Proven work, recognisable patterns.
First we show a real executed project, anonymised. Then we show example scenarios for other processes and sectors, so it is clear where the same approach may apply.
E-commerce as flagship proof.
For an e-commerce company with 20+ employees, we brought dozens of data sources, internal knowledge and recurring workflows together in a custom environment. E-commerce is an important proof area for Silvant because support, operations, stock, reporting and commercial decisions quickly come together there.
E-commerce
20+ employees
Dozens of sources brought together
AI workflows in daily use
What was built
A custom BI dashboard and central data layer replaced loose exports and scattered analytics tools.
Internal knowledge, context and ways of working became searchable and useful for daily questions and decisions.
Customer service, VA workflows, reporting, analytics and recurring operational tasks were supported with AI workflows and automations.
What it delivered
Tens of thousands of euros in direct cost savings.
New sales channels could be opened faster.
Less friction through less searching, fewer context switches and faster preparation work.
The client name intentionally remains anonymous. Measurable amounts are used only where they can be tied to direct savings; broader productivity gains are described qualitatively.
Processes with enough repetition.
The practice case shows how Silvant works when data, knowledge and workflow come together. Below are recognizable process patterns where the same approach often creates value.
Responding faster to customer questions
AI classifies messages, retrieves context from systems and suggests a reply or next step. The team stays in control of exceptions and tone.
More grip on daily planning
An internal overview shows which orders, tickets or tasks need attention. Teams start the day with priorities instead of scattered exports.
Stock, purchasing and reporting
Data from sales, stock and suppliers is brought together in one reliable overview. Decisions are based on current information.
Not client cases, but applicable scenarios.
High volume, clear exceptions
AI can sort messages, retrieve context and prepare draft replies. The team keeps control of exceptions and customer-sensitive decisions.
Daily priorities
Orders, exceptions and status information come together in one overview. That makes it clear faster what needs attention.
Checks before the human review
AI can find anomalies, collect context and prepare suggestions. The decision stays with finance.
Stock, purchasing and timing
AI only becomes useful once stock, sales and supplier data arrive together on time.
Start with the Quickscan.
In a short analysis we look at your processes, systems and data. Afterwards you know where AI can add value and which solution is logical to build first.