Built for Enterprise Intelligence.
A platform your architects can reason about: open integration, explainable models, and a deployment model you choose. The technology exists to make the business answer trustworthy.
Ten layers, one decision path.
Each layer earns its place by making the next decision better, faster or more defensible.
AI & Machine Learning
Multi-agent orchestration with planning, parallel specialists and a review pass before any answer is returned.
Predictive Analytics
Demand, stock, production and performance forecasting fitted to your own operational history.
Decision Models
Explicit decision logic and multi-criteria scoring, so a recommendation can be inspected rather than trusted blindly.
Data Platform
Ingestion, a canonical data layer, retrieval and an enterprise knowledge graph over the systems you already run.
Real-time Analytics
Streaming ingestion and live aggregation, so the decision reflects the state of the operation now.
API-first Architecture
Versioned REST and GraphQL surfaces. Everything the interface does is available to your own systems.
Event-driven Integration
Message-based connectors to ERP, MES, WMS, POS, CRM and IoT, with backpressure and replay.
Workflow & Automation
Decisions executed as workflows — with approvals, guardrails and a human in the loop where it matters.
Observability
Traces, metrics and structured logs across the whole decision path, including model calls and their cost.
Security
Identity, access control, tenant isolation, encryption and audit logging built into the platform, not bolted on.
Enterprise intelligence. Built for your data.
It runs where your data is allowed to live.
Including fully on-premise, with local models, when nothing may leave your network.
On-Premise
Every component, including the models, inside your own network boundary.
Private Cloud
A dedicated environment in your own cloud tenancy and region.
Hybrid
Sensitive data and models stay local; the rest runs where it is most efficient.
Explainable AI architecture, evaluated the way architects evaluate things
An explainable AI architecture is not a feature bolted onto a model; it is a property of how the system is put together. If reasoning is reconstructed after the fact, it is a story about the answer rather than the reason for it. Veraius records the chain as the decision is made — which records were read, which rules applied, which options were compared — because that is the only version that survives being questioned.
The same principle governs where the platform runs. Models, data and execution can sit on-premise, in a private cloud, or split across both, and the choice is a deployment decision rather than a pricing tier. Teams with data residency obligations do not have to trade them against capability.
Integration is read-first and additive: Veraius connects to the ERP, MES, POS and warehouse systems already in place, without asking to become the system of record. Nothing is migrated to make the platform work, which is what makes an evaluation possible in weeks rather than quarters.
Bring your architects to the conversation.
The fastest way to evaluate the technology is to point it at your own systems and see what it does.
What your teams gain from Veraius agents.
Capacity before it runs out
Resource demand is forecast and cost-saving options are proposed before limits are reached.
See the IT Operations agents ManufacturingCapacity against orders
Open orders are compared with available capacity so delivery risk shows before a due date is missed.
See the Manufacturing agents SalesA forecast grounded in your history
A hierarchical 12-month sales forecast is built from your own orders and reconciled with external factors.
See the Sales agentsIn every department
- Agents prepare the work on your own data; your people approve what happens next
- Plain-language questions answered from live ERP data, with tables and interactive charts
- Grounding check: numbers that cannot be traced to a source are not shown
- Explainability, confidence and risk scores with decision rationale
- Slack, Teams, WhatsApp and Signal channels with an approval inbox
- Department-based access policy with personal data masking
Frequently asked questions
Straight answers about integrations, deployment, security and cost — including where the honest answer is "it depends".
Ask your own questionWhat does the technology stack consist of?
A decision engine that composes forecasting models, optimisation and language models rather than choosing one. Forecasting tells you what is likely; optimisation tells you what is best given constraints; a language model explains the result in terms a planner can dispute. Any one of the three alone produces a system that is either unexplainable or unable to decide.
Which models do you use?
Whichever the data supports, tested against the decision rather than against a benchmark. A gradient-boosted model that beats a neural network on forecast error but cannot explain a stockout is the worse choice, because the output has to survive a conversation with the person accountable for it.
How does the platform handle model drift?
By watching the decision, not the metric. Accuracy degrading by two points matters less than a class of decision starting to be overridden by planners. The second is the earlier signal and the one that reaches a person; drift monitoring that only reports error rates finds out after the damage.
Can we bring our own models?
Yes. A forecast your data science team already trusts is worth more than a better one they did not build. The platform will consume it as an input and take responsibility for the layer above — turning the prediction into a decision, with its constraints and its record.