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Model Training & Fine-tuning

Fine-tune foundation models for your specific use case. We handle data preparation, training infrastructure, and optimization to get the best performance.

See training approaches

Why fine-tuning beats prompting

Foundation models are powerful, but generic. Fine-tuning adapts them to your domain, your terminology, and your quality standards — delivering results that prompt engineering alone cannot achieve.

10-50x
cost reduction vs large model APIs
2-4 weeks
typical fine-tuning cycle
90%+
accuracy achievable on domain tasks
100ms
inference latency targets
Training Approaches

Choose the right approach

Different problems require different training strategies.

Fine-tuning

Adapt foundation models to your domain

LLMs for legal, medical, financial terminology

Data needed 1,000 - 10,000 examples
Timeline 2-4 weeks
Custom Training

Train models from scratch for specialized tasks

Proprietary classification, anomaly detection

Data needed 10,000+ labeled examples
Timeline 4-8 weeks
Multi-modal

Combine text, image, audio, or video understanding

Document + image analysis, video understanding

Data needed Varies by modality
Timeline 6-12 weeks
Data Requirements

How much data do you need?

Data quantity and quality directly impact model performance.

1,000+

Minimum Viable

Enough for basic fine-tuning with transfer learning

10,000+

Production Ready

Robust performance across edge cases

100,000+

Enterprise Scale

State-of-the-art accuracy for critical applications

Research-Backed

Limited training data? We wrote the book on it.

Our IEEE-published survey covers synthetic data generation techniques — from prompt engineering to reinforcement learning — now applied to enterprise fine-tuning projects.

Explore our research
Engagement Timeline

From data to deployment

1

Data Assessment

1-2 weeks

Evaluate data quality, coverage, and labeling needs

2

Baseline

1-2 weeks

Establish performance benchmarks and evaluation metrics

3

Training

2-6 weeks

Iterative training with hyperparameter optimization

4

Deployment

1-2 weeks

Production deployment with monitoring and optimization

Who This Is For

Teams ready to customize

Whether you have domain expertise to encode or data assets to leverage, we help you build models that perform.

AI/ML Teams

Needing specialized expertise for complex training tasks

Product Teams

Building AI features that require domain-specific models

Research Teams

Exploring novel architectures or training approaches

Data Teams

With data assets ready to power custom models

Capabilities

End-to-end model development

Data Preparation

Clean, label, and augment your training data for optimal results.

Model Selection

Choose the right architecture for your use case and constraints.

Fine-tuning

Adapt foundation models to your specific domain and requirements.

Evaluation

Rigorous testing with relevant metrics and human evaluation.

Optimization

Compress and optimize models for production deployment.

Deployment

Deploy trained models with monitoring and rollback capabilities.

What We Deliver

Production-ready deliverables

Trained Model

Production-ready model optimized for your use case and infrastructure.

Evaluation Report

Comprehensive metrics, benchmarks, and performance analysis.

Training Pipeline

Reproducible training code and infrastructure for future iterations.

Deployment Package

Containerized model with serving infrastructure and APIs.

Data Documentation

Data lineage, preprocessing steps, and labeling guidelines.

Knowledge Transfer

Training sessions on model maintenance and retraining workflows.

Our Process

A rigorous approach to training

1

Data Audit

Assess data quality, identify gaps, and plan labeling or augmentation.

2

Experimentation

Rapid iteration on architectures, hyperparameters, and training strategies.

3

Optimization

Fine-tune for production constraints: latency, cost, accuracy trade-offs.

4

Deployment

Ship to production with monitoring, A/B testing, and rollback capabilities.

100+
Models trained
1B+
Training samples processed
40%
Average accuracy improvement
3x
Faster inference
Off-the-Shelf Limits

The limits of off-the-shelf models

Generic models weren't trained on your data, your terminology, or your edge cases.

  • Accuracy gaps on domain-specific language and concepts
  • Hallucinations and errors on specialized topics
  • High API costs at scale with hosted models
  • Data privacy concerns with third-party model providers
  • No competitive differentiation from generic capabilities

Executive Takeaway

Fine-tuned models deliver superior accuracy, lower costs at scale, and data privacy — transforming AI from a commodity into a competitive asset.

1
Inventory your data assets and labeling capabilities
2
Identify use cases where generic models underperform
3
Calculate total cost of ownership vs API-based approaches
4
Start with a focused fine-tuning pilot to validate ROI
Model Training

Ready to train your custom model?

Let's discuss your data, use case, and performance requirements.

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We respond to training inquiries within 24 hours

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Our ML team will reach out within 24 hours to discuss your training project.

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