Quickscan
Silvant.AI implementation partner

Every founder or director knows AI and agents will become part of the company. The question is no longer if, but how.

Silvant is your AI implementation partner. We build AI agents and custom AI software around existing processes and systems.

For support, operations, finance and reporting. With integrations into CRM, ERP, ticketing, databases and the tools your team already uses.

Support emailSilvant agentAction completed
Example workflow

From signal to controlled action.

Choose a process and see which information the agent uses, which action it prepares and where a team member decides.

Example scenario, not a client case

Handle a support request with context

The agent collects customer and order information, prepares the next action and leaves exceptions to a team member.

Process is playing
Step 1 / 5Support email

A customer asks about a delayed order and wants to know what happens next.

From question to working software

Choose first. Then build.

An AI project does not start with a model choice. It starts with a process where enough time, errors or delays make improvement worthwhile.

Implementation overviewOpportunity assessed
First resultA go/no-go, priority and scoped first direction.

Process and bottlenecks recorded

Done

Systems and data sources inventoried

Done

Value, risk and owner assessed

Decision

We do not build when process, data or ownership are still insufficiently clear.

Built for the existing stack

Connect where the work already happens.

An agent only becomes useful when it can read and write in the systems your organisation already uses, with control.

Examples of system categories

These are categories, not partner or certification claims.

CRM

Customer, account and activity context

ERP

Orders, finance and operational status

Ticketing

Requests, ownership and history

Workspace

Microsoft 365 and Google Workspace

Data

Databases, warehouses and files

APIs

Internal and external services

Controlled into production

Scoped permissions

The agent only receives access to the information and actions required for the process.

Human approval

Risky or customer-sensitive actions receive an explicit control point.

Recorded actions

Executed, rejected and escalated actions remain traceable.

Test first

Integrations and exceptions are tested in a scoped environment before launch.

Clear owner

An accountable person is assigned for content, exceptions and escalation.

Post-launch management

We monitor quality, usage and integrations as data or processes change.

When Silvant fits

There must be real work to improve.

Silvant is for organisations implementing AI in an existing process, not for a standalone demo without an owner or clear application.

Good fit
  • A recurring process with enough volume or impact.
  • Usable data in existing systems.
  • A subject owner who can make decisions.
  • Users who can test with real examples.
Not a fit
  • Only wanting a general chatbot.
  • Not yet being able to identify a process or problem.
  • Nobody available for decisions and testing.
Frequently asked questions

Practical answers before we begin.

You should not need a sales call to understand the basics. These questions determine whether a first conversation makes sense.

An agent can retrieve information from multiple sources, make a recurring assessment, prepare or execute an action and escalate exceptions. The exact scope depends on the process, data quality and the risk of the action.

First step

Which process deserves attention first?

In the Quickscan, we map the process, systems, data, risks and a logical first build step.

Starting point
One concrete process
Output
Improvement plan and first scope
Next step
Only when building makes sense