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Custom large language model development, fine tuning, and deployment. We build domain specific language models that understand your business context and deliver accurate results every time.
From fine tuning existing models to training custom architectures, we build language models that understand your business better than any generic API.
We fine tune open source language models on your proprietary data using LoRA, QLoRA, and full parameter training. The result is a model that understands your domain vocabulary, tone, and business logic.
For organizations with unique requirements, we train language models from scratch or continue pretraining on domain specific corpora. Custom tokenizers, architectures, and training objectives tailored to your needs.
We deploy language models on your own infrastructure using optimized serving frameworks. All data stays within your network with zero cloud dependencies, meeting the strictest compliance requirements.
Rigorous evaluation against domain specific benchmarks, not just generic metrics. We measure accuracy, latency, throughput, and safety to ensure your model performs reliably in production scenarios.
Systematic prompt design, testing, and optimization for your specific workflows. We build prompt libraries, chain of thought templates, and few shot examples that maximize model accuracy and consistency.
We implement input validation, output filtering, jailbreak prevention, PII detection, and content safety layers. Your language model operates within defined boundaries and rejects harmful or off topic requests.
A structured approach to building custom language models. Every step is transparent, measurable, and designed to deliver a model that performs in the real world.
We work with your team to gather, clean, and format training data. We identify gaps, remove noise, and create high quality instruction response pairs for fine tuning.
We evaluate open source models against your requirements: domain fit, parameter count, inference speed, and licensing. We recommend the optimal starting point for your use case.
We train the model on your data using the most efficient technique for your budget and timeline. LoRA for quick iterations, full fine tuning for maximum quality.
We test the fine tuned model against domain benchmarks, adversarial inputs, and real world scenarios. Multiple rounds of evaluation ensure the model meets your accuracy targets.
We deploy the model with optimized inference, monitoring, and logging. Whether cloud or on premise, the model is production ready with proper scaling and fallback configuration.
We use the most advanced training, fine tuning, and serving frameworks to build language models that are accurate, fast, and cost effective.
Every industry has its own vocabulary, regulations, and knowledge requirements. We build language models that speak the language of your domain.
Domain specific language models for technical documentation, maintenance manuals, quality standards, and production process knowledge that general models cannot handle.
Custom models trained on product catalogs, customer interactions, and brand guidelines for personalized recommendations, customer service, and marketing content.
Medical language models trained on clinical literature, patient records, and treatment protocols. HIPAA compliant deployment for clinical decision support and documentation.
Models fine tuned on building codes, contract language, project documentation, and regulatory requirements for automated compliance checking and report generation.
Custom models for shipping documentation, customs regulations, supplier communications, and demand forecasting narratives that understand logistics vocabulary.
Language models trained on security protocols, incident reports, threat intelligence, and compliance frameworks for automated analysis and reporting.
Common questions about LLM development, fine tuning, and deployment.
Fine tuning makes sense when you need consistent domain expertise, lower per query costs at scale, data privacy guarantees, or responses that match a specific style and format. If you process thousands of queries daily in a specialized domain, a fine tuned model typically outperforms generic API calls.
We work with instruction response pairs, domain documents, conversation logs, and structured knowledge bases. As few as 500 to 1,000 high quality examples can produce significant improvements. We help you curate, clean, and format your data for optimal training results.
Cost depends on the base model size, training data volume, and compute requirements. A LoRA fine tune on a 7B parameter model with 5,000 examples typically takes 2 to 3 weeks. Larger models and RLHF alignment take longer. We provide detailed estimates after reviewing your requirements.
Yes. We deploy fine tuned models on your infrastructure using optimized serving frameworks like vLLM and Ollama. This keeps all data and inference on premise, meeting strict compliance requirements. We also optimize models for your specific hardware to maximize throughput.
Custom models offer better domain accuracy, lower latency, predictable costs, and complete data control. API based models are better for general purpose tasks and rapid prototyping. Many clients use both: APIs for exploration and custom models for production workloads with specific requirements.
Tell us about your domain, data, and use case. We will design a custom LLM strategy that delivers measurable results.