Your data, routed to the right method.
Upload documents or datasets. Nevastack parses, cleans, chunks, deduplicates and scores your data — then recommends RAG, fine-tuning, LoRA, hybrid, or full training with a cost estimate.
We don't sell you the most expensive method
The engine reads real signals from your data and proposes the architecture that actually fits — a built-in qualification filter that prevents mispriced jobs.
Data volume
A handful of docs favors RAG; large proprietary corpora justify training.
Change rate
Frequently-updated knowledge stays fresh with retrieval, not weights.
Behavior vs. knowledge
Tone and format point to fine-tuning; facts point to RAG.
Budget & latency
LoRA bridges cost and control between self-serve and enterprise.
One flow, the right output
RAG Pipeline
RAG- Best for
- Company docs, knowledge bases, support content, manuals, changing data.
- You receive
- Embeddings, vector database, retrieval pipeline, prompt template, hosted endpoint.
Best MVP product — fastest to sell and update.
Fine-Tuning
FINETUNE- Best for
- Tone, response format, domain behavior, classification, repetitive task style.
- You receive
- Fine-tuned model, training report, evaluation metrics, deployable endpoint.
Higher-ticket setup, more support burden.
LoRA / Adapter
LORA- Best for
- Cost-effective open-source model customization.
- You receive
- Adapter weights, merged model option, deployment package.
Good bridge between self-serve and enterprise.
Full Training
FULL- Best for
- Large proprietary datasets and serious enterprise / research clients.
- You receive
- Custom model weights, training logs, evaluation report, deployment support.
Rare, expensive, custom quote only.
Report, artifacts, playground, endpoint
Every job returns a quality report and evaluation metrics. Approve, then deploy to an OpenAI-compatible endpoint in one click.
Ask for RAG when you need RAG. We'll tell you.
The recommendation flow educates during onboarding so compute isn't wasted and outcomes stay predictable.