What we built: AI-powered transformation of operational support

A major agribusiness organization sought to improve the efficiency of its help desk operations, a critical component for resolving operational questions and client requests.

As the organization expanded, the number of support requests grew rapidly. However, the support structure still relied heavily on manual tools and fragmented knowledge sources.

The goal was to explore how artificial intelligence could transform support operations into a faster, smarter, and continuously learning system.

The challenge: When organizational knowledge is scattered

The support team faced a common issue in large organizations: repeated questions and limited visibility into previously solved problems.

Most resolutions relied on tools such as:

• Excel spreadsheets used as operational trackers.

• Limited decision-tree knowledge bases.

• Chat conversations with clients.

• Long email threads.

• Informal notes and reminders.

As a result, valuable knowledge generated during problem resolution was often lost or inaccessible.

This created several operational inefficiencies:

• duplicate efforts across multiple agents.

• lack of visibility into recurring issues.

• unnecessarily long resolution times.

The challenge was to convert fragmented information into reusable knowledge.

CamelidOS: an assistant that turns support into learning

CamelidOS was designed as an intelligent conversational assistant that helps resolve support queries while simultaneously building a dynamic knowledge base.

The interaction flow is simple:

1. Operators interact with CamelidOS through a chat interface.

2. The system attempts to resolve the request in real time using available knowledge.

3. If no solution is found, it automatically generates a diagnostic report.

That diagnostic is then sent to a task management platform where the issue is documented, categorized, and assigned for resolution.

System design

From isolated incidents to shared knowledge

Every incident becomes an opportunity for the organization to learn.

The system enables teams to:

• Group similar incidents.

• Document solutions in a structured way.

• Assign owners with full progress visibility.

• Centralize support knowledge.

CamelidOS continuously analyzes updates and comments generated during case resolution and incorporates them as new knowledge.

Each solved issue, therefore, improves the system’s ability to respond in the future.

Operational impact

More capacity to focus on complex challenges

The initiative demonstrated significant potential to improve help desk efficiency.

Key outcomes include:

• 233% increase in agent capacity for complex issues.

• Zero lost incidents thanks to full traceability.

• Over 80% of queries are resolved automatically.

Support operations evolve from reactive problem-solving into a system that continuously learns and improves.

What comes next

From help desk assistant to knowledge platform

CamelidOS lays the groundwork for a broader knowledge platform.

As the system learns from more interactions, it can evolve beyond answering questions to identifying operational patterns and recommending improvements across the organization.