Meerkats: the context layer for AI systems

Categories: Automation / Agents, Business & Sales, Coding & Developer Tools | Pricing: Freemium | Official Website ↗

Meerkats provides a governed context layer for AI agents, connecting business data, definitions, rules, and actions to improve AI accuracy and automation.

Meerkats offers an infrastructure for building reliable AI systems that operate business functions like e-commerce, ad ops, and RevOps. It creates a context layer that AI systems use, connecting various business data sources and knowledge, defining entities, metrics, and relationships, and maintaining current state and history. This layer also holds business rules and exposes a set of governed actions, allowing AI agents to read this context via MCP and API instead of processing raw data directly. The platform aims to reduce token usage by providing agents with only the necessary context for a task, leading to more cost-effective operations. It also ensures consistent and accurate answers by providing LLMs with defined metrics and computations, tracing results back to their source. Meerkats enables the creation of typed automations from plain language playbooks, ensuring consistent execution of workflows and allowing for rule changes to be immediately reflected in subsequent runs. Meerkats supports multiple teams and organizations with separate workspaces and granular access controls, logging every change for traceability. It is compatible with various agents and models like Claude, Codex, and custom agents, allowing for flexible integration and modification without requiring system rebuilds. The platform provides a deep foundation with layers for data integration, semantic definitions, live context, stateful workflows, governed agent actions, and decision history.

Key Features

Context layer for AI agents
Data & integrations (CRM, ERP, ad platforms, etc.)
Semantic layer for entity/metric definitions
Stateful workflows and automations
Governed AI agent actions
Governance & traceability (permissions, versioning, decision history)
Token cost reduction
Consistent and accurate answers

Pros

Reduces token costs by providing targeted context to agents.
Ensures consistent and accurate AI responses through defined metrics and computations.
Enables reliable automations with typed workflows and rule-based execution.
Offers granular governance, permissions, and full traceability of agent actions.
Supports integration with various AI models and existing business systems without rebuilds.

Cons

Credit-based pricing might be complex to estimate for new users.
Requires initial setup to define business context, rules, and integrations.
Reliance on external AI models for core reasoning (Meerkats provides context, not the LLM itself).
No explicit mention of mobile platform support.
White-labeling and SSO are only available on the highest-tier plan.

Use Cases

E-commerce operations automation
Ad operations management
Revenue operations (RevOps) optimization
Lead follow-up and assignment
Customer support automation with business context
Building custom AI applications with a reliable backend

Best For

Businesses running AI-powered operations
Teams building custom AI agents
Organizations seeking to reduce LLM token costs
Companies needing consistent and traceable AI automations
Developers building AI-driven internal tools or products

Integrations: Meta (Advertising), Google Ads, Amazon Ads, Flipkart Ads, TikTok Ads, Walmart Connect, Blinkit, Zepto, Instamart, Shopify

Platforms: Web

Watch demo on YouTube ↗

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