Javed Sheikh - Jan 31, 2026

Why vector search alone hits a wall, and what comes next‍The retrieval problem nobody talks about
If you’ve built any kind of AI-powered search or retrieval system, you’ve probably followed the standard playbook:
‍
1. Chunk your documents
2. Generate embeddings.
3. Store in a vector database
4. Retrieve by semantic similarity
5. Maybe add a re-ranker for better precision

This works. For a lot of use cases, it works remarkably well. But I kept running into the same failure mode: the system could find relevant information, but it couldn’t find relevant decisions.

Ask it, “What’s our policy on extended payment terms?” It retrieves the policy document. Great.“What did we do last time a healthcare customer in a similar situation asked for extended terms?” Silence. That knowledge exists somewhere in your organisation.
‍
It’s in email threads, Slack conversations, meeting notes, and people’s heads. But it was never captured as searchable, structured data.

‍Gap that led me to explore combining two approaches: knowledge graphs and context graphs.

Knowledge Graphs: Structure for Your Domain
A knowledge graph represents entities and their relationships in a structured, queryable format. Instead of flat text, you have:
‍
🔹 [Entity: Docker] → Requires → [Entity: AWS Credentials]
🔹 [Entity: Healthcare Customer] → Property → [Extended Procurement Cycle]
🔹 [Entity: Payment Terms Policy] → Owned-by → [Entity: Finance Team]

‍What this gives you:
🔹 Disambiguation: When a user asks about “deployment,” the graph knows whether that means Docker deployment, model deployment, or something else in your specific context.
🔹 Relationship traversal: You can answer “what does deploying our app require?” by traversing relationships, not just matching keywords.
🔹 Domain grounding: Your retrieval system understands your domain’s structure, not just its vocabulary.

‍What it doesn’t give you:
‍The knowledge graph captures what should be true in general. It doesn’t capture what actually happened in specific cases.

It knows healthcare customers have extended procurement cycles. It doesn’t know that last quarter, Finance approved a 90-day exception for Acme Healthcare because of their Q4 budget freeze, and that decision was referenced in two subsequent similar requests.

Context Graphs: Memory for decisions
The concept of a “context graph” (as recently articulated by Foundation Capital) fills this gap. A context graph captures decision traces, the record of what was decided, why, by whom, and based on what precedent.