AI implementation and AI automation
We embed AI into specific processes: handling inquiries, knowledge, content, documents, analytics, and operations.
Business objective
For companies that want to move from scattered AI experiments to working use cases in sales, marketing, support, and operations.
What is included
How the work is structured
- 01
Use case
We select an objective with a clear owner, data, frequency, and current operation cost.
- 02
Prototype
We test quality on a limited data set and define acceptance criteria.
- 03
Implementation
We connect sources, the interface, roles, logging, and manual control scenarios.
- 04
Scaling
We compare impact, cost, and risk, then extend the solution to additional processes.
What we will need from you
Before launch: AI implementation
01Which AI use case is best to start with?+
A recurring objective with examples of input data, a clear reference result, and a measurable cost for the current manual work.
02Can internal company documents be used?+
Yes, after access, storage, anonymization, and permitted-source rules have been defined for the specific solution.
03Will AI completely replace an employee?+
The aim is to reduce routine work and improve decision quality. Responsibility, non-standard cases, and critical actions remain with the team.
Describe the current system and desired result
We will clarify the objective, available data, constraints, and the first stage's format.