Informa Tech faces the challenge of effectively analysing and extracting meaningful insights from customer feedback surveys collected during their events. Their goal is to improve their overall Net Promoter Score (NPS), a key metric that measures customer satisfaction and loyalty.

Whilst the customer team had experimented with ChatGPT, they recognised the opportunity to leverage the capabilities of large language models (LLMs) on AWS, to further enhance their analysis capabilities and expand into new markets. By exploring the potential of LLMs, they aimed to harness the power of advanced natural language processing and generative AI, to unlock deeper contextual insights from customer feedback data, and provide meaningful summarisations, enabling them to make data-driven decisions that enhance customer experiences, on a much broader scale.

This presents an exciting opportunity for the customer to optimise their understanding of customer sentiments, identify actionable areas for improvement, and ultimately, elevate their NPS.

Informa Tech chose Firemind as their trusted partner for several compelling reasons. As an AWS Advanced consulting partner, Firemind has gained global recognition as one of the most accredited AWS partners in EMEA, signifying our deep expertise and proficiency in leveraging AWS AI/ML services.

Firemind’s knowledge and experience in generative AI, AI/ML, and modern data strategies, made them the ideal choice for the customer’s Gen AI solution. Their expertise extended beyond technology, encompassing crucial aspects such as UX/UI design. This comprehensive skill set ensured that the solution delivered by Firemind would not only be technically robust but also provide a user-friendly and intuitive experience for the customer’s team.

Firemind’s customer-focused approach and agility were key factors in their selection. They demonstrated a commitment to understanding the customer’s specific needs and working within their timeline.

The implementation journey from discovery to handover was completed in 3 weeks. It began with an introduction to work alongside the customer AWS account team, which quickly transformed into a comprehensive discovery process with the customer’s team and our co-founder. Following this initial engagement, the project seamlessly transitioned through the customary stages, including scoping the requirements, defining the Scope of Works, and obtaining project sign-off.

The delivery process began with regular check-ins for effective communication and collaboration. Steps such as solution deployment, rigorous testing, and knowledge transfer sessions were carefully executed. Diligent follow-ups ensured a smooth transition to the operational phase.

How it worked

The solution Firemind crafted involved a streamlined workflow leveraging AWS services. The LLM UI makes a RESTful call to an Amazon API Gateway, obtaining a pre-signed Amazon S3 URL. Using this URL, the LLM UI securely uploads Informa Tech’s feedback document to Amazon S3. Upon landing in S3, an AWS Step Function is triggered, automating data cleansing, LLM execution, and output storage.

The customer can access the processed file in S3 directly, or query the data using AWS Athena. Firemind’s LLM UI provides a powerful user interface for viewing and analysing the results.

Powered by Amazon Bedrock (a foundational model API service), this solution (we call PULSE) enables the customer to efficiently process customer feedback, gain valuable insights at scale, and support data-driven decision-making.

To discover more about our PULSE LLM tool, as well as our generative AI services, click here.

Designed with ease of use in mind, the interface enabled Informa tech’s team to interact effortlessly with the Gen AI solution. The intuitive navigation and visually appealing design simplified workflows and improved overall productivity. Team members could easily access and leverage the AI solution’s capabilities, facilitating faster decision-making processes.

The efficiency gained from automated feedback analysis allowed Informa tech to streamline their processes and allocate resources effectively. The improved accuracy and efficiency in feedback analysis were key factors in their ability to respond to customer needs promptly and enhance their events to meet (and exceed) customer expectations.