Fine-Tune Models for Clients. Charge Monthly.
Build proprietary AI models your clients rely on — without needing an ML background. Every model you fine-tune creates an asset that earns you recurring revenue as long as the client runs it.
Early-bird price locked for life · Cancel any time
Why Most AI Consulting Practices Stay Small
Project-based work has a ceiling. Fine-tuning changes the business model.
Projects end; costs don't
You deliver a chatbot, client pays once, engagement ends. Meanwhile, OpenAI keeps billing them. There's no recurring revenue for you and no technical moat in the work.
"Can't you just use ChatGPT?" is getting more common
Non-technical clients see AI everywhere. Without proprietary fine-tuned models, your AU$5,000 project feels indistinguishable from a weekend DIY attempt.
Fine-tuning requires ML expertise you don't have time for
Unsloth and Axolotl are powerful but require Python environments, YAML configs, and hours of debugging. You are a consultant, not a researcher.
Your Entire Fine-Tuning Practice, One Platform
From first dataset upload to deployed GGUF in a client's environment — no ML expertise required.
Turn project fees into monthly retainers
Fine-tune once, maintain monthly. Every model you deploy needs monitoring and retraining as the client's data evolves — that's recurring revenue from AU$300-1,200/month per client.
Proprietary assets clients can't easily replace
A fine-tuned model trained on a client's historical data is a capital asset. They can't replicate it without the same data, the same time, and your expertise.
No-code fine-tuning in ~2 minutes
Upload JSONL or CSV training data, pick a base model, click fine-tune. No Python, no CLI, no CUDA configuration. Your first model trains in an afternoon.
GGUF export for private client deployment
Export your fine-tuned model to GGUF — the open format compatible with Ollama, LM Studio, and llama.cpp. Deploy on client infrastructure with no ongoing API dependency.
Side-by-side evaluation canvas
Compare multiple fine-tuned runs on the same test set. Show clients concrete accuracy numbers, not just qualitative impressions.
Iterative training from previous runs
Use a previous model as the starting point for retraining. Each improvement cycle builds on the last — no starting from scratch when the client adds new data.
Ertas vs DIY Fine-Tuning Tools
| Feature | Ertas | DIY (Axolotl/Unsloth) |
|---|---|---|
| Setup time (first fine-tune) | ~2 minutes | 30–60+ minutes (Axolotl/Unsloth) |
| Code required | No | Python + YAML + CLI |
| GGUF export (for Ollama) | One click | Manual conversion scripts |
| Experiment tracking | Automatic | Manual / wandb setup |
| Deployment pipeline | Built-in | Not included — you build it |
| Recurring revenue model | Natural (maintenance retainer) | Project-only |
| Non-technical client handoff | Simple API endpoint | Complex self-managed infra |
"Fine-tuned a model on our product docs in under an hour. Now our support bot actually understands our domain instead of hallucinating."
Jamie K.
Indie Developer
"Replaced our AU$400/mo API bill with a fine-tuned model running locally. Better results, predictable costs. Exactly what we needed."
Maria R.
Startup Founder
Start building the recurring-revenue consulting practice
Early-bird access at AU$14.50/month — locked in for life. Launch price will be AU$34.50/month.
Or join the free waitlist