What Is a Context Layer?

Enterprise data can leave some questions unanswered. For example, a customer record might show an active subscription but not whether the customer can use a particular feature. The record alone doesn’t tell the AI enough to answer.

A context layer brings together the data, business rules, relationships, and current information needed to make sense of it.

This article looks at how context layers work, how they compare with semantic layers, RAG, and agent memory, and how knowledge graphs can help connect information across enterprise systems. It also covers practical applications and the limits of this approach.

What is a context layer for AI systems?

A context layer is an infrastructure component between enterprise data and AI applications. It connects the data retrieved by an application with the context it needs to understand that data. 

In other words, the context layer offers the application information related to that data, which gives the application the ability to split the information, explain its significance, and determine its relevance. 

Without the context information, the application would have to make guesses, which can cause inconsistent responses and a higher risk of hallucinations, meaning information that sounds confident but is factually incorrect.

Contextual information changes depending on the request or question. It can also differ based on the task, user, or business context. One good example of this is that if a customer with an active subscription asks a support assistant a billing question, the context for the assistant could be the customer’s latest invoice and payment status. 

On the other hand, a system checking whether that customer qualifies for a service may require the customer’s plan, location, eligibility rules, and other factors related to the request. The account record is identical in both cases. The surrounding context is not.

Why is enterprise data not enough for AI?

Enterprise data tells an AI system what has been recorded. It rarely tells the system what that record means in a particular context. A simple inventory question highlights why.

Let’s take the example of an AI assistant that helps a retailer’s sales team. When asked if the company can ship 100 product units to a customer by Friday, the assistant checks inventory, sees 120 units in stock, and replies that it can.

The problem is that 80 of those units are already earmarked for another order, half the remaining stock is in a warehouse that doesn’t ship to that region, and the count is six hours old. With the proper context, the assistant would have said, “We only have 40 units of unreserved stock available, 20 of which are in a warehouse that doesn’t serve this region, so based on a six-hour-old count, I can only promise you 20.” The data in the database never changed, but the assistant’s knowledge of it did.

An experienced salesperson would likely have spotted all three of these issues at once. They know that reserved stock doesn’t count, which warehouses serve which regions, and when it is necessary to consult other teams about an unusual situation. 

The assistant’s knowledge gap shows a bigger problem. People naturally fill in missing information using what they already know. They use their experience, rules, and exceptions to come to an answer. Structured data models rarely capture this type of understanding. Instead, it tends to be spread across different systems, policies, and the knowledge of a few key people. This is why accuracy depends on understanding how an organisation works, not just the data available to the assistant.

AI systems aren’t able to do the same kind of context-based filling in, and they make assumptions. In the absence of a shared knowledge layer, teams build their own knowledge within the application, or in other systems, or encode it into their models. Definitions and parameters get pulled out over time. 

One team might define an active customer as someone who has made a purchase in the last 30 days, while another measures it in months. Two teams might use the same definitions, but one has a hard-coded exception in their code that was removed last quarter. 

Across all of these, companies capture critical knowledge and insights that can be difficult to govern, maintain, and share because they exist in different places. The result is context debt, where the buildup of unshared, uncaptured knowledge across disparate systems makes every new AI application more challenging to govern and evolve. Like financial debt, it increases the cost of change over time.

What information does a context layer provide?

A context layer integrates different kinds of information that allow an AI system to understand enterprise data and determine what is relevant to the problem it is tasked to solve. Some of the information may include:

• Business definitions and terminology provide the definitions of various business terms, metrics, and other concepts that allow an AI to consistently interpret enterprise information correctly. An example of this is differentiating between an active and inactive customer.
• Entity relationships define the relationships between the entities the business tracks, such as customers, accounts, products, transactions, and policies. This allows the AI to understand how individual records relate to each other.
• Metadata and lineage describe the data itself, how it was transformed, and how data sources and fields connect. Metadata is data about the data, such as what it is called, what it covers, and how it is recorded. Lineage is the record of how data moved between systems and what changed along the way.
• Business rules and constraints provide information about what parameters or policies apply to specific sets of information. For example, a rule that determines who has access to a customer’s payment history.
• Data freshness, ownership, and provenance provide information about when the particular data set was refreshed, who owns it, and which source originally supplied it. For example, providing the AI with information about whether the stock quantity was updated 6 hours ago or 6 days ago.
• Domain-specific knowledge covers the industry terminology, processes, and guidelines specific to a particular industry, such as healthcare or finance.

Each type of context allows an AI to consider information beyond the individual data values. It also lets AI consider additional information relevant to the business environment in which the data exists. 

