FIG 1.1 · CUSTOM LLM DEVELOPMENT
Fine-tuned language models for your data and domain.
Fine-tuned models trained on your proprietary data, deployed in production with monitoring, evaluation pipelines, and cost controls. Not a ChatGPT wrapper.
WHAT IT IS
A custom LLM is a large language model adapted to your business — your vocabulary, your tone, your decision logic. We either fine-tune an open model (LLaMA 3, Mistral) on curated examples, or build instruction-tuned adapters on top of hosted APIs.
The deliverable is a production endpoint with evaluation harnesses, observability, and a rollback plan. Every prompt is logged, every response graded, every regression caught before it hits users.
Custom LLMs shine when domain language is non-standard (legal, medical, financial), when latency or cost targets are strict, or when data sovereignty matters.
WHEN TO USE IT
Domain-specific language
Your content uses terminology, acronyms, or citation formats that off-the-shelf models handle poorly.
Data privacy constraints
You need an on-prem or private-cloud deployment so customer data never leaves your infrastructure.
Cost or latency targets
You need millions of calls per day at a cost ceiling a hosted API cannot meet.
Consistent voice and structure
Outputs need to follow a strict schema or tone that prompt engineering alone cannot enforce.
OUR APPROACH
- 01
Data audit and sampling
We inventory what training data you have, where it lives, and what it is missing. Quality is worth ten times quantity.
- 02
Base model selection
We benchmark 3–5 candidate models against your evaluation set. Winner goes to fine-tuning.
- 03
Fine-tuning and evaluation
LoRA or full fine-tune depending on scale. Automated eval harness runs on every checkpoint.
- 04
Deployment and monitoring
Deploy to Modal, AWS, or your infrastructure. Observability dashboards from day one.
TECH STACK
FAQ
About custom llm development.
Start with a scoping call.
Thirty minutes. No pitch. We audit what you’re building, tell you what we’d do differently, and let you decide.