award 2018