Custom LLM development adapts a language model to a specific domain through prompt architecture, retrieval over your own content, fine-tuning, or self-hosted deployment. It does not usually mean training a model from scratch, which costs millions and is the wrong answer for almost every business problem.
Four levers, in order of cost
There are four ways to make a model behave like it works for you, and they differ in cost by orders of magnitude. Prompt and context architecture is nearly free and solves more than people expect. Retrieval grounding costs weeks and solves factual accuracy. Fine-tuning costs more and is worth it for tone, format and narrow classification. Training from scratch costs millions and is almost never justified.
The engineering discipline is refusing to reach for an expensive lever when a cheap one would do. We work up the list, measuring at each stage, and stop at the point where the model meets the accuracy bar you set.
Process
How we deliver it
Process flow for Custom LLM development
01
Define the task
Write the evaluation set first: real inputs with correct outputs.
02
Baseline
Measure a general model with good prompting before spending anything.
03
Ground or tune
Add retrieval for facts, fine-tune for tone, format or classification.
04
Evaluate honestly
Score against held-out cases, not the examples used to build it.
05
Deploy
Hosted API or self-hosted, with cost monitoring and a rollback path.
Deliverables
What you receive
A working model pipeline meeting the agreed accuracy threshold
Evaluation set and scored results, including failure cases
Prompt architecture, retrieval configuration or tuned weights — all yours
Cost per request analysis at your expected volume
Deployment, monitoring and a documented rollback procedure
Engagement shape
A baseline and evaluation phase of two to three weeks, then a scoped build. We recommend the baseline as a standalone first step so the spend is informed.
Stack decisions follow the problem. This is where we usually start, not a fixed menu.
Frequently asked
Questions we get about this
Should we fine-tune or use retrieval?
Retrieval for facts, fine-tuning for behaviour. If the model needs to know your current pricing, retrieval — a fine-tune bakes in a snapshot that goes stale. If it needs to write in your house style or classify into your categories, fine-tuning. Getting this backwards is the most expensive mistake in the category.
Can we run this on our own servers?
Yes, with open-weight models like Llama or Mistral. You trade some output quality and take on GPU infrastructure cost, in exchange for data never leaving your environment. For regulated or government-adjacent work that trade is often worth it, and we will size the hardware honestly before you commit.
Talk it through before you commit
A discovery call is a working session on your constraint, not a sales pitch.
