About Tracer

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.

<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. </problem> <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. </solution> <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 </features> <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. </target_audience>

What does Tracer do?

Employees

16 employees

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Tracer

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Executive Summary

tracer.bio 700+

Funding

No funding information available.

Team (15+)

No team information available.

Company Description

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.

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

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.