Build LLMs That Speak Your Language
We fine-tune and build custom large language models that understand your domain deeply — delivering better accuracy, lower inference costs, and complete data ownership compared to generic APIs.
Trusted by innovative teams worldwide
Deep LLM Engineering Expertise
Our team has hands-on experience across every major LLM platform and fine-tuning framework.
Custom LLM Development, End to End
From training data curation to production serving — every step of building an LLM that's truly yours.
Training Data Curation
We build high-quality training datasets from your proprietary content — documents, conversations, code, support tickets — with deduplication, cleaning, and quality scoring.
Fine-Tuning & PEFT
LoRA, QLoRA, and full fine-tuning on base models like Llama, Mistral, and Gemma — optimized for your domain with minimal compute using parameter-efficient techniques.
RLHF & Alignment
Reinforcement learning from human feedback to align model outputs with your brand voice, accuracy standards, and safety requirements.
Evaluation & Benchmarking
Custom evaluation suites measuring domain accuracy, hallucination rates, instruction following, and task-specific metrics — tested against GPT-4 and Claude baselines.
Model Optimization & Quantization
Quantization (GPTQ, AWQ), distillation, and speculative decoding to reduce inference costs by 60–80% while maintaining quality.
Production Serving Infrastructure
vLLM, TGI, or TensorRT-LLM serving with auto-scaling, load balancing, and monitoring — deployed on your cloud or air-gapped infrastructure.
Tired of API Costs and One-Size-Fits-All Models?
Let's build an LLM that knows your domain and runs on your terms. Free evaluation call.
A model that thinks like your best domain expert.
Generic LLMs are impressive but imprecise. Custom fine-tuned models deliver higher accuracy on your tasks at a fraction of the inference cost — with full control over data privacy.
Custom LLMs for Regulated Industries
In fintech and healthcare, sending data to third-party APIs creates compliance risks. Custom LLMs keep your data in your environment while outperforming generic models on your tasks.
Why Teams Choose Us for Custom LLM Projects
We've fine-tuned models for financial document analysis, legal contract review, medical charting, and domain-specific code generation.
Tell Us About Your LLM Needs
Share your use case and data landscape. We'll respond with a fine-tuning strategy and cost comparison within 48 hours.
Our Engagement Process
Use Case & Data Assessment
Evaluate your tasks, data quality, and volume to determine if fine-tuning is the right approach and which base model fits best.
Data Preparation
Curate, clean, and format training datasets with quality scoring and deduplication — the most important step for model quality.
Fine-Tuning & Alignment
Parameter-efficient fine-tuning with iterative evaluation, followed by RLHF alignment to match your quality standards and safety requirements.
Optimization & Benchmarking
Quantization, distillation, and serving optimization — benchmarked against commercial APIs on your actual evaluation suite.
Deploy & Monitor
Production serving infrastructure with auto-scaling, monitoring dashboards, and continuous improvement pipelines.
What Our Clients Say
“We were spending $28K/month on GPT-4 API calls for document analysis. OpenMalo fine-tuned a Llama 3 model that performs better on our specific documents and costs $4K/month to self-host. The ROI was immediate.
“OpenMalo was honest about what fine-tuning could and couldn't do for our use case. They recommended RAG for one task and fine-tuning for another. That kind of honesty is rare in this space.
“Our custom LLM handles financial report summarization with 94% accuracy compared to 76% from the generic API. The compliance team can actually trust the output now, which changed how we operate.
$288K Annual Savings with Custom Financial LLM
Domain-Specific LLM for Financial Document Analysis
How we fine-tuned a custom LLM that outperforms GPT-4 on financial document analysis tasks while reducing inference costs by 85% — saving $288K annually for a mid-size fintech.
Generic APIs too expensive and inaccurate for financial docs
LedgerAI was processing 50,000+ financial documents monthly using GPT-4 API calls — high costs, inconsistent accuracy on financial terminology, and data privacy concerns from sending client documents to external APIs.
Our Approach: Curated 15,000 high-quality financial document examples, LoRA fine-tuned Llama 3 8B, quantized to 4-bit with AWQ, deployed on 2x A100 GPUs in client VPC — accuracy exceeded GPT-4 on all domain benchmarks.
Read Full Case StudyFrequently Asked Questions
Prompt engineering works for general tasks. RAG is best when you need the model to reference specific documents. Fine-tuning is ideal when you need the model to learn domain-specific patterns, terminology, or output formats that prompting can't reliably achieve. We help you decide during our feasibility assessment.
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