Custom AI models
In short
A custom AI model is one adapted to your domain — through fine-tuning, retrieval over your own data, or a purpose-trained model — when a general model used out of the box isn't accurate, fast, cheap, or private enough. My experience includes helping with model fine-tuning on Taxara. For a new project, the starting point is to review the task and examples before deciding whether model adaptation is justified.
Teams with domain-specific data or requirements — accuracy on specialist content, cost at volume, latency, or data that must stay on their own infrastructure.
- An assessment of whether you need a custom model at all, or better prompts and retrieval.
- Retrieval over your own content, so answers are grounded in your data.
- A fine-tuning plan where the task, examples and evaluation support it.
- An evaluation set and a way to compare options on accuracy, cost, and speed.
- A review of deployment requirements, data handling and maintenance responsibilities.
- 1
Define the task and collect real examples with known good answers.
- 2
Establish a baseline with a general model and good prompting.
- 3
Add retrieval or fine-tuning only where the baseline falls short, and measure the gain.
- 4
Deploy with logging and cost tracking, and re-evaluate when models change.
Taxara
Lead contractor · Technical consultant
Lead-contractor delivery through Afiniti Global, from development and pilot testing to launch and updates. Omer now advises Taxara as a technical consultant.
- AI-powered apps
- Custom AI models
All Day Books
Afiniti Global · XO Ai product
A conversational bookkeeping app listed on iOS by Afiniti Global Limited and on Android by XO Ai.
Custom AI models: common questions
- Do we need a custom model, or will a general one do?
- Often a general model with well-written prompts and retrieval over your own data is enough, and it is much cheaper to maintain. A custom or fine-tuned model is worth it when you can measure a gap in accuracy, cost, speed, or privacy that the simpler approach can't close.
- Can the model run on our own infrastructure?
- That needs a project-specific assessment of model licensing, hardware, data handling and maintenance. I would review those constraints before proposing a deployment approach.
- How much data do we need?
- It depends on the approach. Retrieval works with whatever content you have. Fine-tuning needs a set of good examples of the task, and the quality of those examples matters more than the quantity.
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