As AI continues to revolutionize how large enterprises operate, the most crucial startups are those capable of turning massive amounts of unstructured information into actionable intelligence. Morphik, one of the fastest-growing AI knowledge platforms for enterprises, uses MongoDB to deliver secure, high-performance, multitenant systems that power real-world automation at scale. 

Building AI knowledge at an industrial scale

Morphik has one incredible mission: to help developers and enterprises turn large volumes of unstructured data—such as manuals, compliance documents, transcripts, and even computer-aided design drawings—into intelligent, domain-specific AI agents. These agents are capable of automating complex reasoning tasks across a number of industries, including manufacturing, consulting, and even customer experience. Such customers depend on Morphik’s platform for extremely high accuracy and reliability—features that are essential when automating workflows in highly regulated and precision-driven industries.  

Highlighting the importance of performance and structure

When Morphik scaled up from simple pilot deployments to full enterprise rollouts, it realized that its PostgreSQL-based infrastructure wasn’t able to properly keep up. Its JSONB fields were increasingly difficult to query efficiently, and each enterprise customer the company onboarded required a separate physical database, which created unnecessary infrastructure and management overhead. Because of these bottlenecks, performance eventually degraded as data interconnections increased, and chat sessions, users, organizations, and metadata were eventually scattered across relational tables instead of living together naturally. The team at Morphik realized it needed to make a substantial change and make one fast. 

Shifting to MongoDB

Due to the issues with its original control plane infrastructure and reliance on a relational structure, Morphik decided to migrate to MongoDB Atlas. With this shift, it has gained some major improvements: 

• Document-centric modeling: With all related data (such as users, chats, agents, and sessions) now stored together, Morphik reduced query complexity.

• Instant database isolation: Morphik can provision each enterprise tenant with a simple “createDb” call rather than a new SQL instance. 

• Flexible schema: Morphik has gained effortless support for evolving metadata, tagging, and dynamic structures to support its evolving AI features. 

• Cluster-level URIs: Morphik has simplified operations and achieved better multitenant management. 

Morphik was able to easily achieve this shift through the MongoDB for Startups program. Through it, Morphik received MongoDB Atlas credits, the option for hands-on guidance from MongoDB experts, and access to the ventures and startup success teams, ensuring that the migration was as smooth a process as possible. 

The end result? Faster provisioning, lighter operations, and a smoother developer experience.  

Building and shipping more quickly

Even though Morphik doesn’t share internal metrics publicly, the gains of this migration are incredibly evident. By moving to MongoDB Atlas, Morphik has dramatically reduced engineering overhead. Database provisioning and migrations that once took hours now complete in seconds, and long, brittle code scripts for provisioning are no longer needed, eliminating much of the code bloat. The company now runs enterprise instances with lower latency, since isolated databases reduce search space and contention. The migration also helped Morphik simplify its overall developer experience: instead of wrangling separate systems, it can use MongoDB, which feels like a natural extension of its codebase. The net effect of this move means higher velocity for both customers and Morphik’s engineering team, enabling rapid experimentation and delivery.

Achieving security and scale for enterprise AI

Morphik serves some of the most demanding enterprise environments, where having proper data protection and compliance is absolutely crucial. MongoDB’s architecture makes it easy to isolate data by customer while maintaining overall lightweight operations—a key advantage for AI systems that have to handle sensitive designs, drawings, and various amounts of regulatory data. 

This infrastructural trifecta of security, scalability, and simplicity provides Morphik the confidence to expand into new enterprise opportunities without sacrificing speed. 

Enabling an AI-native developer workflow

For a company whose own product is AI-powered, the migration to MongoDB offered a unique advantage. JSON is the lingua franca of AI applications—large language models (LLMs), model context protocols (MCPs), and vector stores all emit and consume JSON. This means that LLMs understand MongoDB’s schema and syntax much more naturally than SQL, and, in turn, they enable Morphik to integrate AI-assisted development directly into its workflow. For Morphik, MongoDB Atlas isn’t just a data store. It’s part of the intelligence loop that helps Morphik build, train, and manage its own AI agents. 

Looking ahead

Morphik is now expanding its use of MongoDB beyond its control plane, with plans to migrate its retrieval, analytics, and even data ingestion pipelines onto MongoDB Atlas as Morphik’s platform continues to scale. This migration will help the company enforce a unified, flexible data backbone across its entire ecosystem. This will facilitate and streamline operations, further simplify development, and enable deeper integration between Morphik’s various AI components. Ultimately, this cohesive foundation will help power the next evolution of Morphik’s “AI knowledge brain,” meaning it can deliver more intelligent insights even faster to enterprise customers.