GoodLads vs Suprbox — Secure Storage for Autonomous AI Agents: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of GoodLads and Suprbox — Secure Storage for Autonomous AI Agents — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
GoodLads
GoodLads
AI growth manager for Google Ads that turns account performance into testable hypotheses and ships each one only on your approval.
Key features
- Hypothesis Feed: Daily analysis of search terms, keyword quality, geography, and audiences produces a ranked list of ideas, each naming the campaign and the spend at risk.
- One-Click Shipping with Approval Gate: Any proposed change is applied in a single click but never without explicit owner approval, and live ads are not edited directly.
- Kanban Verdict Board: Hypotheses move through Proposed, Scheduled, Live, and Completed so every test ends with a measured verdict rather than being forgotten.
- Account Treemap Overview: Campaign spend, conversions, and ROAS roll into one visual overview sized by spend and coloured against the account average.
- Least-Risky Lever Selection: Recommendations favour reversible mechanisms such as 50/50 RSA experiments, stepped target CPA changes, and new paused assets.
- Predicted vs Measured Reporting: Each completed experiment compares the predicted lift against the actual result, with budget shifting to the winner.
- Claude Code and Codex Integration: The same workflows can be driven from Claude Code or Codex for teams that work from a coding agent.
Best for
- Performance Review: Get a single overview of how every campaign is doing on spend, conversions, and ROAS without building reports by hand.
- Wasted Spend Discovery: Surface negative keyword opportunities, poor keyword-ad combinations, and geography issues that are draining budget.
- Budget-Capped Campaigns: Identify campaigns limited by budget and lower target CPA in reversible steps to buy cheaper conversions at the same spend.
- Ad Copy Testing: Run benefit-led versus price-led headline experiments as 50/50 splits instead of editing live ads.
- Seasonal Campaign Prep: Stage seasonal copy and sitelink assets in advance, ready for one-click approval when demand spikes.
- Agency Account Management: Manage optimisation hypotheses across multiple client accounts from one board with a shared approval workflow.
Suprbox — Secure Storage for Autonomous AI Agents
Suprbox
Secure, purpose-built memory fabric that mediates document access and stores context and recall vectors for autonomous AI agents.
Key features
- Purpose-Built Memory Fabric: Provides a dedicated storage layer for agent context and state, optimized for storing and recalling contextual information used by autonomous agents.
- Vector Recall & Retrieval: Stores recall vectors and supports fast retrieval of context vectors so agents can access relevant context quickly during execution.
- Document Gateway: Sits between documents and agents to mediate reads and writes, preventing direct, uncontrolled agent access to source documents.
- Runtime Policy Enforcement: Enforces tight, fine-grained policies at execution time to control what agents can read, write, or execute against stored data.
- Execution Isolation: Isolates agent workspaces and memory to reduce risk of cross-agent data leakage and maintain separation between concurrent agent sessions.
- Controlled Context Delivery: Supplies agents only the allowed slices of context and vectors needed for a task, limiting exposure of sensitive information.
- Interposes between documents and autonomous agents to control access
- Purpose-built memory fabric for storing agent context and recall vectors
- Policy-driven execution and enforcement for agent operations
- Workspace isolation to prevent cross-agent data leakage
- Access controls and document-level gating for agents
- Designed for auditability and runtime visibility of agent access patterns
- Integrates with agent workflows to mediate reads/writes to storage
Best for
- Protecting enterprise document stores from autonomous agents by mediating agent access and preventing unauthorized reads of sensitive files.
- Providing persistent agent memory for multi-step workflows where agents need to store and recall contextual vectors across sessions.
- Enforcing runtime access policies for agents operating in regulated industries (finance, healthcare, legal) to maintain compliance and governance.
- Powering secure Retrieval-Augmented Generation (RAG) pipelines where agents retrieve vetted context vectors rather than raw documents.
- Isolating agent workspaces for research or development teams to experiment with agent behaviors without risking cross-project data leaks.
- Coordinating multi-agent systems by centralizing shared context and controlling which agents can access which pieces of memory.
- Prevent autonomous agents from reading or exfiltrating sensitive documents
- Store and manage agent context and memory vectors securely
- Enforce runtime policies for agentic workflows (who/when/how data is accessed)
- Isolate agent workspaces to reduce risk of data bleed between agents
- Provide a secure intermediary for LLMs and bots accessing corporate documents