Manufacturing & Industrial

We design every engagement to deliver real value early. Most of our manufacturing clients see their first working solution within four to eight weeks of kickoff, with additional solutions following close behind. The goal is to put real value in the team's hands quickly rather than disappear for six months before the business sees a return.

No. Your ERP, MES, quality systems, and other operational tools stay in place. We build a centralized data platform that pulls data from each of those sources into one place, so the team keeps working in the systems they already know while leadership and operations get a unified view across the business. We only flag a system change when there's a clear business case, and even then it's a separate conversation from the analytics work.

You do. The data, the code, and everything we build sit inside your own environment and remain yours throughout the engagement. That's a deliberate contrast to black-box solutions that take your data off-site and return answers with no insight into how they got there. Every model, pipeline, and dashboard we build is transparent and fully documented, and if we ever part ways, everything stays with you with no lock-in.

Yes, and bringing shop floor data together with the rest of the business is some of the highest-leverage work we do for manufacturers. We have experience pulling data from MES platforms, PLCs and SCADA systems, quality management systems, and the full range of sensor and equipment telemetry sources. Each source presents its own integration challenges, and we approach them on a case-by-case basis, working closely with your IT and OT teams to make sure the data flowing in is reliable, timely, and fit for the analytics use cases the business actually cares about.

Yes, and it's a common starting point for our manufacturing clients. True cost-to-make and SKU-level profitability are notoriously hard to assemble because the inputs sit across the ERP, MES, labor systems, and procurement records, often using different conventions and timing. We build cost models that bring those inputs together, refresh them on real data rather than annual rolls, and surface profitability at whatever level matters most to the business, from SKU to customer to product line to channel. Finance gets a view of margin it can trust, and sales and operations get clearer signals on what's actually worth pursuing.

Yes, and it's worth noting that the vendor pitches in this space tend to dramatically oversell what the underlying data can support. We start by assessing whether the data actually says what it would need to say to support a meaningful model, whether equipment telemetry supports failure prediction, whether quality data is structured for modeling, whether maintenance records carry the right signal. Where the data supports it, we build models that deliver real value and integrate cleanly into the maintenance and quality workflows the team already uses.

We design solutions around the way plants already run. That means working with operators, planners, and supervisors to understand their workflows, embedding insights into the tools they already use (not just new dashboards), and measuring adoption as a success metric alongside financial impact. When teams help shape the tools, they’re far more likely to use them every shift.