LLM/SLM Fine-Tuning
Customize large and small language models for your specific domain, terminology, and use cases.
Improve accuracy, reduce costs, and maintain control over your AI with models trained on your data.
Why Fine-Tune?
Off-the-shelf LLMs are powerful but generic. Fine-tuning creates models that truly understand your business.
Domain Expertise
Models that understand your industry terminology, products, and processes
Higher Accuracy
Significantly better performance on your specific tasks vs. generic prompting
Lower Costs
Shorter prompts and fewer tokens needed when the model already knows context
Consistent Behavior
Reliable formatting, tone, and output structure for production use
Competitive Advantage
AI capabilities your competitors can't easily replicate
Supported Models
OpenAI GPT-4
Industry-leading capabilities with fine-tuning support for enterprise use cases.
Best for: Complex reasoning, multi-step tasks, highest accuracy requirements
Anthropic Claude
Strong reasoning with emphasis on safety and helpfulness.
Best for: Customer-facing applications, nuanced conversations, safety-critical use cases
Meta Llama 3
Open-source with full control over deployment and customization.
Best for: On-premise deployment, data privacy requirements, full model control
Mistral
Efficient open-source models with strong performance-to-cost ratio.
Best for: Cost-sensitive applications, high-volume processing, edge deployment
Fine-Tuning Use Cases
Common applications where custom models significantly outperform generic LLMs.
Domain-Specific Assistants
Build AI assistants that understand your industry terminology, products, and processes — far more accurate than generic models.
Document Processing
Train models to extract, classify, and process your specific document types — from BOMs to invoices to technical specs.
Code Generation
Fine-tune models on your codebase, APIs, and conventions to generate code that follows your standards.
Customer Support
Create models trained on your products, policies, and support history for more accurate and consistent responses.
Knowledge Extraction
Train models to extract structured data from unstructured sources specific to your domain.
Content Generation
Build models that generate content matching your brand voice, technical accuracy, and style guidelines.
Fine-Tuning vs. RAG vs. Prompt Engineering
Understanding when to use each approach — and when to combine them.
| Approach | Best For | Data Required | Implementation Time | Cost |
|---|---|---|---|---|
| Prompt Engineering | Quick wins, prototypes, general tasks | None | Hours to days | $ |
| RAG (Retrieval) | Dynamic knowledge, documents, FAQs | Document corpus | 1-4 weeks | $$ |
| Fine-Tuning | Domain expertise, consistent behavior, cost reduction | 500-10,000+ examples | 4-8 weeks | $$$ |
| Combined Approach | Enterprise production systems | Both training data + documents | 6-12 weeks | $$$$ |
Start with Prompt Engineering
Always start here. If you can solve 80% of the problem with good prompts, you may not need more complex solutions.
Add RAG for Knowledge
When your use case requires accessing company documents, policies, or frequently updated information.
Fine-Tune for Mastery
When you need the model to deeply understand your domain, use your terminology, and follow your patterns consistently.
Our Process
A structured approach from assessment to production deployment.
Assessment & Strategy
We evaluate your use case, data assets, and objectives to determine the optimal fine-tuning approach.
- Define specific business outcomes and success metrics
- Audit existing data quality, volume, and relevance
- Select base model (GPT-4, Claude, Llama, Mistral, etc.)
- Choose between full fine-tuning, LoRA, or prompt engineering
Outcome
A clear strategy with model selection and data requirements.
Data Preparation
We curate, clean, and format your domain-specific data for optimal model training.
- Collect and organize training data from your systems
- Clean and deduplicate data for quality
- Create instruction-response pairs and conversation formats
- Split data into training, validation, and test sets
Outcome
Production-ready training dataset optimized for your use case.
Model Training
We train your custom model using industry-leading techniques and infrastructure.
- Configure training parameters and hyperparameters
- Execute training with monitoring and checkpoints
- Apply techniques like LoRA, QLoRA, or full fine-tuning
- Implement safety guardrails and content filtering
Outcome
A trained model customized to your domain and terminology.
Evaluation & Iteration
We rigorously test the model against your success criteria and iterate as needed.
- Benchmark against baseline and competitor models
- Test on held-out data and edge cases
- Measure accuracy, latency, cost, and user satisfaction
- Iterate on training data and parameters to improve
Outcome
A validated model meeting your performance requirements.
Deployment & Optimization
We deploy your model to production and optimize for performance and cost.
- Deploy to your cloud or our managed infrastructure
- Implement caching, batching, and optimization
- Set up monitoring, logging, and alerting
- Establish processes for ongoing model updates
Outcome
A production-ready model with operational excellence.
Frequently Asked Questions
Real Results, Real Impact
See how leading companies have transformed their operations with ATLAS.
TechCorp
SaaS Company • 200+ employees“ATLAS completely transformed our revenue operations. What used to take our team 40 hours per week now takes just 10. The AI handles all the repetitive follow-ups, data entry, and lead enrichment.”
BuildRight Construction
ConstructionAutomated vendor follow-ups and RFQ tracking across all projects.
Finova Group
FinTechEnriched leads instantly and automated personalized outreach sequences.
MedHealth Solutions
HealthcareStreamlined patient onboarding and reduced administrative overhead.
What Our Clients Say
Hear directly from the leaders who've transformed their businesses with ATLAS.
“It feels like having an extra teammate that never gets tired. Our follow-up rate went from 40% to 95%. We’re closing deals we would have lost before.”
Sarah Johnson
VP Sales, TechCorp
“We stopped losing deals to slow follow-ups. ATLAS keeps every opportunity moving without us having to chase. Our team can focus on closing, not admin.”
Mike Rodriguez
CEO, BuildRight Construction
“The ROI was obvious within 30 days. We’re booking 3x more meetings with the same team size. ATLAS pays for itself many times over.”
Amanda Lee
Head of Growth, Finova Group
“We tried three other automation tools before ATLAS. Nothing else could handle our complex workflows. This is the real deal.”
James Park
COO, MedHealth Solutions
Ready to Build Your Custom Model?
Let's discuss your use case and data. We'll help you determine if fine-tuning is right for you and outline the best approach.