Service

Model Fine-Tuning and RAG Integration

Models that speak your industry's language, trained on your data.

Generic foundation models don't know your products, your terminology, your clients, or your regulatory context. Fine-tuning trains a model on your proprietary data, producing dramatically better accuracy, consistency, and domain alignment than prompt engineering alone. We handle the full pipeline: data preparation, training, evaluation, and integration into your existing software stack.

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What this covers

Training Data Preparation

Clean, structure, and format your existing data assets into high-quality training sets with deduplication and quality filtering.

Fine-Tuning Execution

Run supervised fine-tuning, RLHF, or DPO training on base models using your infrastructure or ours, with full experiment tracking.

Evaluation & Benchmarking

Rigorous evaluation against domain-specific benchmarks to quantify improvement over the base model.

Integration & Deployment

Package the fine-tuned model and integrate it into your application stack with versioning and rollback support.

How the engagement runs

  1. 01

    Data Audit

    Assess your existing data assets for quality, volume, and suitability for training.

  2. 02

    Dataset Construction

    Build and validate the training dataset, including augmentation if needed.

  3. 03

    Training & Evaluation

    Execute training runs with hyperparameter optimization and comprehensive evaluation.

  4. 04

    Deployment & Handover

    Deploy the model into production with full documentation and monitoring setup.

What you get

40–80% Accuracy improvement vs. base model
3–5x Reduction in prompt complexity
100% Data stays on your infrastructure
Weeks From data to production model

Figures are drawn from Senteras engagements and are illustrative of typical results. Outcomes vary by data quality, infrastructure and scope.

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Start with a conversation, not a proposal

Thirty minutes. We will tell you what we would change first, and whether you need us at all.

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The firm behind the firm