meghIQ is an independent research project exploring how enterprises can discover, attribute cost to, and govern AI-driven automations at scale.
The capability areas the research prototype is designed to address: discovery, governance, cost attribution, and tamper-evident oversight via blockchain-anchored audit trails.
Automatically discover and catalog all automations across your enterprise, including hidden and undocumented workflows.
Comprehensive dashboards and KPIs to monitor automation health, performance, and ROI across your entire ecosystem.
Built-in compliance monitoring for GDPR, HIPAA, SOX, and custom rules. Automated violation detection and remediation.
Unique features like Automation DNA Profiling, Health Scoring, and Lifecycle Prediction powered by advanced AI.
Designed to surface duplicate automations and consolidation candidates, with modeled savings attached to each candidate.
Designed to attribute cost to teams and owners across platforms and produce forecasted spend over future periods.
All compliance events, audit trails, and automation data are cryptographically verified on XRP Ledger for tamper-proof governance
Every compliance check, violation, and remediation is recorded on XRP Ledger with cryptographic proof
Automation DNA profiles and version history are stored on-chain for verifiable lineage and authenticity
GDPR, HIPAA, and SOX compliance events are independently verifiable by auditors without accessing your systems
Powered by XRP Ledger • Fast, Low-Cost, Enterprise-Ready Blockchain
Where biology meets automation—DNA profiling, health scoring, and predictive analytics that transform how you see automations
Analyze automation patterns to predict failures before they occur and recommend optimal architectures that prevent costly downtime.
Map complex automation dependencies and surface high-blast-radius relationships before they trigger cascading failures across your ecosystem.
Navigate through automation history, compare versions side-by-side, and forecast future states to prevent regression issues.
Continuous health monitoring with AI-powered diagnosis and automated remediation plans that keep automations running at peak performance.
Measure automation impact and identify high-influence workflows that could cause widespread disruption if they fail.
Classify automation behaviors and match compatible workflows to optimize performance and reduce conflict-driven failures.
Planned: a library of automation templates with reliability scoring. Not yet released.
Forecast automation obsolescence and proactively suggest migration strategies before legacy systems become critical liabilities.
Automatically detect and resolve automation conflicts in real-time, preventing data corruption and workflow disruptions.
Trace automation lineage through generations to identify inherited vulnerabilities and prevent systemic failures from propagating.
The prototype is designed to integrate with these automation platforms and tools. Connector availability varies by platform; some are implemented today, others are planned.
Zapier
Microsoft Power Automate
GitHub Actions
AWS Lambda
Azure Functions
Jenkins
Slack
Microsoft Teams
Email (SMTP)
Custom APIs
All product names, logos, and brands are property of their respective owners. meghIQ is not affiliated with, endorsed by, or sponsored by any of these companies.
meghIQ is shared with research collaborators evaluating how to govern AI-driven automations. No fees, no commercial obligations.