How to reduce an AI project timeline by 30% and lower technology risk

The client was developing an AI solution for the automatic classification of large volumes of communication, including potentially risky content such as spam, abuse, and unwanted content.

The project had reached a stage where technology decisions had a direct impact on:

• implementation time,
• operational costs,
• solution stability in the production environment.

Three key risks were identified from both a business and technology perspective:

1. Lack of Production AI Experience

The team had strong software development skills but no experience in designing and deploying end-to-end AI models in a production environment.

2. Time Pressure

The project was linked to further product development, which meant a clearly defined timeline and limited room for iteration.

3. Risk of Technology Decisions

Poor decisions made at the beginning of the project could lead to:

• greater system complexity,
• higher maintenance costs,
• the need for refactoring after deployment.

The client considered building the required capabilities internally. However:

• recruitment in this area is time-consuming,
• experienced end-to-end AI Engineers are a limited group,
• the risk of a skills mismatch is high,
• the project required immediate support.

In this situation, recruitment was not the best option.

The client decided to add the missing capability to the team through an on-demand expertise model.

The goal was to:

• speed up technology decisions,
• reduce the risk of mistakes at the start,
• shorten the time needed to move into production.

An experienced AI Engineer joined the team and:

• entered the project at the technology decision-making stage,
• took responsibility for the direction of the solution architecture,
• proposed a simpler and more efficient approach to model implementation and deployment.

The key point was that his role was not simply to “support the team”, but to fill a critical capability gap.

The expert’s involvement led to clear and measurable results:

• a simpler system architecture,
• shorter model training time,
• faster deployment to the production environment,
• lower operational costs,
• reduced technology risk in the next stages of development.

• project delivery time reduced by around 30%, from three months to two,
• faster production readiness,
• no need for post-deployment fixes,
• lower solution maintenance costs.

In AI projects, a larger team is not always the main advantage.

It is often more important to add the right capability at the exact moment when it has the greatest impact on the direction and pace of delivery.

The on-demand expertise model makes it possible to:

• speed up project delivery,
• reduce technology risk,
• improve the quality of decisions at an early stage.