BxW Solutions

Fine-Tuning & Model Training

Fine-TuningModel TrainingEvaluationSynthetic Data

For AI systems with specific language, recurring classification logic or strict output formats, BxW prepares training data, synthetic examples and evaluation processes. Fine-tuning is used where context, prompting or retrieval alone do not carry the required precision.

Fine-Tuning & Modelltraining

Context

We develop training and evaluation foundations for AI systems that need more stable classification, extraction or output behavior.

Real examples, synthetic data, labels, output formats, test sets, model comparisons and fine-tuning steps form a quality cycle.

Model behavior becomes easier to test, compare and improve beyond prompt-level adjustments.

Implementation

Scope & Building Blocks

Data Basis

Training dataSynthetic dataExamplesLabels

Training

Fine-tuningModel comparisonParametersIteration

Evaluation

Test setsQuality criteriaError patternsComparison

Integration

Output formatsAPIsWorkflowsMonitoring

AI Agents

For recurring tasks that need a clear role, relevant context and controlled access to tools and systems.

AI Agent•AI

System Layer

Multi-Agent Systems

When complex work needs multiple roles: clear tasks, handovers, reviews and controlled execution.

Multi-Agent System•AI

AI knowledge system with RAG, document context and controlled answers

AI Knowledge Systems

Documents, data sources and business logic become a stronger foundation for search, answers and AI-assisted work.

Knowledge System•AI