Language Models Built for Your Domain
Generic LLMs give generic answers. We build, fine-tune, and deploy domain-specific language models that understand your industry, your data, and your users — delivering accuracy that off-the-shelf models can't match.
Trusted by innovative teams worldwide
LLM Engineering Credentials
Our ML engineers bring deep expertise in transformer architectures, training pipelines, and production deployment.
Full LLM Lifecycle — From Data to Deployment
Whether you need a fine-tuned model, a custom training pipeline, or a production-ready deployment — we handle every stage of the LLM development process.
LLM Strategy & Use Case Design
We identify your highest-value language model use cases, assess data readiness, and design an LLM strategy that balances capability with cost — fine-tune vs RAG vs full training.
Data Pipeline & Preparation
Data collection, cleaning, annotation, and formatting for model training. We build automated pipelines that continuously improve your training data quality over time.
Custom Model Training
Fine-tuning foundation models (GPT, Claude, Llama, Mistral) on your domain data. Full custom training for specialized use cases where foundation models fall short.
Model Fine-Tuning & Optimization
RLHF, DPO, and LoRA-based fine-tuning that maximizes accuracy while minimizing compute costs. We optimize for your specific quality metrics, not generic benchmarks.
Production Deployment
Model serving infrastructure with auto-scaling, caching, and fallback strategies. Optimized for latency, throughput, and cost — whether on-prem, cloud, or edge.
Monitoring & Continuous Improvement
Production monitoring for quality drift, hallucination detection, and user satisfaction. Automated retraining pipelines that keep your model sharp as data evolves.
Ready to Build a Language Model That Understands Your Domain?
Book a free LLM strategy session — we'll assess your data and recommend the most cost-effective approach.
Language models that know your industry — not just language.
We fine-tune and deploy LLMs that understand your terminology, your workflows, and your quality standards — delivering accuracy that generic API calls can't match.
LLM Development Grounded in Practical Reality
At OpenMalo, we don't chase model size for its own sake. We build the smallest, fastest, cheapest model that delivers the accuracy your use case requires.
Why Companies Choose OpenMalo for LLM Development
We've shipped language models into production for finance, healthcare, legal, and SaaS — and understand that accuracy in production is what matters.
Tell Us About Your LLM Project
Share your use case and our ML engineers will respond with a tailored approach within one business day.
Our Engagement Process
Assessment & Strategy
Use case evaluation, data audit, model selection (fine-tune vs RAG vs custom training), and cost-benefit analysis. Clear recommendation with projected accuracy and costs.
Data Pipeline
Data collection, cleaning, annotation, and validation. Building the training dataset that determines your model's quality ceiling.
Training & Fine-Tuning
Model training with rigorous evaluation at each checkpoint. Hyperparameter optimization, ablation studies, and benchmark comparison against baseline models.
Evaluation & Testing
Domain-specific test suites, adversarial testing, hallucination detection, and human evaluation. We don't ship until accuracy meets your production bar.
Deployment & Monitoring
Production deployment with auto-scaling, A/B testing, quality monitoring, and automated retraining triggers. Ongoing optimization as your data and requirements evolve.
What Our Clients Say
“OpenMalo fine-tuned a medical summarization model for our clinical notes that outperformed GPT-4 on our internal benchmarks — at 1/8th the inference cost. Our clinicians save 40 minutes per day on documentation. The ROI was clear within the first month.
“We needed a legal document analysis model that understood our jurisdiction-specific terminology. OpenMalo's fine-tuned model reduced contract review time by 65% and caught clause risks that our previous keyword-based system missed entirely.
“The cost difference was the deciding factor. We were spending $38K/month on GPT-4 API calls for our customer support automation. OpenMalo's fine-tuned model handles the same volume at $6K/month with better accuracy on our product-specific questions.
65% Faster Contract Review with Domain-Specific LLM
Legal Document Analysis Model for LegalEdge AI
How we built a fine-tuned language model that reduced contract review time by 65% and identified clause risks with 96% accuracy — outperforming generic LLMs on jurisdiction-specific legal terminology.
Generic LLMs that didn't speak legal
LegalEdge AI's contract review tool used GPT-4, but it consistently missed jurisdiction-specific clause risks, confused similar-sounding legal terms, and hallucinated case references that didn't exist.
Our Approach: Curated 15,000 annotated contract examples across 12 contract types. Fine-tuned Llama 3 70B with legal-specific instruction tuning and RLHF from senior attorneys. Built custom evaluation suite testing clause identification, risk scoring, and jurisdiction awareness. Deployed on-prem for data security. 12-week engagement.
Read Full Case StudyFrequently Asked Questions
Fine-tuning modifies the model itself to be better at your specific tasks — like teaching it your domain language. RAG retrieves relevant context from your data at query time and feeds it to the model. Fine-tuning is better for style, format, and domain knowledge. RAG is better for factual accuracy over large document sets. We often combine both.
Explore Related Services
Discover complementary solutions that work together to accelerate your digital transformation.
