Transforming support for smart city platform clients
At a glance
Industry
SaaSUse case
Enhancing customer support with an intelligent assistantGoal
Resolve customer issues and onboard new staff more efficientlyLlama versions
Llama 3.2 Vision 90B, Llama 3.3 70BDeployment
Oracle Cloud Infrastructure (OCI)
Improving quality of life with software that makes cities smart
Exati is Latin America’s leading provider of smart city operations software serving over 700 municipalities in six countries and in three languages. The Exati ecosystem helps oversee streetlights, roads, trees and waste from 10 million assets and 300,000 IoT sensors across urban operations like road maintenance, street cleaning and forestation. With Exacti, cities and their contractors can manage urban infrastructure and deliver services efficiently.Making it faster for customer support to access information and resolve requests
The Exati smart city platform ingests vast streams of unstructured video, audio, photos and text data from IoT sensors embedded across urban environments. It transforms this raw data into actionable intelligence for city governments and contractors — supporting a wide range of roles.Exati customer support agents answer over 1,000 requests a month, many of which require them to search for information through fragmented data sources, including lengthy video recordings and dense documentation with visual elements. Support agents often need to sift through these resources during live customer calls, causing delays in case resolution and resulting in a suboptimal customer experience.
A Llama-based intelligent customer support assistant
Teaming up with Oracle, Exati implemented a Llama-backed AI solution that processes information about smart cities and company software and provides live assistance to customer support agents. Support agents can ask questions in natural language and receive succinct syntheses of multimodal information — text documents, video feeds, still images, graphs and diagrams — in Spanish, Portuguese and English.
Llama provides multimodal understanding and real-time agent support
The solution uses Llama 3.2 Vision 90B and Llama 3.3 70B to process documents and extract key information. The Llama 3.2 Vision 90B model generates detailed, context-aware image descriptions to infuse responses with a more nuanced understanding of documents with illustrations. The Llama 3.3 70B model creates concise, insight-rich text summaries and reports from text and visual data sources, helping support agents get the information they need to facilitate real-time customer interactions.When a support agent submits a query, the system enhances it through prompt engineering and single-shot learning, then retrieves the most relevant information using Sentence Transformers and Facebook AI Similarity Search (FAISS). This context is combined with the original question and sent to the large language model (LLM) to generate precise, real-time responses. Visual data is handled similarly, with Llama 3.2 Vision producing descriptive embeddings that can be integrated directly into the retrieval-augmented generation (RAG) pipeline.
Saving time and elevating trust with streamlined customer support
The solution significantly reduces the average response time for support agents and enhances the accuracy of information passed on to customers. This saves time and money while creating more satisfied users, ultimately enhancing Exati’s reputation and improving customer trust.Before implementing the AI solution, handling a support case took an average of 63 minutes — now it takes just 38, saving 25 minutes per case. Onboarding time also dropped from 12 weeks to under 9 weeks. 