How does a context layer differ from a semantic layer, RAG, and memory?

A context layer extends capabilities that already exist in enterprise AI architectures, including semantic layers, retrieval augmented generation (RAG), and agent memory. Their capabilities can overlap with what a context layer provides. The difference is in how the information is brought together and applied to a certain task and business situation.

The differences become clearer when comparing their primary purposes, the information they typically provide, and how they relate to the broader context available to an AI application.

Context Layer vs. Semantic Layer vs. RAG vs. Agent Memory

The differences become clearer when comparing their primary purposes, the information they typically provide, and how they relate to the broader context available to an AI application. 

A context layer’s overlap with a semantic layer is particularly confusing, as both can represent entities, relationships, and rules. The difference is the use of information. A semantic layer provides shared meaning that is consistent across the data and the applications to achieve interoperability. A context layer uses these meanings while considering current state, permissions, and tasks to determine relevance to a particular situation.

For example, a semantic layer might define ‘active customer’ as a customer with a currently valid subscription. That definition is consistent across the organization. However, when an active customer questions why they can’t use a specific feature, the definition of an active customer doesn’t help. The assistant would need more information such as their plan, permissions, and the eligibility criteria for the feature. The customer is still an “active customer,” from a semantic point of view, but this definition does not tell the application anything else.

RAG and agent memory can also contribute to the context available to an AI application. RAG can retrieve relevant documents, records, or operational data, while memory can preserve information established earlier in a conversation or task. A context layer can bring those inputs together with semantic definitions, relationships, and current system state when determining what information is relevant to a particular situation.

Consider the active customer from the earlier example, who asks why they cannot use a specific feature. In this case, RAG would retrieve the relevant information, such as the feature’s help article and its eligibility rules. Memory would recall that the customer mentioned earlier in the conversation had recently switched plans. The context layer would combine these with the customer’s current plan and permissions to explain why the feature is unavailable.

These technologies work together rather than in a hierarchy. A context layer does not have to replace a semantic layer, RAG, or agent memory. Instead, it provides a way to connect and apply information from these and other sources according to the requirements of the current task.

How do context layers differ by use case?

While the role of a context layer stays the same across use cases, the information it provides changes depending on the task, the user, and the decision being made. Here are a few examples to explain how that works across different domains:

In financial crime and fraud, the important context may be the relationships surrounding an event. An investigation system may need to connect a transaction with related accounts, devices, customers, previous alerts, and investigation outcomes. The value of the context lies in the fact that it shows the connections and patterns of history that would otherwise be difficult to establish through isolated transactions.

For customer-facing AI, the focus will be on the current situation of the customer. An AI assistant will have to take into account factors like account status, entitlements, recent transactions, applicable policies, and the age of the transaction itself. This is how it will be able to differentiate between information that relates to the current transaction and the one that is not relevant anymore.

In healthcare and other regulated industries, context helps determine whether information is relevant to a task. For example, an application that reviews a patient’s records needs to distinguish between recent and older test results, consider the guidelines for the patient’s condition, and check whether the user has permission to access that information.

Where does the context come from?

The information providing context is rarely stored in one place. In most enterprises, it is distributed across the systems that store business data, define its meaning, track its use, and document how business processes work. A context layer usually gathers these details from:

• Data warehouses and databases contain customer records, transactions, business data, and historical records. These give AI access to the facts it needs to work with.
• Data catalogs and semantic layers explain what business terms and metrics mean. They also define how different pieces of data relate to each other.
• Operational systems show what’s happening right now. This could include an account’s status, current inventory, an order, or an ongoing case.
• Knowledge bases and documentation hold company policies, procedures, business processes, and industry guidelines. Some of this information exists only in documents, rather than structured databases.
• Lineage and governance systems record where data originated, who owns it, how it has changed, and what access rules apply.

A context layer adds a unified way for AI applications to reach all of these sources and draw on the same shared context.

Why do knowledge graphs matter for context?

Much of the context lies in the relationships between records and documents. A single record, taken alone, often says little about what it means or whether it matters. Traditional retrieval returns them separately. An AI system may therefore receive several related records without knowing how they connect or which connections are relevant to the task. A knowledge graph provides one way to address this challenge.

A knowledge graph represents information in the form of a connected structure of entities and their relationships. For example, it can link a customer to an account, the account to its transactions, and those transactions to the products or policies involved. An AI can then follow that chain across systems, often called multi-hop reasoning, which means finding and reasoning over multiple supporting documents. At an enterprise scale, the knowledge graphs and semantic models that connect business concepts across different data sources form a semantic backbone, providing a single, dependable structure for a context layer to draw on.

