Services

We integrate your historic data to make your future processes smarter

Too much valuable process knowledge is hidden in Spreadsheets, PDFs, Slide decks, machine logs, or buried in past projects. Our Yieldmanager integration service turns this scattered data into a structured, searchable, and intelligent knowledge base.

What we do

Historic Data Import

Import and normalize data from Excel, MES, and machine logs

Document Parsing

Extract insights from PDFs, Word, and PowerPoint reports

Experiment Reconstruction

Reconstruct experiments with context — parameters, outcomes, decisions

Data Visualization

Visualize everything — trends, patterns, process windows

Yield Correlation

Correlate machine performance with product quality

AI Pattern Detection

Use our AI to find patterns, outliers, or reuse opportunities

Hidden Insights

Uncover patterns and anomalies that aren't immediately visible.

Process Optimization

Create faster and more efficient processes

What you get

A centralized, interactive dashboard of your project history.

Annotated insights — What worked. What didn’t. And Why.

A searchable database for faster decisions and fewer repeated tests.

Foundation for AI-assisted planning in future trials.

The Impact

Higher yield through data-driven parameter refinement
Improved quality via machine-performance-to-outcome correlation
Faster ramp-up with reuse of validated setups
Fewer failures thanks to historical issue traceability
Services

Key Components of the Service

A) Project Scoping and Mapping
  • Joint kick-off to uncover the hidden history of development work
  • Mapping all sources: lab sheets, test bench logs, MES exports, machine signals, slide decks
  • Creating a storyline: key moments, decision forks, data deserts
  • Defining high-impact categories (e.g., failed attempts, pivotal trials, unexplored areas)
B) Data Import and Structuring
  • Secure upload or automated pipeline connection
  • Smart recognition of structure, timestamps, signals, and parameters
  • Translation into Yieldmanager’s development schema: Process parameters, Test outcomes and observations, Trial metadata and development phases, Machine data and configuration context
  • AI-supported tagging to link related events across time and formats
C) Context Reconstruction and Annotation
  • Adding missing hypotheses, goals, and reasoning
  • Identifying patterns of evolution, dead-ends, and surprises
  • Clustering similar iterations, contrasting divergent outcomes
D) Visualization and Insight Generation
  • Dynamic dashboards for: Project timelines and experiment chains, Parameter influence on outcome metrics, Discovery loops and missed forks, Signals of readiness and robustness
  • AI to uncover untapped findings, risky repetitions, or hidden outliers
  • Optional smart alerts: resurface validated setups, highlight patterns never fully followed through
Deliverables
  • A resurrected digital twin of your development history, mapped and annotated
  • Interactive dashboards that make patterns, anomalies, and insights visible
  • A structured knowledge base for faster decisions and better reuse
  • A narrative report outlining missed insights, critical forks, and high-leverage learnings
Benefits for You
  • Uncover breakthroughs already hiding in your data
  • Increase yield and quality by reusing proven setups and avoiding costly detours
  • Accelerate development cycles through pattern recognition and reuse
  • Protect innovation investment by capturing learning across teams and time
  • Fuel AI-driven planning with rich, contextual knowledge

Ready to get started?

Stay ahead of the curve and get a demo of the Yieldmanager capabilities!
Demo