Custom Model Training
Make an Open Source Model Yours
Last updated: February 2026
We take an open source or open weights model and adapt it to your world. Whether that's full retraining, partial adaptation through transfer learning, or targeted fine-tuning on your proprietary data, the result is a model that understands your domain, your terminology, and your specific requirements. Fully yours: your IP, your infrastructure, your control. A complete engagement from data preparation through deployment and ongoing model maintenance.
Training Approaches
Every model starts with a different foundation and serves a different purpose. We assess your data, your domain, and your requirements to recommend the right approach, or a combination of approaches, to get you a model that performs where it matters.
Full Retraining
Complete retraining of an open source model on your datasets. This is the most thorough approach: we rebuild the model's knowledge from your domain data, resulting in maximum specificity and the strongest alignment with your terminology and requirements. Best suited when off-the-shelf models fundamentally lack the knowledge your domain requires.
Use Cases
- Unique or highly specialized domains
- Proprietary knowledge that doesn't exist in public training data
- Maximum customization and domain specificity
Partial Adaptation
We adapt an existing model's knowledge to your domain through transfer learning, preserving its general capabilities while adding your specific expertise. Faster to deploy and more cost-effective than full retraining, this approach works well when the base model already understands your general field but needs to learn your particular context.
Use Cases
- Faster time to deployment
- Cost-effective domain specialization
- Building on strong existing model foundations
Targeted Fine-Tuning
Refine how a model behaves on specific tasks using your internal or proprietary data. Fine-tuning adjusts the model's outputs, tone, formatting, and decision patterns to match your operational standards. This is the most focused approach: the model already knows the domain, it just needs to learn how you work.
Use Cases
- Task-specific behavior and output alignment
- Adjusting tone, format, and style to your standards
- Rapid iteration on model behavior
How We Work
We follow a structured process from initial assessment through production deployment. At each stage, your team is involved and informed. There are no black-box phases where we disappear and return with a model.
Discovery
We start by understanding your problem space: your current data landscape, domain requirements, model usage patterns, and success criteria. This phase determines which training approach fits and sets realistic expectations for timeline and performance.
Data Engineering
Your data is prepared, cleaned, validated, and augmented as needed. Data quality directly determines model quality, so this phase is thorough. We work with your data where it lives, under your security and privacy requirements.
Architecture Design
We select the optimal base model and training architecture for your requirements. This includes model size, training strategy, hardware requirements, and performance targets. You review and approve before we begin training.
Training & Iteration
Systematic training with continuous validation against your success criteria. Training is iterative: we run experiments, evaluate results, adjust parameters, and repeat until the model meets your benchmarks.
Evaluation
Rigorous testing against your real-world requirements, not just academic benchmarks. We evaluate for accuracy, consistency, edge cases, and the specific behaviors you need in production.
Deployment & Handoff
We deploy the model to your production environment and ensure it runs reliably under real workloads. Full documentation, monitoring setup, and knowledge transfer to your team. This isn't the end, it's the start of the model's operational life.
Data Security & Privacy
Your training data is your competitive advantage. We treat it accordingly, with security practices designed for organizations that take data protection seriously, whether that means on-premise processing, air-gapped environments, or full GDPR compliance.
On-Premise Processing
Training runs within your own infrastructure. Your data never leaves your environment. We bring the expertise, you keep the data.
Air-Gapped Options
For maximum isolation, we support fully air-gapped training environments with no external network connectivity. Complete data sovereignty by design.
GDPR Compliance
European data protection built into every engagement. Data processing agreements, lawful basis documentation, and full compliance with EU privacy regulations.
Data Deletion
Complete removal of all training data, intermediate artifacts, and model checkpoints at the end of each engagement. You retain the final model. We retain nothing.
The Model Lifecycle
Model training isn't a one-time project. It's an ongoing cycle. Your model goes into production, encounters real-world data, and gradually drifts from peak performance. Retrieval-Augmented Generation can bridge the gap temporarily, but RAG is a workaround, not a solution. At some point, your model needs actual retraining: new knowledge baked into its weights, not bolted on through retrieval. We structure every engagement around this reality.
Base Training
The foundation. Full retraining, adaptation, or fine-tuning to create a model that understands your domain. This is where domain knowledge, terminology, and behavioral patterns are established.
Adaptation & Fine-Tuning
Iterative refinement based on evaluation results, user feedback, and edge cases discovered during testing. The model is shaped to handle your specific tasks and operational requirements.
Production Deployment
The model goes live in your infrastructure. Serving optimization, monitoring setup, and integration with your existing systems. This is where the model starts generating real value and real feedback.
Post-Deployment Monitoring
Continuous tracking of model performance, output quality, and drift detection. As your domain evolves and new data accumulates, you'll see where the model's knowledge has gaps that RAG alone can't fill.
Continuous Training
When accumulated feedback and new data demand it, the model re-enters the training pipeline. New knowledge is baked into the model's weights, not patched through retrieval. The cycle begins again, and your model gets stronger with each iteration.
Common Questions
Let's Build a Model That Evolves With You
Share your requirements and data landscape. We'll design the right training approach and build a pipeline that keeps your model performing as your domain evolves.