AI & Custom Software Development for New Energy & Advanced Manufacturing

Battery traceability is the ability to link every cell and pack to its materials, equipment, process parameters, and test results. It supports quality investigations, warranty analysis, safety response, and regulatory reporting.

Analytics on process and test data can reveal which parameters drive defects, so engineers can tune the process faster than with trial and error alone. Results depend on data quality and process stability.

Yes. The core capabilities of traceability, inspection, process analytics, and equipment integration apply across battery, solar module, electrolyzer, and power electronics lines.

Robotics and automation cells, additive manufacturing, precision components, and other high-mix, high-precision production where data and adaptability are critical.

We help structure product, material, and supply chain data so it can be assembled for regulatory and customer reporting, and we design integrations with your systems. Final compliance interpretation remains with your regulatory team.

Cost depends on scope, the number of plants and lines, integration depth, data volume, and whether AI models must be trained on your data. A focused pilot costs far less than a multi-plant platform. Zenkins provides a fixed-price or time-and-materials proposal after a discovery session. For general ranges, see our Custom Software Development page.

You do. On project completion and final payment, all source code, documentation, trained models, and IP transfer to you, and your production data remains yours. On-premises and private-cloud deployment are both supported.

A typical pilot covers one line, plant, or workflow and runs about 6 to 10 weeks, depending on equipment connectivity and data readiness. (Please confirm this range against your real delivery experience before publishing.) It usually includes a discovery workshop, data source and equipment mapping, a working slice such as traceability for one process stage or a yield dashboard, and a review against metrics agreed up front. Those metrics might be scrap rate, traceability completeness, or time to investigate a defect. You then decide whether and how to scale.

Yes. We build on top of your existing systems rather than replacing them. We connect to equipment and testers through standard industrial protocols such as OPC UA, MQTT, and Modbus, through edge gateways, and through vendor-specific interfaces where needed. We also integrate with MES, ERP, and PLM platforms so traceability and yield data flow into the systems your teams already use. During discovery we map each data source and flag integration risks, such as older equipment or proprietary formats, before development starts.

Yes. The core capabilities apply to both: cell-to-module-to-pack traceability, formation and grading analytics, end-of-line test data, and quality data that supports warranty and safety investigations. EV and ESS lines often differ in volumes, pack variants, and reporting requirements, so we use configurable data models that adapt to each product family rather than hard-coding one pack design.

Yes, and this is common on new lines. We start by assessing data completeness and consistency across stations, then build pipelines that clean, align, and contextualize records, for example by matching timestamps, serial numbers, and equipment IDs across stages. Where gaps exist, we recommend practical fixes at the source, such as adding a scan point or standardizing a data field. Analytics and AI results depend on data quality, so we are upfront about what the data can and cannot support before building models.