One Platform for Better Business Decisions.

Fifteen industries, from manufacturing, retail and logistics to energy, financial services, healthcare and the public sector. Each is configured on its own data and decisions rather than a generic template — a demand forecast that understands promotions, seasonality and production capacity is a different thing from a generic time-series model. Manufacturing and retail have the deepest ready-made playbooks.

It depends on how many systems are connected and how clean the underlying data is, so we do not publish a single figure — any vendor who quotes one without seeing your estate is guessing. What we can say is that Veraius reads from the systems you already run rather than replacing them, so there is no migration project in the way. The agent-readiness assessment on this site gives an honest indication of how much preparatory work your data foundation will need.

Pricing is not published, because it depends on deployment model, the number of systems connected and the scope of decisions automated. Commercial terms are quoted per engagement. Contact us for a figure specific to your situation rather than a range that would not apply to you.

Six components: Decision Intelligence (the reasoning layer), Decision Cockpit (the operating view), Predictive Analytics (forecasting and anomaly detection), AI (natural-language analytics grounded in your data), Data Intelligence (connecting ERP, MES, POS, WMS, CRM and IoT), and Decision Automation (the path from signal to executed action). Each is useful on its own; together they close the loop between data you already have and the action someone finally takes.

No. Veraius reads from the systems you already run and sits on top as a decision layer. Data stays in the systems that own it. This is deliberate: a decision platform that requires a migration project first will not be adopted, and the migration usually costs more than the platform.

Every recommendation carries its reasoning: the inputs at the time, the alternatives considered, and the constraint that was binding. Decision logic is explicit and multi-criteria rather than a single opaque score, so a planner can disagree with a specific part of it. This is a hard requirement of the design — an explanation nobody can argue with is not an explanation.

The rejection is captured as data, with a reason. This matters more than it sounds: a planner who consistently overrides a suggestion and is consistently right knows something the model cannot see. If rejections are not recorded, that expertise evaporates and the system never improves. It is the most common gap in this category and the least often asked about during evaluations.