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KNOWLEDGE
Amanda Ye
Growth, Morphic
8 min read
Stack Fragmentation. SaaS Sprawl. Tool Fatigue. They all describe the same condition from a different angle: too many disconnected systems yet not enough shared understanding between them.
We started our wanting to fix this the obvious way.
We built one platform and tried to put everything we thought GTM teams would need inside it. But after talking to customers, we realized how wrong we have been. Teams do not need another all in one platform, what they truly needed is a unified context layer that connects the specialized tools teams already use so the context inside them moves freely between systems without anyone having to migrate off what's working.
What is Stack Fragmentation?
Upon seeing these statistics, you’d understand why companies would want to create an all in one platform. But this just fixes it at face value. After talking to our customers, we realize companies don't need an all-in-one platform. Some concerns voiced out by them include:
"Organizations typically hit a point, usually around 50 employees, where an estimated 20% of collective work time is spent on overlapping systems"
"Context switching consumes up to 40% of productive time"
"Data silos cost the average mid-size enterprise an estimated $12.9 million a year"
Upon seeing these statistics, you’d understand why companies would want to create an all in one platform. But this just fixes it at face value. After talking to our customers, we realize companies don't need an all-in-one platform. Some concerns voiced out by them include:
Feature compromise: an all in one platform optimize for breadth, to solve for as many functions as possible but never specialized enough to solve specific problems companies may face
Single point of failure: if one platform goes down, every function that depends on it will go down with it as well
Concentrated data ownership: there’s a significant amount of leverage to hand over to one vendor
What they really need is a unified context layer. Although it's not a settled product category yet, the underlying technical ideas are established and increasingly urgent inside the tech industry: AI agents cannot act on context they cannot see, and most enterprise context is scattered by design. Unified Context layer is a governed intelligence layer that connects existing tools, resolves what entities and fields mean, models their relationships, and supplies relevant signals to people and agents without requiring all work to move into one system.
One product that has emerged from this need is Anthropic’s Model Context Protocol (MCP). MCP is an open standard for securely connecting AI applications with external data sources and tools. It gives an AI a common way to discover available capabilities, retrieve context, and invoke approved functions across systems such as CRMs, databases, file repositories, and internal services.
Though it's a powerful access layer, it only lets an AI application discover and call tools and retrieve resources from connected systems. It does not decide whether two records describe the same customer, define what “revenue” means, validate freshness, or determine whether an action is allowed. For the unified context to come in, some things we still need to figure out are…
knowledge graphs
Semantic models
Domain Ontology
Identify and Entity solution
That's what our job at Morphic is. To build that system so you can have a tool that allows for your stack to talk with one another so no context nor deal gets lost in the conversation.
Book a demo with us today to learn more!