Why true executive assistance requires a structured world model, not just fuzzy similarity search. How Elani uses Knowledge Graphs to remember what matters.
In the rush to build "AI Agents," the industry has largely settled on a standard stack for memory: Vector Databases.
The logic is simple: Take a user's documents (emails, notes, calendar), chunk them into pieces, convert them into mathematical vectors (embeddings), and store them. When the user asks a question, find the "nearest" vectors and feed them to the LLM.
This is Retrieval Augmented Generation (RAG). It's powerful. It's scalable.
But for an Executive Assistant, it's also dangerously insufficient.
Vector search is probabilistic. It finds things that sound like what you're asking for.
If you ask, "Who is the CEO of Acme Corp?", a vector search might return:
The LLM then has to read these snippets and guess the answer. It usually gets it right. But what if you ask: "Have I ever introduced Sarah to the CEO of Acme Corp?"
A vector database struggles here. It doesn't inherently understand "Introduction" as an event, or the relationship between "Sarah" and "CEO". It just sees text. It might miss the connection entirely if the email didn't explicitly use the word "introduction."
At Elani, we realized early on that to be a true Chief of Staff, our agent couldn't just read your data; it had to understand the structure of your world.
This is why we built the Fact Extraction Workflow.
Instead of just dumping emails into a vector store, every piece of incoming data goes through a rigorous extraction process using our FactExtractionClassifier. We turn unstructured text into structured Facts.
When an email arrives, Elani doesn't just embed it. She analyzes it to extract:
Person("Sarah Chen"), Company("Meridian"), Project("Q3 Launch").WORKS_FOR(Sarah Chen, Meridian), IS_BLOCKED_BY(Q3 Launch, Vendor Approval).Meeting(2026-01-24, "Strategy Sync").This creates a Knowledge Graph.
The hardest part of building a Knowledge Graph is Entity Resolution.
In your inbox, "Sarah", "S. Chen", and "sarah.c@meridian.com" are all the same person. A naive system treats them as three different things.
Elani's resolution engine constantly merges these nodes. When you say "Draft a note to Sarah," Elani knows exactly which Sarah you mean based on the graph of your recent interactions, not just a keyword match.
Why does this matter? Because executives need certainty.
Vectors are great for "fuzzy" search (finding a document). Graphs are essential for "hard" reasoning (understanding the state of a project).
We haven't abandoned vectors. In fact, Elani uses a hybrid architecture.
By combining them, Elani doesn't just remember what you wrote. She remembers what you did, who you know, and what you promised.
That's the difference between a chatbot that searches your files and an agent that knows your business.