Beyond the Hallucination: Why Context is Key to Great AI
We are now in the age of AI, but for most businesses, it stays "smart" only in theory. At Biztory, we see a recurring issue: companies deploy advanced AI models only to realize these models do not actually know their business.
When you mix high intelligence with low context, the result is more than just a poor tool—it is dangerous. It leads to confident hallucinations at scale, where AI systems provide technically "right" answers that lack any real-world business logic. To achieve truly performant AI, businesses must close the gap between raw intelligence and their specific enterprise context.
The Hidden Complexity of "Simple" Questions
The industry often underestimates how much context is needed to answer even a basic business question. Take a query like, "What are the top 10 new shows?". To answer this accurately, an AI needs to navigate four distinct layers:
- User Context: Who is asking, and is the ultimate goal to promote ads or grow the audience?
- Knowledge Context: What does "new" mean? Is it content released this month or simply new to the platform?
- Meaning Context: How is "Top 10" defined—by unique viewers, total plays, or total watch time?
- Data Context: Which specific tables hold the "truth" (e.g., analytics.fct_streams), and are the metrics properly deduplicated?
Without these layers, different AI agents within the same company will have conflicting views of the truth. One agent might think "revenue" means bookings, while another thinks it means ARR, and a third may not understand the term at all.
Hitting the Three Walls of AI Scaling
As companies move from isolated use cases to company-wide systems, they hit three main walls:
- Wall 1: Context Bootstrapping. Building an agent takes five minutes, but giving it enough business context to be reliable can take five months.
- Wall 2: Testing Hell. Without a clear definition of "done," teams get stuck in a cycle of spot checks and intuition. If the business does not trust the context, adoption can drop by as much as 90% within a month.
- Wall 3: Context Portability. Most agents currently lack a "shared brain". When one agent learns a business rule, that knowledge isn't shared across the company, causing the effort to grow linearly with every new use case.
The Solution: An Open, Interoperable Context Layer
To scale, businesses need an Enterprise Context Layer that serves as a system of record for business intelligence. At Biztory, we are working with Atlan to help our clients build this layer through a five-step process:
- Unify: Pulling context across the entire data estate into one living Enterprise Data Graph.
- Bootstrap: Using AI to generate descriptions, metrics, and ontologies at scale to solve the bootstrapping bottleneck.
- Collaborate: Implementing a human-in-the-loop model where domain experts certify the context and resolve logic conflicts.
- Activate: Serving this certified context to every agent and tool via SQL, APIs, or the Model Context Protocol (MCP).
- Learn: Creating a feedback loop where evaluations and traces feed back into the pipeline so the context gets sharper with every interaction.
Context as a Compounding Asset
We believe that Context Quality Compounds. Better column lineage leads to better metric definitions, which eventually leads to a more robust automated ontology. Much like GitHub repos are the standard for code, Context Repos will become the standard for company memory—portable, shareable, and version-controlled.
As we prepare for Atlan Activate on April 29, Biztory is proud to be a Context Layer Partner. The path to production AI is not found in a larger model; it’s found in a better shared understanding of your business.
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