Automatic Assignment Assistant
Overview
Automatic Assignment Assistant is a system for intelligently distributing tickets and chat messages within a support platform.
It optimizes message handling by automatically routing incoming requests to the right support agents based on workload, message type, and complexity.
Messages that do not require human intervention are redirected to chat-bots, allowing the team to focus on tasks that need real agent attention.
Problem
Before AAA, all incoming tickets and chats were processed manually.
This created uneven workloads:
some agents received too many messages
others received too few or none
As a result, response times were inconsistent, some messages remained unattended, and the overall efficiency of the support team suffered.
Hypotheses
Automation can reduce manual workload.
If we automate message routing and classification, the number of manually processed tickets will significantly decrease, improving overall team efficiency.
Balanced workload increases response speed.
Evenly distributing requests among agents should minimize idle time and reduce the average response time per message.Chat-bot redirection improves throughput.
By identifying routine or low-complexity messages and redirecting them to chat-bots, we can handle a large portion of inquiries automatically — without sacrificing response quality.AI-based message classification improves accuracy.
Implementing AI-driven message analysis will help detect intent, urgency, and complexity more precisely than rule-based logic alone, leading to more effective routing.Transparency boosts team performance.
Making the distribution process visible to all agents will promote fairness, increase motivation, and prevent passive participation.
These hypotheses shaped the development of AAA’s architecture and guided subsequent performance testing and validation.
Prototyping & Flow Design
We began with low-fidelity prototypes based on real support scenarios to visualize automated routing and agent interaction. Using existing design components, we mapped message flows — from classification to assignment and feedback — to identify bottlenecks and test routing efficiency. Once validated, high-fidelity prototypes with AI-driven logic were built for internal testing, confirming both technical feasibility and a clear, seamless user experience.
This iterative process helped validate not only the technical feasibility of AAA but also the usability and clarity of its interface — ensuring the automation felt like a natural extension of the existing support environment.
Automation can reduce manual workload.
If we automate message routing and classification, the number of manually processed tickets will significantly decrease, improving overall team efficiency.
Balanced workload increases response speed.
Evenly distributing requests among agents should minimize idle time and reduce the average response time per message.Chat-bot redirection improves throughput.
By identifying routine or low-complexity messages and redirecting them to chat-bots, we can handle a large portion of inquiries automatically — without sacrificing response quality.AI-based message classification improves accuracy.
Implementing AI-driven message analysis will help detect intent, urgency, and complexity more precisely than rule-based logic alone, leading to more effective routing.Transparency boosts team performance.
Making the distribution process visible to all agents will promote fairness, increase motivation, and prevent passive participation.
The solution
AAA uses a combination of rules and AI-driven analysis to distribute messages automatically:
messages are classified by type, category, and complexity
workload is balanced across all agents
chat-bots handle routine messages, filtering and responding automatically
This approach ensures fair distribution, prevents passive participation, and allows the team to work efficiently within the support system.
AAA demonstrates how intelligent automation can transform support operations.
By combining rule-based routing, AI classification, and chat-bots, the system improves efficiency, ensures fairness, and significantly reduces response times - allowing support teams to focus on high-priority tasks while routine requests are handled automatically.
Impact
The introduction of AAA led to measurable improvements:
average handling time per message decreased by 28%
approximately 42% (around 6194) of incoming messages were automatically redirected to chat-bots
workload distribution became balanced and transparent
response efficiency and team performance improved significantly