From Complexity to Clarity: Understanding & Creating the Ultimate AI Service Co-Pilot

Tim Burge
From Complexity to Clarity: Understanding & Creating the Ultimate AI Service Co-Pilot

The era of AI co-pilots is upon us, and it’s nothing short of revolutionary. These digital assistants are unlocking new possibilities and reshaping the way we work. 

But with so many AI solutions on the market, how can you be sure you’re getting the best results?

Amidst this AI boom, you’ve probably come across countless guides on developing a service AI co-pilot of your own—and it’s perhaps tempting to give it a go. But while it may seem straightforward, building a service AI co-pilot that stands out requires deep understanding and careful execution. 

Let’s explore the process and some challenges you should consider every step of the way. 


The steps and challenges of creating an AI co-pilot for service

The phases of creating a co-pilot for service. Click to enlarge.


Step 1: Load content.

  • Challenge #1: Content in multiple forms, data types, and sources.
  • Challenge #2: Content owners need to be service experts—and they usually are not.


As you build your AI co-pilot, you’ll quickly realize the sheer volume and diversity of content you need to manage. The complexity can be overwhelming, whether it’s regarding the different data types or various sources. Additionally, many of those contributing to your project might not be experts in these solutions, making it even more challenging to distinguish what’s crucial from what’s not. 


Step 2: Make it searchable.

  • Challenge #3: Documents are more than just text.
  • Challenge #4: There may be multiple documents for one product.


Service documentation is notoriously complex, often spanning thousands of pages filled with intricate terminology, detailed diagrams, and comprehensive schemas. 

But it doesn’t stop at text—there are headers, footers, notes, and more. To complicate matters further, each document may pertain to multiple products and purposes.

The good news is that Service Co-Pilot excels at understanding the nuances within these documents. It identifies intelligent boundaries, providing only the essential recommendations from various technical resources. This goes beyond the simple “Eureka! I found the document I needed.” With the co-pilot, you get the precise snippet of information you need in seconds, saving you time and helping you focus on what truly matters.


Step 3: Keep it updated.

  • Challenge #5: Streamlining data sources for continuous improvement and creating feedback loops.


If you built your tool yesterday, its information may not be accurate today. 

In the real world, manuals and machine specifications are constantly evolving and being updated. This continuous change often leaves service teams playing catch-up. Ensuring your AI co-pilot stays current with these updates is crucial. 

By streamlining data sources and establishing robust feedback loops, your AI co-pilot can adapt in real time, providing the most accurate and up-to-date information. This dynamic approach keeps your service team ahead of the curve, ready to tackle any new challenge that comes their way.


Step 4: Search in natural language format.

  • Challenge #6: Making machines process queries just like a human would.


When problems are reported, the language used can vary widely. For example, a vacuum pump issue might be described as “vacuum pump failing,” “vacuum pump not working,” or simply a
“broken pump.” A basic word search would treat these as different problems, but we need a more sophisticated approach to make the AI system understand them like a human would.

By combining semantic search, which focuses on the meaning, with lexical search, which focuses on the words, we achieve a hybrid approach that captures the full context. For the Aquant Service Co-Pilot, we developed straightforward, unambiguous questions that provide context to the language model. Our AI engine can then adapt to different personas or user languages, ensuring it understands the nuances of each situation just like a human would.


Step 5: Show “the answer.”

  • Challenge #7: Getting to the point and highlighting what matters. 


It’s not just about sifting through thousands of resources to find what’s relevant; it’s about making proper recommendations at the right time. To deliver a precise answer, we first process the question to add context and relevance. Then, we perform a combined semantic and lexical search to retrieve the most specific and pertinent information. Finally, we use large language models (LLMs) to generate a summarized, concise answer. This approach ensures you receive not just any answer, but the best possible one tailored to your needs.


Step 6: Make it explainable.

  • Challenge #8: Building trust by avoiding “the black box.”


No one trusts a “black box” solution that provides answers without transparency. We need a system that gives accurate answers and guides us through the sources, building trust. Aquant Service Co-Pilot excels at this. It ranks recommendations based on relevance and usability, providing direct links to the specific parts of the information. This way, you can see exactly where the information comes from and trust the guidance you’re receiving.


Step 7: Ensure its accuracy. 

  • Challenge #9: Creating a feedback loop for continuous improvement.


Building an AI co-pilot isn’t just about creating a tool for yourself; it’s about crafting a solution that works for everyone. Without a robust feedback framework, maintaining accuracy becomes a challenge. 

That’s why we prioritize continuous improvement at every step. We’ve developed a feedback system that makes it easy to refine and enhance the AI’s performance over time. Users can upvote, share comments, and suggest changes in real-time. Meanwhile, admins and experts can review all suggestions to validate and ensure their accuracy. This collaborative approach ensures that the AI co-pilot evolves and delivers even better results as time goes on.


Step 8: Make it compliant and secure.

  • Challenge #10: Protecting organizational data and users.


Service activities can often be complex, demanding extra safety precautions. Ensuring transparency, auditability, and a guided experience becomes mission-critical. 

At Aquant, we take data security seriously. We protect your data through secure integrations and provide a comprehensive audit and reporting environment. This allows you to monitor usage and feedback effectively, giving you peace of mind and maintaining the highest safety and transparency standards.


Using a Co-Pilot you can trust

Even when it seems straightforward, building, maintaining, and continuously improving a unique experience for every service interaction requires significant effort. 

At Aquant, we’ve been helping companies achieve this for over seven years, witnessing transformational results firsthand. We’ve created a solution that addresses real-world needs by bringing together top behavioral scientists and data science experts.

Curious to see Aquant Service Co-Pilot in action? 

Sign up for our 7 Day Challenge and discover how to elevate your service business with AI.


About the Author

Tim Burge, Director of AI Solutions, Aquant

For over 15 years, I have used advanced data technologies to help people solve real-world business problems. I have worked in both the technology and agency worlds, focusing on bridging the gap between business and technology and assisting organizations in becoming more data-driven.