Who Will Capture the Trillion‑Dollar Value of Context Graphs?

Key Insights

Cross‑scenario value – Context Graphs deliver more than single‑use cases; enterprises need 6‑8 heterogeneous systems to build a complete decision view, which a single vertical agent cannot provide.

Compounding platform effect – A generic Context platform learns from all agent interactions and reuses decision patterns across domains, giving it a growth advantage over isolated vertical agents.

Open standards to avoid lock‑in – Capturing the "why" behind decisions is a strategic asset; firms will not fragment this knowledge across multiple vendors unless an open standard guarantees control.

Semantic alignment as a barrier – Companies like Databricks, Snowflake and Palantir already excel at integrating heterogeneous systems and mapping semantics, giving them a structural edge.

Value perception drives payment – Positioning Context Graphs as a tool to make agents more accurate yields limited willingness to pay, whereas framing them as ownership of decision knowledge creates strong demand.

Why Heterogeneity Is Core

Enterprise decisions often involve data from dozens of systems—PagerDuty, Zendesk, Slack, Salesforce, Snowflake, etc. A simple renewal discount scenario requires context from all six systems, each with its own data model and API. Medium‑size B2B SaaS firms typically manage 5‑8 sources of record, 10‑15 collaboration tools, and 20‑30 specialized tools, leading to a combinatorial explosion of integration requirements.

Prukalpa Sankar estimates that covering 80% of customers would require a vertical agent to integrate 50‑100+ systems, repeating the same integration work across domains. The execution path is local, while Context is global.

Dual Nature of Context

Tomasz Tunguz distinguishes two Context databases:

Operational Context Databases store procedural knowledge (e.g., password‑reset steps, NDA clauses, incident response playbooks) that are highly scenario‑specific and hard to formalize.

Analytical Context Databases extend the semantic layer (e.g., definitions of revenue, CAC, LTV) and teach AI how to reason about metric changes.

Both are needed for a renewal decision: the operational “how” and the analytical “why”. Vertical agents see the workflow but lack access to the analytical layer; data platforms see metrics but not their real‑time decision usage.

Architectural Challenges

Building a Context Graph requires two layers: a Context Layer that captures real‑time decision trajectories and an Ontology Layer that structures domain knowledge. Challenges include cross‑system entity resolution, semantic alignment, and temporal consistency—problems already solved by data‑integration leaders.

Vertical agents focus on deep workflow optimization (sales scripts, intent detection), while data engineers have spent a decade solving cross‑system integration, giving data companies a head start.

Platform‑Level Compounding Advantages

The platform’s feedback loop creates a virtuous cycle: accuracy → trust → adoption → feedback → higher accuracy. Unlike vertical agents, which improve only within a single workflow, a platform aggregates learning from all agents, identifying common decision patterns across domains.

For example, if a company deploys 20 agents each making 100 calls per day, the platform can learn from 2,000 interactions daily, recognizing transferable patterns such as “how to identify an urgent case” that benefit multiple agents.

Open standards are essential for this compounding effect. Databricks’ Unity Catalog and Snowflake’s push toward an AI platform illustrate how controlling the representation and exchange protocol of Context determines who captures long‑term value.

Competitive Landscape

Three player categories emerge:

Data companies (Databricks, Snowflake, Palantir) – possess structural advantages in cross‑system integration and semantic mapping, but must extend upward to capture operational context.

Source‑of‑record (SoR) vendors (Salesforce, ServiceNow, Workday) – own deep customer relationships but face technical debt, organizational inertia, and a business model that could be disrupted by AI‑driven automation.

AI‑native vertical startups (Regie, Maximor, PlayerZero) – excel at domain‑specific workflow depth but struggle with fragmentation and lack the ability to provide a unified Context layer.

The decisive question is whether data companies can move from being “data providers” to “Context platform owners” by delivering operational context, or remain limited to analytical context only.

What a Platform Must Deliver

A successful Context platform needs seven core capabilities:

Cross‑system connectivity – integrate hundreds of data sources and map their semantics.

Operational Context synthesis – transform Slack conversations, ticket logs, and emails into queryable, governed structures.

Analytical Context management – go beyond traditional semantic layers to let LLMs understand metric definitions.

Real‑time Context delivery for inference – millisecond‑level retrieval from graph + vector + relational stores with access control.

Large‑scale feedback loops – continuously improve Context quality from millions of daily agent interactions.

Governance and trust – provide visibility, editability, auditability, and fine‑grained permissions.

Balanced internal‑external learning – decide which knowledge stays in the graph versus being baked into model weights.

No company currently possesses all seven, but data firms are closest.

Why Integrators Win

Prukalpa Sankar’s thesis: in a heterogeneous world, integrators win. History repeats this pattern—Snowflake won over single‑purpose analytics tools by unifying data, Tableau won over departmental reporting by connecting sources, and Atlan/Collibra won over siloed metadata tools by offering cross‑system governance. The same logic applies to Context Graphs: the first to build a cross‑system Context network becomes the de‑facto standard.

Enterprise Payment Drivers

If Context Graphs are marketed merely as “making agents more accurate,” willingness to pay is low. Positioning them as “the platform that lets enterprises own and control decision knowledge” creates strong demand because it addresses a strategic asset rather than a convenience.

Data‑catalog failures illustrate this: tools that only ease engineers’ work see limited budgets, whereas solutions that directly impact revenue capture higher investment.

Strategic Outlook

In the next 1‑2 years a “who owns Context” war will unfold: SoR vendors will try to acquire or extend into Context, data companies will push upward into operational layers, vertical AI startups will broaden horizontally, and new Context platforms will race to build cross‑system connections and ecosystems.

The winner will be the entity that first establishes a network effect, offers an open yet controlled standard, and demonstrates clear business value to non‑technical decision makers.

References

Ashu Garg and Jaya Gupta, “The Context Graph Wars”, Foundation Capital

Prukalpa Sankar, “Context Graphs Are a Trillion‑Dollar Opportunity. But Who Actually Captures It?”, Atlan

Tomasz Tunguz, “Context Databases”, Redpoint Ventures

Animesh Koratana and Jamin Ball, “Why context graphs are the missing layer for AI”, Foundation Capital

Arvind Jain, “Context is the next data platform—and why context graphs are key to understanding processes”, Glean

Daniel Davis, “The Context Graph Manifesto”, TrustGraph

Kirk Marple, “Context Graphs: What the Ontology Debate Gets Wrong”, Graphlit