An open research effort exploring how enterprises can discover, attribute cost to, and govern AI-driven automations — with a tamper-evident audit trail. Shared with research collaborators on a non-commercial basis.
Non-commercial research project • No fees or subscriptions • Beta access by request
Command Center
Automation Risk Overview
Critical violations
14
Needs review
Duplicate spend
$82K
Savings found
Unowned automations
37
Assign owners
Dependency Hotspots
Impact pathsRecommended action
Consolidate 8 duplicate lead workflows
Projected annual savings: $128,400
Industry-reported failure modes in enterprise AI & automation governance.
Organizations have lost track of their automations. Critical workflows run without oversight, creating compliance and operational risk.
Source: Gartner, AI & Automation Risk, Jan 2026
Multiple teams build the same automations. No visibility means paying 3–4x for the same functionality across different platforms.
Source: Zylo 2026 SaaS Management Index
Without governance, automations process sensitive data outside compliance boundaries. A single violation can cost millions in fines.
Source: Benchmarkit/Mavvrik Compliance Benchmarks, 2025
Manual discovery and documentation of automations for audits takes weeks. Teams scramble to find what automations exist and what they do.
Source: Industry surveys, 2025–2026
What the prototype is designed to do. Validating these capabilities in real environments is the work of the current research phase.
The discovery layer connects to SaaS platforms and cloud services and indexes what it finds. The research question is how complete and how fast that inventory can be made in real environments.
A duplicate-detection engine compares automation behavior across platforms and produces a candidate-merge list with estimated cost recovery. Validating the savings model is part of the research.
Policy-as-code definitions are evaluated against each automation’s runtime profile. Alerts are emitted on potential violations. The research investigates how tight the alert quality can be made.
A health-monitoring layer collects execution signals and tries to predict failure conditions in advance. The research evaluates how reliably this can be done across heterogeneous automation stacks.
Modeled potential based on published industry benchmarks — not measured customer results. Validating these models is the purpose of the current research phase.
A research model for discussion, not a guaranteed or measured outcome. Final figures will depend on each environment.
These figures are research models for discussion, not guaranteed or measured outcomes.
Today vs. the capabilities the prototype is designed to enable.
The capability scope of the research prototype, by area.
Designed to find every automation in the enterprise
Most organizations have no central inventory of the automations running in their environment.
The discovery engine is designed to connect to multiple automation and SaaS platforms and produce a comprehensive catalog of what it finds.
Connect platforms via secure API (target: ~5 minutes per platform)
Automated scanners enumerate automations across each connected platform
Build a catalog with metadata, owners (where derivable), and execution history
Map dependencies and relationships between automations
Continuous monitoring for newly created or modified automations
What the prototype is designed to enable — not measured customer outcomes.
Interactive walkthroughs of how the prototype is designed to work. Numbers shown are illustrative scenarios, not measured customer results.
How the prototype is designed to discover automations across platforms
Duration: 2:30
meghIQ is an independent, non-commercial research project — share your interest and the maintainer will reach out.
Design targets — measured in prototype testing, not customer environments.
Per-platform connection time (design target)
Initial discovery window after connection (design target)
Watch meghIQ discover hidden automations across your enterprise
Click "Start Discovery" to begin scanning your automation ecosystem
How the research prototype's capability scope compares to manual processes and typical existing tooling.
| Feature | Manual Process | meghIQ | Typical existing tooling |
|---|---|---|---|
| Automation Discovery | Limited | ||
| AI-Powered Insights | |||
| Blockchain Verification | |||
| Real-time Compliance | Manual | Basic | |
| Cost Optimization | Limited | ||
| Automation DNA Profiling | |||
| Multi-platform Support | Limited | ||
| Predictive Analytics |
Discover hidden automations, predict failures before they happen, and govern with confidence using blockchain-verified 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.
Identify duplicate automations, consolidation opportunities, and performance bottlenecks to reduce costs by up to 40%.
Track automation costs across platforms, forecast spending, and calculate ROI with detailed analytics and reporting.
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.
Discover battle-tested automation templates with verified community ratings and enterprise-grade reliability scores.
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.
Connect with all your automation platforms and tools
Zapier
Microsoft Power Automate
GitHub Actions
AWS Lambda
Azure Functions
Jenkins
Slack
Microsoft Teams
Email (SMTP)
Custom APIs
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