Silvant / What we build
An agent system your team can delegate work to.
Silvant builds AI agents that use business knowledge and system access to execute work. Employees give instructions from their own role, without technical knowledge. Agents can also respond independently to an enquiry, a schedule or a change in a system.
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Support resolution
Execution & exceptions
Customer and order matched · carrier confirms delivery tomorrow
Customer updated through the support system; reply and source recorded in the ticket.
Resolved under service policyRequested refund falls outside standard policy
Case and order history sent to the responsible colleague.
Approval requiredIllustration of a possible implementation, not a live client system.
Tickets resolved, causes investigated and a new check created by the support team.
Work you can delegate to agents
- Research in your business data
- An employee asks why deliveries are falling behind. Agents combine orders, supplier messages and commitments, investigate exceptions and show their sources.
- Take action
- After research, agents can send messages, update records and start follow-up work. Silvant builds the actions and checks needed to do that.
- Reuse a way of working
- An employee turns a useful instruction into a recurring check or analysis. They describe what matters; technical building skills are not required.
Agents, fixed software and responsibilities
For each process, we choose what an agent may assess and what fixed software must determine. In invoice processing, AI can interpret a document while amounts and approval thresholds are checked with fixed rules. Different agents can have their own roles and permissions.
Autonomy is part of the design
We explicitly define which cases can be completed independently. A routine email has different rules from a payment or contract change. The system receives only the access it needs and stops or escalates when conditions are not met. Your team sees the decision required and the supporting information.
Test the complete workflow
We assess more than the quality of a model's answer. We test whether it uses the correct source, chooses an appropriate action, writes the right data and recovers after a system failure. A controlled rollout lets us monitor behaviour before increasing volume or permissions.
Resolving enquiries and investigating recurring complaints.
A colleague asks: ‘Which complaints about product X keep coming back this month? Investigate what is happening.’ They add what the warranty covers for this product. The agent system groups tickets, examines order data and collects sources. The colleague makes this a weekly check without building technical settings.
Explore execution, system access and exceptions
- Systems involved
- Ticketing · CRM · Order records · Shipping data
- How the work happens today
- Employees retrieve customer and order data for each ticket. A recurring complaint often stays scattered across individual conversations while the same enquiries keep arriving.
- What the agent executes
- The agent matches the message to the customer, checks order and shipping records and applies the agreed service rules. A routine enquiry supported by clear information is answered and recorded. A return can trigger its workflow; an exceptional refund requires approval.
- What the employee does
- A colleague asks: ‘Which complaints about product X keep coming back this month? Investigate what is happening.’ They add what the warranty covers for this product. The agent system groups tickets, examines order data and collects sources. The colleague makes this a weekly check without building technical settings.
- Limits and recovery
- The agent does not invent a delivery date that the carrier has not provided. Conflicting records, sensitive complaints or commitments outside policy create a review task. A failed message delivery does not incorrectly mark the ticket as resolved.
- What Silvant builds
- Silvant builds customer identification, ticketing and order access, service handling and checks on commitments. The team can use that knowledge and those actions for its own research and new ways of working.
Support resolution
Execution & exceptions
Customer and order matched · carrier confirms delivery tomorrow
Customer updated through the support system; reply and source recorded in the ticket.
Resolved under service policyRequested refund falls outside standard policy
Case and order history sent to the responsible colleague.
Approval requiredIllustration of a possible implementation, not a live client system.
- 01
Request and context
A customer enquiry or an employee's instruction starts the work.
- 02
Investigate
Agents use tickets, orders and the team's service agreements.
- 03
Resolve
Routine enquiries are answered; an exceptional refund requires a decision.
- 04
Extend the routine
An employee turns complaint research into a weekly check.
Practical questions
How is this different from conventional automation?
Fixed rules work well for predictable actions and calculations. AI is useful when messages, documents or situations need interpretation. A strong implementation combines them; not every step needs a language model.
Is an agent the same as a chatbot?
No. Chat is an interface. An agent can also react to a new ticket, supplier email, schedule or system change and continue working within the permitted workflow without another chat instruction.
Can our team create new applications themselves?
Yes, using the sources, knowledge and actions we make available in the agent system. A new instruction or check need not be a new development project. An additional ERP integration or new payment permissions still require further design and technical setup.
Silvant · The next step
What would you like to change in your business?
Tell us what is getting stuck or what your team would like to do with AI. No technical specification needed.
Tell us what you need