Design Your Own Custom, Private LLM at Scale

Building on a Custom LLM Architecture Design gives you full control over data handling, latency, costs, and model behavior, which is essential for proprietary use cases or highly regulated industries. Unlike API models, a custom LLM model architecture can be tailored to your domain and compliance automation needs.

Training a custom LLM requires large volumes of clean, high-quality, and domain-relevant text. As part of the Custom LLM Architecture Design process, we assist in sourcing, cleaning, and structuring public, licensed, or internal datasets for optimal performance.

Timelines vary, but most Custom LLM Architecture Design projects take 8–16 weeks, depending on model size, compute access, and whether fine-tuning or custom transformer design is required.

The ideal model size depends on your use case, latency goals, and budget. We guide you in selecting the right LLM model architecture from compact models for real-time tasks to large language-scale deployments requiring advanced custom transformer design.

Custom LLMs need routine monitoring, prompt tuning, periodic retraining, and security updates. As part of our Custom LLM Architecture Design services, we provide tools and managed support for continuous model health.

We build custom LLM architectures with strict access controls, audit logging, and deployment options that support GDPR, HIPAA, and SOC 2. Our design process also includes privacy-focused LLM model architecture choices like on-prem hosting and zero-data-retention inference.

Yes. Our Custom LLM Architecture Design services include integrations with retrieval-augmented generation (RAG), semantic search, vector databases, and agentic frameworks like AutoGen or CrewAI, powered by flexible custom transformer designs.