Making your AI models more accurate and longer-lasting
We are ready to stand proudly behind you
NAME
SLOGAN
PURPOSE
NOTES
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WELCOME
Partners
Investors
Partners
Investors
Insight #01
Why 87% AI/ML projects never make it into production? Is all well with your models?
Lack of leadership support and misalignment with business objectives often result in projects without clear direction or impact. Data silos, poor data quality, and inadequate infrastructure hinder accessibility and reliability, while insufficient collaboration between teams slows progress. Overemphasis on technology without a strategic approach and organizational resistance to change further obstruct deployment. To overcome these barriers, companies must foster strong leadership, enhance cross-functional collaboration, ensure high-quality data, and align AI initiatives with business goals.
Insight #02
Avoiding the Anti-Patterns of AI
By some estimates, over 80% of AI projects fail—twice the rate of non-AI IT projects due to misaligned objectives, data deficiencies, overemphasis on technology, inadequate infrastructure, and unrealistic expectations. To ensure sustainability, a robust model retraining strategy is essential. Regular updates with relevant data enable AI models to adapt to evolving conditions and maintain effectiveness. This continuous learning approach mitigates data limitations, aligns technology with practical applications, and improves the overall success rate of AI projects. Without ongoing retraining, AI systems risk becoming obsolete, leading to diminished performance and increased failure rates over time.
Insight #03
Inductive, deductive, and abductive reasoning
Inductive conclusions are based on limited observations and can lead to multiple plausible outcomes. The interplay between induction and deduction enhances the validity of conclusions, ensuring they are not solely dependent on patterns observed in a limited dataset. Similarly, in data analysis, exploratory findings should always be validated against domain knowledge to distinguish meaningful insights from spurious correlations. By integrating both reasoning approaches, one can critically assess claims, minimize biases, and refine conclusions in light of new evidence.
VALUES
we continuously hone for excellence
Adaptability:
True expertise is staying curious,
embracing new perspectives and learning quickly.
Presentation:
We are not judged by the greatness of our ideas,
but by how effectively we present them to the world.
Trust:
Strong trust eliminates the need to enforce responsibility,
keeping focus on what truly matters.
Communication:
Miscommunication hinders success,
while effective communication unlocks solutions.
VALUES
we continuously hone for excellence
Adaptability:
True expertise is staying curious,
embracing new perspectives
and learning quickly.
Presentation:
We are not judged by
the greatness of our ideas, but by
how effectively we present them to the world.
Trust:
Strong trust eliminates
the need to enforce responsibility,
keeping focus on what truly matters.
Communication:
Miscommunication breeds confusion,
while effective communication fosters clarity, builds trust, and unlocks solutions.
With years of experience and an unwavering passion for innovation, our team is the catalyst behind
every breakthrough we’ve achieved and every bold leap into the future we dare to imagine.
Dong-Joon Lim
Founder & CEO
Gyu-Jin Kim
AI Team Lead
Soo-Jeong Kwak
Lead Data Scientist
Jisun Kim
Senior ML Scientist
Sung-June Lee
ML Engineer
Min-Keon Song
LLM Engineer
Dong-Joon Lim
Founder & CEO
Gyu-Jin Kim
AI Team Lead
Soo-Jeong Kwak
Lead Data Scientist
Jisun Kim
Senior ML Scientist
Sung-June Lee
ML Engineer
Min-Keon Song
LLM Engineer
“Comping” is an accompaniment technique in jazz where other instruments create
a rhythm to support the soloist. COMPiNG™ is our solution that orchestrates
workflows to ensure your Al models perform at their best.
Ready to PLAY? Let us COMP for you!
contact@comping.ai
Ready to PLAY?
Let us COMP for you!
contact@comping.ai