Ingkle puts customer success first, and is committed to deeply understanding customer needs and solving customer problems. We provide support for all technical issues related to Nazare, and additional fees may apply depending on each technical support item.
When implementing Nazare, Ingkle actively supports various tasks that need to be done incidental in relation to machineries.
We will safely migrate the machinery data that was previously in use to Nazare. Machinery data is collected in Nazare after the migration is complete.
Using Nazare's management console, Ingkle will set up the machinery connection directly. We help the customer to immediately import the specified data from the specified machinery.
If it is necessary to collect machinery data from special machinery or systems not supported by Nazare, we will develop collection driver software for this purpose and connect it to Nazare.
We will set the PLC's I/O address so that the machinery can communicate with Nazare. Machinery data can then be imported by specifying the corresponding address in Nazare's management console.
If the network connection between Nazare in the server room and the machinery on site is unstable, or if direct connection is difficult due to different IP bands, we will install a separate gateway collection device between the two.
We solve various difficult problems related to big data construction and operation, and integration with other systems.
Using Nazare, we collect and integrate not only machinery data but also various data within the organization to build a big data infrastructure that can be used by all members.
Ingkle directly operates and manages the big data infrastructure built with Nazare. Since Ingkle manages it like the customer's IT team, there is no need for a separate infrastructure management staff.
We will securely integrate your existing data pipeline system or desired open source technology with Nazare so that you can use it.
We use the Nazare data platform to provide solution software that performs specific functions required by customers.
Using HMI and SCADA, we create a solution that can detect errors that cannot be detected by simple upper and lower limit checks and receive alarms based on real-time machinery data trends (patterns).
By integrating the LLM model with machinery data collected in Nazare, we create an environment where machineries can be monitored using an interface like ChatGPT.
We will create a next-generation historian system that performs trend analysis and statistics aggregation at high speed based on Nazare's excellent query response performance for large amounts of data.