When an LLM API is too slow, too costly at volume or not allowed to see your data, we train a model you own — and keep it accurate.
Volume, metric, data — train, call an API, or hybrid?
LLM-assisted labelling of 5–20k examples, sampled for quality.
Model, eval against a held-out set, compare with API baseline.
Serve, monitor, retrain monthly.
| Tier | Scope | Timeline | Price |
|---|---|---|---|
| Classifier / extractor | One task, your labels | 3–5 weeks | $8k – $20k |
| Forecast / ranking | Time series or recommendations | 5–8 weeks | $20k – $45k |
| Vision / multi-model | Images, pipelines, MLOps | 8–14 weeks | $45k – $120k |
Every project starts with a written scope and a fixed price, delivered within 48 hours of your brief.
Two to five thousand clean labelled examples for most classifiers.
Below roughly 100k predictions a month, or when the task changes weekly.
We set up the pipeline and can run it on retainer, or hand it to your team.
When a custom-trained ML model beats calling an LLM API — cost per prediction, latency, data privacy and accuracy compared, with a decision table.
Our step-by-step method for cutting LLM API spend on a production agent — measure, cache, trim inputs, tune effort, route models, batch — with typical savings.
Generative AI produces content; agentic AI gets things done. What separates them, why the distinction changes what you should build, and what each costs.
We reply within 24 hours at hello@truecodeai.com with how we would build it.