TimeStack operates 9 production AI models spanning large language models, temporal transformers, graph neural networks, reinforcement learning agents, and anomaly detection systems — all trained and optimized on NVIDIA GPU infrastructure.
Each model specializes in a distinct aspect of behavioral intelligence. Together, they form a comprehensive system that understands, predicts, and guides human behavior.
A domain-specialized large language model that understands human behavioral context, generates coaching interventions, and reasons across life domains with the nuance of an expert behavioral coach.
Built on the LLaMA-3 architecture with custom modifications for behavioral reasoning. We add a temporal position encoding layer that enables the model to reason about time-dependent behavioral patterns — understanding that a goal set 3 months ago has different context than one set yesterday. The model also includes a domain-aware attention head that specializes in cross-domain reasoning (e.g., understanding how career stress impacts health goals).
The model is fine-tuned on a curated corpus spanning multiple behavioral domains:
100B tokens of behavioral corpus. 8x H100 80GB, ~72 hours. Trains domain knowledge into base weights.
200K instruction pairs for coaching, goal decomposition, and domain reasoning. LoRA rank-64 adapters. 4x H100, ~8 hours.
Reward model trained on 50K preference pairs from expert coaches. PPO alignment with NeMo-Aligner. 8x H100, ~24 hours.
Red-team testing for harmful outputs. Constitutional AI constraints for health/mental-health topics. Guardrails for scope limitation.
Decomposes abstract life visions ("I want to be healthier") into concrete, time-bound milestone hierarchies across relevant domains, accounting for cross-domain dependencies.
Generates personalized motivational interventions grounded in the user's behavioral history, current energy level, recent achievements, and upcoming commitments.
Processes free-form journal entries to extract behavioral themes, emotional patterns, domain-specific insights, and connections the user may not consciously recognize.
Identifies and explains causal links between life domains — e.g., how irregular sleep patterns (Health) are impacting deep work capacity (Career) and relationship quality (Relationships).
A smaller, specialized LLM trained for structured output — converting high-level life goals into hierarchical plans with temporal dependencies, resource requirements, and measurable milestones.
While the Behavioral LLM handles free-form coaching, the Goal Decomposition Engine is optimized for structured JSON output. Built on a LLaMA-3 3B base (smaller for speed), it uses constrained decoding to ensure valid goal hierarchies with proper temporal ordering and dependency graphs.
{
"vision": "Become a published author",
"horizon": "12_months",
"milestones": [
{
"title": "Complete first draft",
"deadline": "2026-06",
"domain": "learning",
"sprints": [
{
"title": "Establish daily writing habit",
"weeks": 4,
"weekly_goals": [
"Write 500 words daily (Mon-Fri)",
"Read 1 chapter of craft book weekly"
],
"dependencies": [],
"difficulty": 0.6
}
]
}
],
"cross_domain_impacts": {
"career": 0.3,
"joy": 0.7,
"relationships": -0.1
}
}A multi-scale temporal fusion transformer that models human behavioral patterns across 5 time horizons — predicting everything from today's energy curve to 12-month goal completion probability.
Based on the Temporal Fusion Transformer (TFT) architecture, extended with multi-horizon attention heads and domain-conditioned gating. The model processes variable-length behavioral time series with irregular intervals (humans don't check in at exact intervals) using our custom temporal windowed convolution CUDA kernel.
Task completions, check-in responses, goal progress deltas, streak status, focus session durations, distraction counts
Time of day, day of week, days since last check-in, time since goal creation, seasonal patterns
Self-reported mood, energy levels, journal sentiment scores, emotional volatility index
Active domain counts, concurrent goal load, social interaction frequency, reward redemption patterns
A personalized graph attention network that learns causal interdependencies between 8 life domains — the core innovation that enables TimeStack's whole-life intelligence.
Human life domains are not independent. Sleep quality affects work performance. Financial stress impacts relationships. Exercise boosts mood and learning capacity. No existing productivity tool models these interactions — they treat each domain in isolation.
DomainGraph learns a personalized causal graph for each user, where nodes represent domains and edges encode the strength and direction of causal influence. The model discovers that for User A, career stress strongly impacts health (negative edge), while for User B, the dominant pathway is health → career (exercise improves focus).
"If you skip workouts this week, your Career domain score is predicted to drop 15% over the next 2 weeks based on your personal pattern."
"Your Relationships domain is underserved. Based on your graph, investing 2 hours here would positively impact Joy (+12%) and Growth (+8%)."
"Your Career satisfaction dropped this month. Tracing upstream: the root cause appears to be a Learning domain decline (no new skills) that reduced confidence."
"Rather than addressing Career directly, the highest-leverage intervention is improving your sleep quality (Health), which cascades to 4 other domains for you."
A specialized temporal model that learns individual daily energy patterns to predict optimal windows for different types of tasks — deep work, creative thinking, physical activity, and recovery.
Traditional productivity advice prescribes fixed schedules ("do deep work at 9am"). But individual circadian patterns vary dramatically. Our model learns each user's unique energy curve through check-in data, focus session performance, task completion timing, and self-reported energy levels.
The model uses a mixture of periodic functions (learned sinusoidal components) combined with contextual modifiers (sleep quality last night, day of week, recent stress levels) to produce a personalized hourly energy forecast. This drives the scheduling optimizer.
The Circadian model feeds directly into the scheduling optimizer:
A multi-task transformer (DeBERTa-v3 base) fine-tuned for simultaneous domain classification, sentiment analysis, named entity recognition (goals, people, activities), and intent detection. Processes every text input to the platform in real-time.
Graph neural network modeling accountability dynamics in Tribes (social groups). Learns influence patterns: who motivates whom, optimal group compositions for sustained engagement, and when peer interventions (kudos, challenges) are most effective.
PPO-based RL agent that learns the optimal timing, type, and intensity of behavioral interventions for each user. Balances immediate engagement against long-term behavioral change, explicitly modeling notification fatigue and diminishing returns.
Variational autoencoder that learns each user's behavioral baseline and flags statistically significant deviations — early indicators of burnout, disengagement, or wellbeing decline. Triggers graduated intervention protocols from gentle check-ins to escalated support suggestions.
Our model architecture follows a two-tier approach: shared foundation models that capture universal behavioral patterns, enhanced by lightweight per-user adapters that personalize predictions.
Large shared models (trained on collective behavioral patterns) provide robust base predictions. Lightweight LoRA adapters (0.5-2M parameters) fine-tune per user, enabling personalization without per-user training costs. Adapters converge within 7-14 days of user data.
User behavioral data never leaves their shard. We use federated averaging to improve shared model weights: local gradients computed on-device (or on user-specific server partitions) are aggregated without exposing raw data. Differential privacy noise (epsilon=8) provides formal privacy guarantees.
Models don't wait for batch retraining. Our custom CUDA kernel for personalized embedding updates enables real-time adaptation. When a user's behavior shifts (new job, life event), the model detects the distribution shift and accelerates adapter learning rate to re-converge within 48 hours.
Models that share users benefit from joint training. The NLP pipeline, embedding model, and Chronos predictor share lower encoder layers, enabling knowledge transfer: better sentiment understanding improves energy prediction, and vice versa. Trained end-to-end on multi-task loss.
Every model in the TimeStack suite is trained on NVIDIA GPUs, optimized through TensorRT, and served via Triton. Together, they form the most comprehensive AI system ever built for understanding and optimizing human behavior.