AI that helps your business decide.
AI is a component of Decision Intelligence, not the whole product. It reasons over data that has already been connected, contextualised and predicted — and every claim it makes is grounded and checked.
Reasoning that is grounded and explainable.
AI Insights
What changed, where and why — surfaced without someone having to ask the right question first.
AI Recommendations
Ranked next actions with expected impact and the trade-offs made explicit.
Natural Language Analytics
Ask an operational question in plain language and get an answer sourced from your own systems.
Explainable AI
Every output carries its reasoning and its sources, so it can be challenged rather than trusted blindly.
Anomaly Detection
Patterns that do not fit are flagged with the context needed to judge whether they matter.
Predictive AI
Models that turn historical operational behaviour into forward-looking signals.
AI Automation
Routine, low-risk decisions handled automatically under rules you set and can audit.
Decision Assistance
A working partner for the decision, not a replacement for the person accountable for it.
What the AI layer actually does.
Every feature below runs on the same governed foundation: your data, your access rules, your audit trail.
Enterprise assistant
- Plain-language questions answered from live ERP data, with tables and interactive charts
- Natural language to SQL over uploaded tables and canonical business data
- Grounding check: numbers that cannot be traced to a source are not shown
- Live reasoning trace you can open and read
- Missing details asked in conversation, with pre-filled approval forms
- Voice input and read-aloud in Turkish
Knowledge
- Document question answering with a tenant-isolated hybrid search index
- One multilingual query across documents, conversation history, the knowledge graph and the data fabric
- Knowledge graph that discovers join keys between tables and asks for approval
Agents and automation
- Department agents that run named steps with explicitly granted tools
- Agent memory that recalls earlier episodes
- A dedicated model per agent, trained and bound at runtime
- Supervisor routing, cross-department sagas and chained quotations
- Drag-and-drop workflow canvas the assistant can run
- Decision tables (DMN) and business rules with recorded rationale
Open to other agents
- MCP server exposing the assistant tool families to agent and LLM clients
- WebMCP tools for browser agents
- Agent discovery through llms.txt, an API catalogue and an MCP card
- Slack, Teams, WhatsApp and Signal channels with an approval inbox
Trust and governance
- Department-based access policy with personal data masking
- Prompt firewall and injection detection with security findings
- Explainability, confidence and risk scores with decision rationale
- Retrieval evaluation, LLM-as-judge, prompt tests, A/B and regression checks
Runs where your data lives
- Fully on-premise models, or models running inside the browser
- Model tiers matched to task difficulty, from small to reasoning models
- Fallback chain from cloud providers to local models to a response cache
- Cost-aware routing and exact token counting
- LLM telemetry: traces, latency and cost
AI is a component of Decision Intelligence, not the whole product.
A model on top of fragmented data produces confident nonsense. AI only earns its place at the fourth step of the chain — after the data is connected, related and predicted.
ERP, POS, MES, WMS, CRM and IoT — into one place.
Operational data related and put in business context.
Demand, stock, production and performance, ahead of time.
Decision models and AI weigh the options with you.
Decisions executed through workflow and automation.
Explainable AI, because an unexplained answer cannot be approved
AI decision making fails in enterprises for a reason that has little to do with model quality: nobody will sign off on an action they cannot explain to the person who asks why. An accurate recommendation without its reasoning is, operationally, an unusable one.
Every answer Veraius produces carries its sources and its chain of reasoning. The AI reads data that has already been connected and contextualised, states what it concluded and from which records, and leaves the judgement with the person accountable for it. Claims that cannot be grounded in your systems are not made.
That is also what makes the output auditable months later. When a decision is questioned, the record shows what was known at the time, what was recommended, who approved it and on what basis.
What your teams gain from Veraius agents.
Budget against reality
Planned and actual spend are compared by cost centre, and variances above your threshold are escalated with their cause.
See the Finance 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 IT OperationsChanges approved by risk
Low-risk changes move fast; high-risk ones go to the change board with the evidence already gathered.
See the IT Operations 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 questionHow is the AI explainable?
By constructing the reasoning rather than extracting it. Feature importance tells you which inputs a model weighted; explainable AI for business has to say which past case it reasoned from, what that case implied, what constraint bound the answer, and what would have changed it. A planner can argue with the second and not with the first.
Can the AI make up facts or figures?
The reasoning is grounded in retrieved records rather than generated from a prompt, and every figure in an explanation traces to a row someone can open. Where the data does not support a claim the system says so instead of producing a plausible sentence — which is the failure mode that ends deployments.
How do you choose the models behind the AI layer?
Whichever the data supports, tested against the decision rather than a benchmark. A model that beats another 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.