On the building side of the gap, since before AI became a category

Most companies attempting AI never get it into production. MIT's NANDA initiative found that 95% of enterprise generative AI pilots show no measurable profit-and-loss impact, based on more than 300 deployments and 52 company interviews. Gartner puts the GenAI abandonment rate after proof of concept at 30%, and predicts 60% of AI projects lacking AI-ready data will be canceled through 2026. The gap is not between companies that want AI and companies that do not. It is between firms that can build it and get it running inside a real operation, and everyone else.

Erbis has been on the building side of that gap since before AI became a category. Our founders started in large-scale, regulated enterprise software: security and development engineering on healthcare laboratory systems used by Mayo Clinic, and a clinical-trial patient management system funded by Pfizer. That work carried HIPAA compliance, data governance, and security requirements that most development firms have never had to meet. It is the reason we know how to ship AI that holds up in production, not just in a demo.

Today we work primarily with mid-market and enterprise supply chain companies, where our portfolio runs deepest. We have built demand forecasting, computer vision quality control, warehouse and logistics automation, and AI orchestration for clients including DHL, SAP, and Siemens. The software we have built has taken more than $110 million in costs out of our clients' operations, and our AI implementations run at roughly 95% accuracy in production. Beyond the supply chain, our engineers have built for regulated healthcare, manufacturing and industrial operations, and legal and compliance technology.

We build on the infrastructure our clients already run. Erbis is an AWS partner and a Databricks partner, and we also build in Azure and Oracle environments. We hold ISO 27001 certification and have completed more than 4,000 system integrations, including deep work inside the legacy WMS and TMS environments that other firms decline to touch. When data governance is the thing a client is most afraid of, we build the controls in from the foundation rather than bolting them on at the end.

At 200 engineers, we are large enough to take on complex, multi-system deployments and small enough that an operations leader can reach an actual decision-maker here when something needs to move. We do not take every engagement. If a company needs a generalist team, a quick MVP, or software built outside our core verticals, we will say so in the first conversation and point them somewhere better.