AI software development
involved in implementing ML models as software.
Instantly assemble and deploy ML models both in-house and external
Easily integrate your in-house ML models and models from external vendors, and quickly deploy to the cloud
Complex async processing, such as integrating multiple ML models, can also be expressed simply
Deploy features designed on the GUI just in 1 click.
Speed up your “build-test” process
"Collaborative design and
development of AI software."
Co-design with users
Design business and data flows through conversations and deploy them with ease using the "Canvas" interface (Patented)
Clear role division among engineers
Machine learning engineers can focus on model development, while software engineers can focus on software development
Incremental digital transformation
Flexibility to add new components and embrace changes after
Gradually incorporating business into the data
The data structure is flexible and allows for gradual systematization of complex on-site operations, and connecting to existing databases and systems is also easy
Accumulating data for later AI implementation
It's also easy to use the system on a small scale, collecting data as you go, and then replace certain processes with AI at a certain point and run A/B tests.
"DX as Code"
The invention supporting exaBase Studio is the domain-specific language "exaBase Blueprint."
By incorporating the paradigm of reactive programming, it allows to express real-world data and logic extremely flexibly
A collection of AI software designs
– Automate complicated development/implementation processes to realize the Democratization of AI led by operational departments and managers –
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