Czech financial savings financial institution Česká spořitelna, a division of Austria’s Erste Group, lately collaborated with AI resolution builder DataSentics to discover the usage of GenAI in name facilities. Česká needed to enhance high quality management and optimize prices of their inbound name heart operations, which obtain round 2 million calls per 12 months. They selected the Databricks Knowledge Intelligence Platform to experiment with each inside and exterior AI fashions to evaluate the effectiveness of name heart brokers.
Exploring a High quality Management System for Buyer Assist
The decision heart workforce at Česká spořitelna needed to check a top quality management system powered by GenAI that may be sure that brokers adhere to scripted tips throughout buyer interactions. A important problem for Ceska was guaranteeing constant agent communication for routine buyer inquiries. When prospects name about account balances, brokers must direct them to on-line banking options, a key enterprise requirement that drives digital adoption and operational effectivity. The assist workforce wanted a scalable method to confirm agent compliance and keep communication requirements throughout 1000’s of buyer interactions. To attain this, the workforce started through the use of Whisper, a speech-to-text mannequin from OpenAI, to transcribe conversations precisely. The problem was to provide human-readable textual content that precisely represented spoken phrases utilized by name heart brokers with out distorting their that means. The transcriptions wanted to make logical sense and replicate the intent of the dialog precisely for additional evaluation.
Following the transcription, the workforce explored integrating each inside GPT fashions and open supply fashions corresponding to Mixtral to guage their effectiveness. GenAI fashions have been examined in a simulated QA function, the place they have been tasked with answering particular questions corresponding to “Did the agent redirect the client to on-line banking?”. The purpose of this train was to evaluate how effectively these fashions might mimic human understanding and decision-making when verifying compliance with established tips. By evaluating the efficiency of each the inner GPT mannequin and the open supply fashions, the workforce aimed to search out the simplest resolution for enhancing customer support via automated AI-driven high quality management.
Advantages of the Databricks Knowledge Intelligence Platform for GenAI
The DataSentics workforce evaluated a number of choices for this resolution, and in the end selected to deploy the Databricks Knowledge Intelligence Platform and Mosaic AI instruments at Česká spořitelna for a number of causes:
- Knowledge Administration and Governance Advantages: Unity Catalog makes knowledge simply accessible for various fashions whereas maintaining delicate knowledge beneath restricted entry.
- Complete Knowledge Processing Capabilities: the Databricks Platform helps the complete workflow of preprocessing of name heart knowledge, from transcription to high quality management. This permits us to provide intermediate outcomes that may be leveraged for different fashions and tasks, corresponding to advertising and marketing, danger evaluation, regulatory compliance, and fraud detection.
- Mannequin Coaching and Assist: Databricks offers strong assist and experience for GenAI, together with mannequin structure and coaching capabilities. This made it an excellent platform for testing and deploying open supply fashions rapidly, enabling us to experiment and iterate effectively.
- Ease of Cluster Creation: With Databricks, it’s simple to create clusters and deploy open-source fashions. This streamlines the experimentation course of and permits us to focus extra on mannequin efficiency and fewer on infrastructure administration.
Insights and Outcomes
All through the challenge, we experimented with numerous segmentation methods and gathered a number of worthwhile insights:
- High quality of Enter Knowledge is Essential: The standard of the audio recordings various from shopper to shopper, with some talking quietly or from a distance, which may later have an effect on the accuracy of the transcription. Whisper or comparable programs may help remedy the issue.
- Class Definition is a Should: We discovered that if classes can’t be simply outlined for people, it’s equally difficult for LLMs to grasp them. This strengthened the necessity for clear and exact class definitions to coach the fashions successfully.
- Open-Supply Fashions Ship Outcomes: Open-source fashions demonstrated that they might compete successfully with proprietary fashions like ChatGPT. This discovering is important for companies trying to optimize prices whereas nonetheless reaching high-quality outcomes.
What’s Subsequent
With GenAI instruments powered by Databricks Mosaic AI, Česká spořitelna staff are actually capable of acquire entry to solutions present in a spread of paperwork by way of “good search” performance. For instance, the buying workforce could must seek the advice of a whole lot of pages of course of documentation on find out how to management and approve funds to totally different international locations. Earlier than leveraging Databricks, it will take staff hours to search out the proper info they want. Now, RAG-powered search provides staff solutions inside seconds, together with citations and hyperlinks to the supply doc.
Trying forward, there are many alternatives to discover extra GenAI workloads at Česká spořitelna. We purpose to create a sturdy integration between Databricks and Česká spořitelna’s inside database name heart recordings. This can unlock new use instances corresponding to churn detection, sentiment evaluation, and gross sales sign detection since Databricks is the go-to platform for streaming knowledge. These day by day studies will permit Česká spořitelna to react to modifications in actual time whereas reaching price reductions with improved high quality assurance of their name facilities.
This weblog submit was collectively authored by Petra Starmanova (Česká spořitelna), Tereza Mokrenova (DataSentics), Dalibor Karásek (DataSentics) and Joannis Paul Schweres (Databricks).