An independent research effort on automation evidence, privacy-preserving governance, and AI agent oversight.
The latest backend work strengthens enterprise-grade governance: tamper-evident audit records, cryptographic key rotation, encrypted sensitive fields, GDPR rights workflows, resource mutation evidence, and an AI Agent Registry.
Gives enterprise reviewers a verifiable sequence of actions instead of a loose activity log that can be rewritten after the fact.
integrityLets the audit chain keep cryptographic continuity while keys evolve, which matters for long-lived regulated environments.
continuityProtects sensitive identity fields while preserving operational search and ownership lookup without exposing plaintext.
privacySupports export and erasure workflows so privacy review is part of the platform architecture, not a manual afterthought.
rights workflowTurns creates, updates, deletes, executions, toggles, acknowledgements, and remediations into reviewable tenant-scoped evidence.
change lineageCreates a controlled inventory for autonomous agents so organizations can study ownership, access, lifecycle, and operational state.
ownershipmeghIQ is currently maintained as an independent, uncompensated research project. The maintainer receives no financial compensation, royalties, or equity payouts from the project.
The platform is shared with a limited group of research collaborators for evaluation and feedback. No commercial sales, paid subscriptions, licensing, or paid services are offered at this time.
Any future commercialization, hiring, or business operations are conditional on future regulatory milestones and are not currently in effect.
meghIQ began from a recurring observation in enterprise environments: as organizations adopt AI-driven automations across SaaS platforms, cloud functions, and internal systems, they accumulate hundreds or thousands of workloads that nobody fully owns, inventories, or attributes cost to.
The downstream effects are well-documented in industry reporting on AI infrastructure cost overruns at large enterprises: duplicate spend, unattributed compute, compliance gaps discovered only during audits, and a missing audit trail for autonomous agents acting in production systems.
This project investigates open research questions in three areas — discovery and cataloging of AI workloads, tamper-evident evidence, and privacy-preserving governance and oversight. The work is conducted as an independent, uncompensated research effort and shared with research collaborators on a no-fee basis.
meghIQ is not currently offered as a commercial product or service. No paid subscriptions, licenses, or transactions are available. Any future commercialization is conditional on future regulatory milestones and is not in effect at this time.
The principles that guide how this research is conducted
The work is conducted openly as an independent research effort. Documentation, architecture notes, and findings are shared with collaborators.
Research direction is informed by the practitioners and researchers participating in the beta program — not by sales targets.
Prototypes are built quickly, tested with collaborators against real workload data, and revised based on what the results show.
The project's non-commercial status, scope, and research framing are stated plainly on every page of this site.
Independent Research
meghIQ is maintained as an independent, uncompensated research project. The maintainer receives no financial compensation, royalties, or equity payouts from the project.
What this project investigates
How can enterprises discover, evidence, and govern AI-driven automations operating across SaaS, cloud, and homegrown stacks while preserving privacy-sensitive ownership data?
Conditional on milestones
Any future commercialization, hiring, or business operations are conditional on future regulatory milestones and are not currently in effect. Collaborators will be notified of any such transition.
Explore the research tracks or reach out to discuss beta participation on a non-commercial basis.