1. The Goal: Representing a Human Life in Data
Human lives are not databases.
They’re fluid, emotional, and filled with context that defies categorization.
Yet, to make AI truly useful for self-understanding, it must be able to work with that complexity — to reason not just about words, but about meaning.
At DeepJournal, we asked a simple question:
How can we represent a person’s life in a way that’s structured enough for AI to reason about — yet flexible enough to stay human?
Our answer is a two-tier system of entities: logs and states.
- Logs are moments — things that happen once: a meeting, a trip, a conversation, a realization.
- States are continuities — ongoing aspects of your life such as relationships, goals, habits, projects, or emotions that evolve over time.
Each journal entry adds new logs and updates existing states.
Together, they form a connected, evolving map of your life — one the AI can understand, query, and help you reflect upon.
2. The Core Mechanism: From Text to Structure
Every time you write, DeepJournal runs a carefully tuned LLM workflow.
It reads your entry, extracts the meaningful entities, and decides whether each one represents a new element in your life or an update to an existing one.
This involves two key processes:
- Extraction — using large language models to identify and describe new entities (logs and states) in natural language.
- Connection — using search methods (by name, fuzzy match, and embedding similarity) to link these entities with what already exists in your structured memory.
If a match is found, the model merges them; if not, a new entity is created.
Over time, this builds an evolving, self-updating network of meaning.
The result is not just data — it’s continuity.
The AI begins to see the same relationships, habits, and emotions unfolding across time, just like you do.
3. The Challenges of Structuring a Life
This process sounds straightforward, but it touches on some of the deepest challenges in both AI and knowledge representation.
1. Making It Read Like a Human Life
AI-generated structures must remain readable.
Logs and states need to sound like they were written by you, not for a database.
Each entry, description, and update must be coherent, natural, and personal — not robotic metadata.
We spend a significant part of our development on prompt design and post-processing to ensure every generated text feels alive and human.
It’s not just about accuracy — it’s about preserving voice.
2. Avoiding False Connections
Human life is ambiguous.
Names repeat. Feelings overlap.
Without careful logic, “Laura (coworker)” could merge with “Laura (sister)”, or “Project Atlas” with “Atlas Café.”
To handle this, DeepJournal combines deterministic search (exact and fuzzy matches) with semantic similarity (embeddings) and LLM reasoning for tie-breaks.
It’s a constant balance between connecting what truly belongs together — and keeping distinct what should remain separate.
3. Defining a Standard for the Human Experience
Perhaps the hardest challenge isn’t technical — it’s conceptual.
What’s the right schema for representing a human life?
DeepJournal’s model of logs and states is just the beginning.
Each type — Relationship, Goal, Habit, Project, Emotion, Health — defines its own structure and merge logic.
But this ontology is not fixed. It evolves as we learn more about how people actually write, think, and change.
The ultimate goal isn’t to model everyone the same way — it’s to create a flexible system that can adapt to how each person’s life expresses itself through language.
4. The Philosophy Behind the System
DeepJournal’s algorithm is not just an engineering problem — it’s a question of meaning.
How can AI help us see ourselves without reducing our lives to data points?
Each log and state is a fragment of consciousness made durable.
Together, they form a structured mirror of your inner world — one that doesn’t just remember what happened, but learns what it meant.
This is what “AI journaling” really is about:
not automation, but amplification — using artificial intelligence to extend the range of human reflection.