Map the context
We model the user's domain, workflows, tools, permissions, and quality targets before choosing architecture.
Custom distilled intelligence
Imbrial AI
AI, Your Way.
We turn frontier-model knowledge, private context, and real usage patterns into tiny custom models that feel native to each customer.
Distill
Capture rich reasoning traces from frontier systems.
Specialize
Fit compact models to user context and constraints.
Deploy
Run on your cloud, private GPU, VPS, or edge stack.
The Imbrial idea
Smaller models should know more about the world they serve.
Imbrial AI builds customer-specific model systems: compact enough to run efficiently, structured enough to preserve the nuance of larger models, and tuned around the tasks, data, taste, policies, and latency targets that matter to each team.
Platform
From frontier intelligence to production model systems.
Imbrial is designed around the full loop: understand a use case, collect high-value traces, train compact specialists, evaluate them, and deploy them where the customer wants control.
Distill the structure
Frontier-model reasoning, examples, constraints, and feedback become training signal for compact models.
Train specialists
Small models are optimized for task mix, data shape, mixed precision, latency, and deployment hardware.
Ship and improve
Models deploy into private infrastructure with monitoring, evaluation, rollback, and continuous adaptation.
Models
Tiny does not have to mean generic.
Personalized inference
Model behavior adapts to the customer, not the other way around: voice, context, rules, tools, and recurring decisions.
Mixed model systems
LLMs can work beside estimators, classifiers, retrieval, simulators, and domain models in one inference loop.
Private deployment
Run on rented GPU time, private cloud, VPS-backed services, or customer-managed infrastructure.
Efficient arithmetic
Precision and compute choices can be matched to the data, task, and tolerance of each model component.
Evaluation memory
Every deployment should learn from tests, failures, review traces, and user feedback without losing control.
End-to-end ownership
The customer brings the problem and the compute. Imbrial brings the modeling, training, deployment, and user loop.
Company
Built by AI researchers and operators.
Imbrial AI combines applied product instincts with deep model research: practical AI for business workflows, compact custom models, and technical systems that can be deployed where customers want control.
Carrie Zhou
CEO
Carrie leads company strategy, customer development, partnerships, and go-to-market execution. Previously at Amazon, she has developed AI products for warehousing, customer acquisition, and social media branding.
Grant Boquet
CTO
Grant leads model architecture, distillation systems, training infrastructure, and deployment design. His work spans GeoGPT, scientific workflow intelligence, domain-specific foundation models, uncertainty-aware AI, and prior applied ML and statistics roles at Lawrence Livermore National Laboratory and Metron.