Here’s an example. Let’s say  a customer asks, “Why am I unable to export my reports?” A document search will probably return a help article about exporting reports. That article does not explain why this customer’s export fails. 

A context layer built on a knowledge graph follows the links between the customer’s billing and support records instead. It finds that the customer’s subscription was downgraded last month and that exports are only available on higher plans. It also finds an open support case in which an earlier agent called the problem a bug. Putting these together, the assistant can explain that the customer’s plan, not a bug, is blocking the export.

A real project shows the same idea. Avalara, a tax compliance software provider, explored vector-based RAG for its support chatbots. This approach retrieves the passages closest to the question based on some similarity score.  However, as answers improved, customers grew less tolerant of the wrong ones. The team concluded that this approach could not provide the required level of reliability for a tax application.

Graphwise worked with Avalara’s team to build a Document Object Model (DOM) GraphRAG that turned its structured documentation into a knowledge graph. Consequently, answers then came from the content and its relationships, not from text that merely looked similar. According to Graphwise’s published account, the proof of concept produced fact-based, explainable answers, thus proving the importance of structured and human-curated content for reliable AI.

Knowledge graphs can also carry contextual information. A context graph extends a knowledge graph with each fact’s source, timing, reliability, and where it applies. This helps the AI decide when and how to use the connected information in a specific situation. A knowledge graph may show, for example, that a customer’s payment is overdue, whereas the context will help to know whether the payment is pending or settled.

How do you build a context layer?

Building a context layer involves combining connected capabilities that deliver enterprise information, with its meaning and current state, to AI applications. The specific details may vary by organization, but most context layer implementations require these six capabilities:

1. Map the sources of enterprise context

Start by listing the systems that hold your enterprise data and what each one provides. These might include databases, operational systems, documentation, data catalogs, semantic models, lineage systems, and governance platforms. This gives you a clearer view of what information is available, where to find it, and how to access it. If you miss a source, the AI may have to work without information it needs.

2. Establish shared entities and definitions

Apply entity resolution, which links records for the same customer or product across systems. Reuse existing semantic models and the shared definitions you may already have. Without entity resolution, the same customer can appear as several different people.

3. Represent relationships, rules, and provenance

The architecture needs to represent how entities and business concepts relate to one another, along with relevant policies, rules, and constraints. Record how entities connect, which business rules apply, where each fact originated, and when it was valid. Knowledge graphs and context graphs can be used to model these connections. An AI that lacks this record may be unable to show where an answer came from or defend it.
ting these connected relationships and their associated context.

4. Assemble and serve context at runtime

Combine several retrieval methods to pull what is relevant to the current task, user, and situation. Structured queries look up exact facts in a database. Keyword and vector search find text by exact words or by similar meaning. Graph-based retrieval follows the connections between facts. The goal is to assemble the information relevant to the current task, user, and business situation. Sending the model everything creates very long inputs, and long inputs can hinder how a model processes information.

5. Incorporate memory and changing state

Session history, long-term memory, task state, workflow progress, and current operating conditions all change what an AI application should consider. Without them, the AI may answer from information that was true yesterday but is no longer true today.

6. Govern and maintain the context

Assign owners, set permissions, and monitor for stale, conflicting, or broken context. Applications should only receive what they are authorized to see. An unmaintained context layer becomes another source of confident but wrong answers. 

What are the limitations of a context layer?

A context layer derives its value from the information it is given, so it inherits many of the weaknesses of its sources. For example, if an inventory system has the wrong number, the context layer will pass that number on, along with any surrounding context. Similarly, stale information is a problem. The layer might be able to flag a specific fact as out of date, but someone has to keep its sources up to date.

Permissions are a related issue. Wherever access is incorrectly allowed or denied at the source, that error carries through the layer, and sensitive information may even reach the wrong application. Reasoning is another limitation. An AI model may misinterpret the context it is given, so human judgment remains important, especially for critical decisions.

A context layer is also not simply a larger search index. Adding more documents doesn’t necessarily mean more context. A context layer instead provides structure, which decides which documents, records, and rules apply to a particular question, which do not, and which are no longer current. That structure requires an owner, because the rules and documents it refers to change over time. An owner also clarifies who is responsible for fixing a problem when one occurs.

Wrapping up

A context layer can’t make AI smarter, but it can give it better information to work with. That depends on keeping the source data accurate and up to date. It brings together information from the systems a company already uses, along with the meaning, relationships, and current status behind it.

Graphwise uses knowledge graphs and semantic models to connect business concepts across data sources. Context graphs add details about where information came from, when it was recorded, and how reliable it is. 

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