LLM fine-tuning
Adapt large language models to your domain for better accuracy, tone, and task performance.
Custom Model Training
Models trained on your domain for accuracy that matters
Off-the-shelf models only get you so far. We fine-tune and train custom models on your data so they understand your terminology, your edge cases, and your quality bar.
- Fine-tuning on your domain-specific data
- Custom classification and NLP models
- Continuous evaluation and retraining pipelines
Operations insights
Processed
1,284
Avg. handling
2.4m
Automation
86%
Throughput
↗ trending upWhat we deliver
Models built for your domain
From data preparation to production deployment, we own the full model lifecycle.
Data preparation
Clean, label, and structure your training data for maximum model quality.
Custom classifiers
Purpose-built classification models for your specific categories, labels, and decision criteria.
Embedding models
Custom embeddings optimized for your semantic search and retrieval use cases.
Evaluation frameworks
Rigorous benchmarking against your quality standards with automated test suites.
Retraining pipelines
Automated pipelines that keep models current as your data and requirements evolve.
How it works
Precision that generic models cannot match
Your domain has terminology, patterns, and edge cases that general models miss. We train on your data, evaluate against your standards, and iterate until the model meets your production bar.
- Domain-specific training data curation and labeling
- A/B evaluation against baseline models
- Automated retraining triggered by data drift
How we work
From raw data to a production model
A structured approach that delivers reliable, accurate models.
- 01
Assess data and goals
We review your data assets and define the task, metrics, and success criteria.
- 02
Prepare training data
We clean, label, and augment your data for optimal training quality.
- 03
Train and evaluate
We train candidate models, benchmark performance, and iterate on the best approach.
- 04
Deploy to production
We deploy the model with monitoring, versioning, and rollback capabilities.
- 05
Monitor and retrain
We track performance in production and retrain as data distribution shifts.
The payoff
What custom models unlock
Accuracy and reliability that generic models cannot provide.
Domain accuracy
Models that understand your terminology and edge cases, not just generic text.
Faster processing
Smaller, specialized models that run faster and cost less than general-purpose alternatives.
Data control
Your data stays in your environment - no sending proprietary content to third-party APIs.
Scalable foundation
A model you own and can extend to adjacent use cases over time.
Measurable improvement
Clear before-and-after metrics proving the model outperforms the baseline.
Stays current
Retraining pipelines ensure the model adapts as your business evolves.
Under the hood
Technologies we build with
Frameworks
- PyTorch
- TensorFlow
- Hugging Face Transformers
Training
- LoRA
- QLoRA
- PEFT
- DeepSpeed
MLOps
- MLflow
- Weights & Biases
- DVC
- BentoML
Infrastructure
- AWS SageMaker
- Azure ML
- GPU clusters
- Docker
FAQ
Questions, answered
Still deciding? These are the things teams ask us most - and if yours isn't here, a 30-minute call will cover it.
When you need consistent accuracy on domain-specific tasks, lower latency, reduced costs at scale, or data privacy that prompt-based approaches cannot guarantee.
It depends on the task. Classification may need a few hundred labeled examples; fine-tuning an LLM typically benefits from thousands. We assess your data and advise honestly.
Absolutely. We often fine-tune Llama, Mistral, or other open models so you own the result with no ongoing API costs.
Rigorous train/test splits, cross-validation, regularization, and evaluation on held-out data that the model never sees during training.
You do. Full ownership of weights, training data, and documentation.
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Let's scope your project
Tell us what you're trying to solve. We'll come back with a clear, fixed-scope plan - no jargon, no obligation.