Amal Mohsen, Head of Customer Care & Success, Orisha Commerce

For the past few months, Amal Mohsen has been spearheading a strategic initiative: Fastbot, the new conversational voice assistant designed for Fastmag software.

By enabling quick calls and instant resolutions without disrupting the user’s workflow, Amal and her teams have developed a high-performance tool tailored to the needs of retailers on the ground. Integrated directly into the Interactive Voice Response (IVR) system, this callbot provides Fastmag clients with step-by-step guidance on software usage and best practices. 

The primary objective is to automate routine inquiries, allowing support agents to pivot their focus toward high-value customer experience and personalized assistance.

1. Understanding and eliminating customer friction

What are the most frequent customer pain points today, and how does a tool like Fastbot provide a concrete solution to them?

The friction points most often cited by customers are actually quite basic:

• slow response times, 
• imprecise answers, 
• hesitation when providing information, 
• Documentation that is simply too long to digest quickly.

Fastbot delivers immediate answers, eliminating wait times and call queues entirely. It leverages a real-time knowledge base and our internal protocols to provide clear, accurate responses. 

By removing the guesswork often associated with manual information searches, the bot can identify user intent, locate the relevant technical documentation, and formulate a reply in seconds. 

This prevents the back-and-forth typical of vague answers. Instead of forcing a customer to sift through a multi-page manual, Fastbot extracts the essential details and presents them concisely, saving time for both the client and our team.

What do customers value most when interacting with the bot?

First and foremost, customers appreciate the immediacy. By eliminating wait times, Fastbot has radically transformed their perception of technical support. Users also highlight that the callbot gets straight to the point it identifies the intent, provides the most relevant answer, and moves on. This efficiency is a major convenience.

Another recurring feedback point is the bot’s infinite patience; it can repeat, rephrase, or detail information as many times as necessary without ever showing signs of fatigue. 

Finally, customers have noted the vocal fluidity and the sheer quality of the responses. With a natural tone and clear information, the interaction increasingly mimics a standard conversation, which significantly boosts user adoption and trust.

ion.

2. Measuring the bot’s effectiveness

What percentage of routine inquiries can currently be handled by the bot?

Currently, Fastbot manages approximately 30% of simple requests a figure that is steadily rising as the system learns. 

This growth is driven largely by our Customer Care teams, who are transitioning into “AI coaches.” They train the bot, refine its responses, expand its knowledge base, and fine-tune its conversational communication style. Every week, Fastbot becomes more accurate and capable of handling a higher volume of queries.

How is the bot freeing up or how will it eventually free up valuable time for your team?

Fastbot absorbs all “mechanical” requests those requiring basic technical data without an emotional component. These inquiries previously accounted for a large portion of our volume and tied up our staff unnecessarily. 

By automating these tasks, we empower our employees to focus on high-impact work: consulting, advisory services, and managing sensitive or complex situations. This is where their emotional intelligence becomes indispensable, allowing them the time to truly listen, reassure, and guide our clients.

3. Transforming the support model

How did the teams respond to the integration of the bot?

The reception has been overwhelmingly positive. We maintained regular communication about the project, discussing not just with customer relations teams but with the entire company about how AI is reshaping our professional lives.

The role of the agent is evolving; they are becoming orchestrators and managers of the AI. By positioning themselves as leaders in customer relations, this shift validates their expertise and amplifies their overall impact.

What best practices would you recommend to other support teams looking to deploy a bot?

The first priority is education for both clients and staff. Everyone must understand the bot’s purpose, how it functions, and its role in the support journey. Secondly, do not wait for a perfect product.

We are in an era where iterative testing and adjustment are far more valuable than perfectionism. Launching quickly allows for real-world learning, which accelerates improvement.

Finally, it is crucial to involve your most motivated team members. They are the ones who will drive the bot’s success by enriching the knowledge base, setting behavioral guidelines, and automating the initial use cases.

4. Where is Fastbot going? The evolution of customer service

What developments do you foresee for the bot in the coming months?

Future developments are focused on creating true synergy between humans and AI. This involves implementing a seamless experience for escalation, where the bot handles what it can and then instantly transfers the call to a human expert when the context demands it without any disruption for the client.

We also plan to roll out highly operational new features. These include outbound follow-up calls after an incident is resolved to ensure continued satisfaction, as well as appointment scheduling, automated Customer Relationship Management (CRM) updates, order validation, and ticket creation directly from the conversation.

Another major step is the generation of structured interaction summaries, which provide immediate value to both the client and the support team. This saves time and ensures highly reliable traceability. Notably, all these use cases are technically possible today.

What new use cases could further enhance the customer experience?

The next major milestone is personalization. The bot will eventually be able to track interaction history, recognize returning clients, and remember their specific preferences or previous inquiries. 

This “memory” will allow the callbot to automatically adapt its tone, answers, and suggestions. Rather than starting from scratch with every exchange, it will be able to provide meaningful context to every conversation