This biotechnology company offers a bioinformatics error database to monitor and fix broken pipelines used in cancer treatment development. Their platform integrates and tracks errors across different frameworks, enabling users to detect issues in pipelines and assess the effectiveness of cancer treatments.
Funding
Funding not disclosed
Founders
Product
Problem
Scientific and engineering organizations leveraging high-performance computing (HPC) and AI face challenges in monitoring complex, distributed workloads, leading to inefficiencies, wasted resources, and delayed breakthroughs. Existing generic monitoring tools lack the nuanced understanding of scientific workflows and the specific data demands of these environments. This lack of visibility hinders the ability to optimize performance, debug issues, and accurately attribute costs in AI-driven scientific discovery.
Solution
Tracer provides an observability platform tailored for AI-driven scientific computing, offering deep insights into HPC systems and computational workloads. By extracting information directly from the operating system using eBPF-powered connectors, Tracer provides real-time, granular visibility into every workload and process, regardless of coding language, cloud structure, or location. The platform transforms this data into actionable insights, enabling scientists, engineers, and executives to optimize performance, reduce costs, and accelerate scientific breakthroughs. Tracer's architecture ensures that data never leaves the user's environment, complying with stringent security and regulatory requirements.
Target Audience
Tracer is designed for data scientists, engineers, DevOps teams, and executives in regulated industries such as pharmaceuticals, biotechnology, aerospace, and automotive, who rely on high-performance computing and AI for scientific discovery.
Features
- eBPF-powered operating-system (OS) level extraction for low-overhead, high-speed data capture
- Automatic recognition and extraction of science-specific information about tools, frameworks, and files
- Transformation of extracted data into Open Telemetry (OTel) format, with synthetic log generation
- AI-powered insights for error resolution, cost reduction, and performance improvements
- Compute requirement prediction to forecast resource needs for pipelines
- Bottleneck identification to pinpoint underutilized instances and slow tools
- Cloud cost dashboard for detailed cost attribution across departments and tools
- Secure, on-premise deployment ensuring data never leaves the user's environment