San Francisco skyline

TEM-SEM EDS Quantitative Analytics

100, 10K, 10M+

All Images

All Features

Structure & Composition
Data for Materials GenAI

Technology

Know your data, choose your future.

More images and more data mean faster & better insights. You've invested years collecting SEM-TEM EDS data, with millions of images, and trillions of features. Extract actionable insights from your existing structure and composition data to drive performance, improve reliability, increase yields, and accelerate product innovation.

Custom Segmentation

Combine custom and human-driven ML segmentation along with spectral processing to analyze the elemental composition of every structure in every image.

Quantification

Advanced algorithms reveal every pixel: precise identification and location with 50-100X higher resolution than traditional methods.

  • Every pixel, feature, and image
  • 10, 10K, or 10M+ images

Curation

Guaranteed pixel-level traceability and provenance of image and processing parameters ensure your data can be accessed and leveraged as needed.

Scorecards

Quantitative Scorecards deliver clarity and control:

  • Compare processes, materials, and machines
  • Reveal hidden patterns and variations
  • Workforce skills development
  • Track performance trends over time
  • Empower teams with actionable intelligence

Insights

Unlock existing materials data for:

  • Performance benchmarking across operations.
  • Root cause analysis
  • In-line & predictive quality control
  • Innovation acceleration with data
  • Intelligent automated continuous improvement

Statistical Analytics

Accurate and precise, statistically valid data with the highest confidence (lowest p-value) for:

  • Technology development
  • Manufacturing processes
  • Failure Analysis

Materials GenAI

Transform image and process data into nano-scale materials intelligence powering GenAI to predict performance, improve yields and reliability, reduce costs, and accelerate new product development.

AI-ready, optimized data formats for:

  • Large Language Model (LLM) training
  • Retrieval-augmented generation (RAG)
  • Materials AI Agents