Contextual AI
Use case library
Discover real-world use cases powered by your enterprise data. Build agents in seconds by importing pre-written YAML into Agent Composer.
AerospaceFlight & Systems Test Anomaly Detection
Reduce test run anomaly identification time from 8 hours to 20 minutes with an agent that flags statistically significant deviations and generates a summary of anomalies.
Data Sources
- Telemetry and sensor data (CSV, Parquet, binary logs) from flight, HIL, and bench test systems
- Test execution logs and system outputs (structured logs, text files)
- Historical test results and anomaly reports (PDFs, spreadsheets) in engineering repositories (e.g., SharePoint)
- Test procedures and requirements documentation (Word, PDF, HTML)
- Issue tracking records (e.g., Jira)
Technology (Semiconductors)Device Log Error Analysis
Shorten device failure investigation time from 8 hours to under 20 minutes with an agent that parses large log files, detects likely root causes, and proposes resolutions.
Data Sources
- Device and system logs (text files, binary logs)
- Error codes and diagnostic references (HTML, PDF)
- Historical failure analyses (PDFs, spreadsheets)
- Issue tracking records (Jira, internal systems)
ManufacturingEquipment Failure Prediction
Decrease unplanned downtime by identifying failure signals days to weeks earlier with an AI-powered workflow that analyzes equipment data and surfaces prioritized maintenance risks.
Data Sources
- Machine sensor and PLC data (time-series logs, CSVs)
- Maintenance records and work orders (CMMS systems)
- Historical failure reports (PDFs, spreadsheets)
- Equipment manuals and SOPs (PDF, HTML)
EnergyDrilling Operations Advisor
Cut non-productive drilling time by 10–20% by leveraging an agent that analyzes operational data and surfaces early indicators of risk.
Data Sources
- Drilling sensor and mud log data (time-series files)
- Daily drilling reports (PDFs, Word docs)
- Operational procedures and playbooks (HTML, PDF)
- Historical well performance data (spreadsheets)
AerospaceIssue Disposition Ticket Review
Accelerate issue triage and disposition time from 45 minutes per ticket to under 5 minutes using an agent that summarizes evidence, recommends disposition, and drafts ticket updates.
Data Sources- Issue and defect tickets (Jira, Polarion)
- Test logs and failure artifacts (text files, CSVs)
- Historical issue resolutions and root-cause analyses (PDFs, wiki pages)
- Engineering knowledge bases and procedures (Confluence, SharePoint)
Changing the way the world works through AI
Contextual AI is trusted by industry leaders, such as Qualcomm, HSBC, and Shipbob, to power their most complex and knowledge-intensive AI use cases. Contact us to find out how.