MEGA(X) Jellyfish — The Data Nervous System for Agentic AI
MEGA(X) Jellyfish is the governed intelligence layer for enterprise AI — unifying real-time data access, agent spend control, and tech stack oversight across ServiceNow, multicloud, and sovereign environments, so every workflow decision is fast, accountable, and built to last.
Platform Overview
MEGA(X) Jellyfish gives AI agents a reliable, cross-platform nervous system — connecting them to trustworthy data and governed spend controls wherever they operate, across ServiceNow, multicloud, and sovereign environments.
Enterprises came to us with a common challenge: AI agents were only as good as the data they could reach and the budgets they could stay within. Most were stuck relying on stale, fragmented data sources on one side, and unpredictable, runaway token consumption on the other. MEGA(X) Jellyfish was built to solve both problems at once, and to work platform-agnostically rather than locking customers into a single ecosystem.
Data & Consumption Architecture
Once the strategy was finalized, we moved into architecture and implementation. Our design process centered on making live, governed data access and intelligent AI spend control feel effortless for agents, administrators, and finance teams alike, built to extend across whichever platforms an organization already runs on.
Real-Time Context Mapping — identifies which data sources agents can reach instantly versus which rely on stale batch syncs
Adaptive Token Governance — throttles AI consumption dynamically based on workflow value rather than blanket limits
Meaningful Human Checkpoints — routes only high-stakes, consequential workflow moments to human review, wherever the workflow lives
Governance & Cost Control
MEGA(X) Jellyfish closes the loop between data access and financial accountability. As AI agents scale across ServiceNow and connected platforms, the app continuously tracks token and "assist" consumption against budget thresholds, automatically throttling agent autonomy before overage costs hit. Paired with adaptive human checkpoints on only the most consequential workflow steps, it gives finance and IT leaders a single governed view of both what agents are doing and what it's costing them — turning unpredictable AI spend into a controlled, forecastable line item.
