About Clarifresh
Clarifresh was born from a simple observation: a surprisingly bland clementine picked from co-founder Avi Schwartzer’s backyard. This moment revealed a significant gap in the market—no comprehensive tool existed that could accurately perform quality control for fruits and vegetables. While digital transformation was revolutionizing other industries, fruit and vegetable quality assessment had remained largely unchanged for decades.
Today, Clarifresh is revolutionizing the fresh produce supply chain from field to fork by digitizing and bringing objectivity to quality control processes. Their innovative solution includes:
- Mobile applications leveraging computer vision to assess produce quality through photos
- Objective measurements of color, size, stem quality, and other critical attributes
- Comprehensive reporting and analytics capabilities
- AI-powered insights for global produce management
Their technology creates favorable revenue opportunities across the fresh produce supply chain while significantly contributing to reduced global food waste.
The Technical Challenge
Clarifresh operates on a sophisticated, modern architecture:
- Client applications (iOS, Android, and web apps)
- Java applications and serverless functions
- MongoDB for transactional database needs
- Specialized infrastructure for training and deploying computer vision models
- Comprehensive monitoring through both Lumigo and Datadog
The company runs 40-50 mission-critical Lambda functions as core components of their solution. Luke Thompson, Chief Architect, leads an engineering team specialized across mobile development, platform/back-end services, and computer vision technologies.
As a growing startup with a complex, serverless-heavy architecture, Clarifresh faced several significant challenges:
- Efficiently monitoring and troubleshooting their distributed serverless infrastructure
- Maintaining visibility into Lambda function performance and dependencies
- Quickly resolving production issues to ensure consistent service reliability
- Enabling team members of varying experience levels to effectively troubleshoot complex issues
Implementing Lumigo for Monitoring and Troubleshooting
With their core applications running on extensive microservice systems, distributed tracing became critical for Clarifresh. They needed the ability to easily trace errors from one service upstream to identify root causes. In Lumigo, they discovered a troubleshooting platform purpose-built for microservices that not only visualized complete end-to-end request traces but also provided detailed request and response data between services. Today, Lumigo offers a comprehensive observability solution combining tracing, metrics, and logs.
“Debugging is a fundamentally different experience with Lumigo. It’s become essential to our workflow,” says Luke Thompson, Chief Architect at Clarifresh. “Before, we’d have a log with an exception in it, and we’d need to dig through countless logs to find the cause. Now, when an error occurs in production, we immediately see the data passing between a lambda and other services—from AWS resources like S3 to queries against MongoDB. It’s all presented alongside the full transaction context in Lumigo with an easy to understand view of what happened.”
Revolutionizing Observability with AI-Powered Monitoring
Clarifresh was already benefiting from Lumigo’s capabilities when they adopted Lumigo Copilot’s Beta program. According to Thompson, “I’ve loved Lumigo since I first started using it. I’ve found it provides exceptional Lambda insights. Before Lumigo, getting effective APM visibility into Lambda functions was always challenging.”
When the opportunity to join the Lumigo Copilot Beta emerged, Thompson was “intensely interested,” particularly given his parallel focus on building AI capabilities into Clarifresh’s own platform. Clarifresh seamlessly integrated Lumigo Copilot with their existing monitoring infrastructure, which added:
- Alert integration through Slack: The team receives intelligent alerts through Slack that provide issue details, accelerating the resolution process
- Interactive troubleshooting: Engineers use the intuitive chat interface to ask follow-up questions and receive detailed information directly within Slack
- Code integration: Copilot’s GitHub integration allows it to access source code to help identify root causes with greater precision
The Business Impact
Slack-native troubleshooting
Lumigo Copilot transformed the team’s ability to respond to issues by integrating directly into their existing communication channels, allowing for immediate action without context switching. “The ability to interact without having to open the product—directly in Slack—and further integrate with our alerts is incredibly valuable,” says Thompson. “You can ask questions and get clarification from a Slack alert anywhere, even on your phone.”
Efficient log analysis
The platform dramatically reduced the time engineers spent combing through logs, enabling them to pinpoint critical issues in seconds rather than minutes or hours. “When you’re examining logs and don’t want to spend valuable time scanning through volumes of data for the particular log line with an issue, the Copilot chat speeds that process up immensely.”
Democratized troubleshooting
Lumigo Copilot levels the playing field for team members of varying experience levels, creating a more resilient engineering organization where knowledge isn’t siloed among senior staff. “As our team grows, we anticipate the value increasing even further with more junior developers joining. Currently, we have a senior team who are very familiar with most of the lambdas, but we can clearly see how this will help new team members troubleshoot issues more quickly whilst having less familiarity with the code.”
Future Outlook
Thompson sees even more potential as Lumigo Copilot continues to advance. “It’s the perfect time to add these capabilities as they provide the ability to understand complex issues involving large amounts of log data and source code through integrations with GitHub—dramatically reducing time to resolution, and the underlying language models will continue to become even more sophisticated, enhancing the AI features within Lumigo.”
Lumigo has become an invaluable component of Clarifresh’s serverless monitoring strategy, with Copilot extending its capabilities through AI-powered observability. This integration enables smarter, more intuitive troubleshooting directly from Slack, reducing resolution times and improving overall operational efficiency.
For Clarifresh, a company bringing AI innovation to the fresh produce industry, leveraging AI-powered observability through Lumigo Copilot represents a natural extension of their technology-forward approach.
“Lumigo has been an invaluable part of Clarifresh’s serverless workloads and monitoring, resolving issues when they arise due to its ease of use. When you’re under pressure to find an answer for a critical issue, it’s always efficient and accurate. Copilot takes it a step further with AI-powered observability—it’s a powerful addition to Lumigo’s toolset that enables smarter and more intelligent troubleshooting directly in Slack.” — Luke Thompson, Chief Architect, Clarifresh
