Mystrika
Immersa helps teams unlock the value of their enterprise data, documents, and more. It automates workflows and provides insights, predictions, and recommendations, leveraging AI agents and diverse data sources to streamline operations and boost productivity for data engineers, customer teams, and IT operations.
Immersa, also known as the Model Context Repository (MCR) and marketed as MarcoPolo, is an enterprise data infrastructure tool designed to bridge the gap between internal data sources and AI applications. It acts as a secure, governed workspace where AI agents can query, correlate, and analyze live data across databases, SaaS tools, and cloud storage. Unlike simple API connectors, Immersa provides a persistent execution environment with real compute, enabling AI to iterate on complex queries without starting over.
The core value of Immersa lies in its ability to assemble and maintain a complete directory of enterprise context. It continuously collects and updates data, code, infrastructure, and business information, tracking data lineage and dependencies in real time. This eliminates the months of integration work typically required to feed AI models accurate context, reducing hallucinations and improving reasoning. The platform integrates with popular AI clients like Claude, ChatGPT, Cursor, and GitHub Copilot via the Model Context Protocol (MCP), making it easy to add to existing workflows.
Security is a standout feature. Every AI session runs inside an isolated Kubernetes container with scoped credentials and zero data leakage. Users can set granular permissions for which systems the AI can access, and no data or credentials leak to the LLM or outside the environment. This enterprise-grade governance makes Immersa suitable for regulated industries and organizations with strict data policies.
Pricing is not publicly listed; interested teams must contact sales for enterprise deployment costs. The ColdIQ directory lists a Starter tier at $0.00 and a Professional tier at $12.00, but these likely represent placeholder or directory-specific tiers rather than actual Immersa pricing. The product is clearly enterprise-focused, with managed SLAs and private deployment options. A trial or demo is available upon request.
Immersa is best suited for mid-market and enterprise organizations that are actively building AI applications and need a reliable, secure way to provide their AI with real-time context from across the tech stack. It is ideal for data engineers, AI developers, and IT operations teams who want to accelerate AI development without compromising on data governance. However, it is not a standalone AI model or CRM system; its value is purely in context provisioning and data orchestration.
Overall, Immersa fills a critical gap in the AI stack by turning disconnected data into a live, queryable resource for AI agents. Its combination of security, persistent compute, and cross-source joins makes it a compelling choice for teams that have outgrown simple API integrations and need a governed, scalable solution. While the lack of transparent pricing and public reviews may give some buyers pause, the technical capabilities and enterprise focus suggest a mature product worth evaluating for serious AI initiatives.
Features
- Assembles enterprise context constantly from across the tech stack
- Enables intelligent reasoning with real-time data lineage tracking
- Provides secure data service tools with scoped credentials and isolated containers
- Manages data transformation pipelines using DuckDB, Python, and shell
- Detects events for automated workflow triggers
- Deploys privately with full control and managed SLAs
- Connects to AI clients like Claude, ChatGPT, Cursor, and any MCP client
- Supports cross-source joins across databases, warehouses, and SaaS tools
Pricing
Pros
- Speeds AI development by providing a comprehensive, up-to-date context engine
- Offers secure, isolated execution environment with zero data leakage
- Enables persistent, iterative querying with live data across multiple sources
- Schema awareness and query history make each session smarter
Cons
- No transparent public pricing; requires contacting sales for enterprise costs
- Not a standalone AI model or CRM; focused solely on context provisioning
- Setup requires technical expertise and integration with existing AI clients
- No public customer reviews or case studies available
Best For
Mid-market and enterprise teams needing to provide AI models with real-time, secure enterprise context for faster, more accurate AI development.