Contextual AI

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)

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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)

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)

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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)

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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)