Building Context Windows That Scale to Your Entire Data Estate

The naive approach to AI context is simple: stuff as much data as possible into the prompt window and hope the model figures it out.

It doesn’t work at scale.

Why Raw Context Doesn’t Scale

Modern language models can accept 128K, 200K, even 2M tokens in a single prompt. The temptation is to treat this as a solved problem — just push everything in.

But context quality degrades with size. Models perform worse when relevant information is buried in a large window. The signal-to-noise ratio drops. Latency increases. Costs explode. And critically, the model loses the ability to reason precisely about specific data points.

The answer isn’t a bigger window. It’s smarter retrieval.

Retrieval-Augmented Context: The Right Model

At KAIRO, we spent eight months building a context architecture that does something different. Instead of injecting everything, we ask: what context does this specific agent action actually need?

The system answers that question in milliseconds using a multi-stage retrieval pipeline:

Stage 1: Intent classification — What is the agent trying to do? The system classifies the action type and determines which data sources are relevant.

Stage 2: Sparse retrieval — BM25 search across structured data sources (databases, spreadsheets, CRMs) to retrieve exact records.

Stage 3: Dense retrieval — Embedding-based semantic search across unstructured sources (documents, emails, wiki pages) to surface conceptually related content.

Stage 4: Re-ranking — A smaller, faster model re-ranks the combined results by relevance to the specific task, not just the query.

Stage 5: Context assembly — The top-ranked results are assembled into a structured context block, with explicit source citations that get attached to every agent action.

The result is a context window that’s highly relevant, efficiently sized, and fully auditable.

Cross-Document Reference Resolution

One of the hardest problems in enterprise AI is connecting information that lives in different systems.

A contract references a customer ID. That customer ID maps to a Salesforce record. That Salesforce record links to a support history in Zendesk. To answer a question like “what’s our exposure if this customer churns?”, the agent needs to traverse that entire graph.

Our Context Engine handles this with what we call cross-document reference resolution — a pre-indexing step that maps entity relationships across all connected data sources. When an agent needs to reason about a customer, it gets a unified view that spans every system that customer touches, assembled automatically.

Real-Time Context Refresh

Static context is dangerous. Data changes. A contract that was valid yesterday may have been amended this morning.

KAIRO’s Context Engine maintains live subscriptions to connected data sources. When a record changes, the embedding index updates within seconds. Running agents pick up the change on their next context fetch without any manual intervention.

This sounds simple. In practice, building a system that handles updates at the rate enterprise data changes — hundreds of thousands of records modified daily — required significant engineering investment. We’re sharing our approach in an upcoming technical paper.

What This Means for Your Workflows

If you’re evaluating AI platforms for enterprise use, context architecture should be a primary evaluation criterion. Ask vendors:

• How is context retrieved? (Semantic search alone isn’t enough for structured data)
• Is context auditable? (Can you see exactly what the agent knew?)
• How does context update when data changes? (Real-time vs. batch refresh)
• What happens when the relevant data isn’t in the context? (Graceful degradation matters)

These questions will quickly separate production-grade systems from demos.