Architecture Assessment
Compare prompting, RAG, tool use, fine-tuning, and hybrid approaches against the requirement.
SERVICE / LLM ENGINEERING
Adapt models when the evidence supports it.
We help teams determine when prompting, retrieval, tool use, or model adaptation is the right technical approach—and when those methods should be combined.
Fine-tuning is useful when a task requires consistent behavior, specialized structure, domain language, or learned patterns that cannot be supplied efficiently at inference time.
BASE MODEL
DATASET
TRAIN
EVALUATE
VERSION
SERVE
CEREBRIXSYSTEM
01 / SYSTEM VIEW
The model is one component. The operating system around it creates dependability.
02 / WHAT WE BUILD
Capabilities designed around real operating requirements.
Dataset Engineering
Collect, clean, label, balance, version, and govern training and evaluation examples.
Supervised Fine-Tuning
Adapt capable base models to specialized output patterns and domain tasks.
Evaluation Design
Define task-specific metrics, rubrics, holdout sets, and human review processes.
Model Deployment
Package inference, scaling, versioning, monitoring, rollback, and cost controls.
Continuous Improvement
Use production evidence to improve datasets, prompts, routing, and model behavior.
03 / CORE CAPABILITIES
Engineering depth across the complete solution.
- Use-case assessment
- Dataset curation
- Labeling guidance
- Synthetic data strategy
- Fine-tuning pipelines
- Evaluation harnesses
- Model comparison
- Inference optimization
- Model routing
- Version control
- Safety testing
- Deployment monitoring
04 / REPRESENTATIVE USE CASES
[ Structured extraction ][ Domain classification ][ Specialized writing ][ Consistent formatting ][ Intent recognition ][ Entity normalization ][ Task-specific scoring ][ Model behavior adaptation ]
05 / DELIVERY APPROACH
Structured delivery without unnecessary ceremony.
Establish a baseline
Measure a strong prompt, retrieval, and tool-enabled solution before training.
Define the task
Specify desired behavior, boundaries, data requirements, metrics, and deployment constraints.
Engineer the dataset
Prepare representative, licensed, balanced, and reviewable training and evaluation data.
Train and compare
Run controlled experiments against the baseline and analyze failure categories.
Deploy with controls
Version models, monitor drift, manage rollback, and retain evaluation coverage.
06 / QUALITY + CONTROL
Fine-tuning should outperform a measured baseline.
We use holdout data, task rubrics, failure analysis, cost, latency, and maintainability to decide whether adaptation earns its complexity.
LET'S DEFINE THE RIGHT SYSTEM
Determine whether your use case needs model adaptation.
Start with the task, available data, current baseline, and operating constraints.