Answer from your own documents
Embed, retrieve, rerank and answer on your own models. Fine-tune the retriever with a retrieval file of query and passage pairs, and the pipeline improves from your own corpus.
Four embedding models, two rerankers.
Six models from 300M to 8B parameters, all trainable with LoRA. Pick a bi-encoder for embedding and a cross-encoder for reranking.
Fine-tune the retriever.
A retrieval file of query and passage pairs trains the embedding or rerank model on your domain. A few thousand labelled pairs move recall substantially.
Metered on tokens read.
Embedding and reranking calls are priced per million tokens read, from $0.02. The models return a vector or a score, so there is no output token charge.
How retrieval-augmented generation works
Embed the corpus once
Turn every document into a vector with an embedding model. Pass an array to embed a batch in one call, up to 256 inputs. An input longer than the model's context length is rejected rather than silently truncated, so a document that will not fit is a 400 you can act on.
Results from LLMs powered by Nucleus
from site reading to client report
systems on one thread
vendors under review for Q4 renewal
Retrieval in the field
Cable survey readings processed while the crew was still on site
Six vendor contracts ranked before the renewal meeting
Every incident past its four-hour SLA, escalated by name
What the pipeline includes
Embedding
- 4 bi-encoder models, 300M to 8B parameters
- Up to 4096 dimensions, Matryoshka truncation supported
- Batch up to 256 inputs per call
- Context from 2K to 32K tokens
- float or base64 encoding
Reranking
- 2 cross-encoder models, 568M and 8B parameters
- Up to 256 documents per call
- top_n filtering in the response
- 100+ languages (BGE Reranker v2 M3)
- Scores comparable within one call
Fine-tuning
- Retrieval files of query and passage pairs
- All 6 models trainable with LoRA rank up to 64
- Training from $0.03/Mtok (EmbeddingGemma 300M)
- Checkpoints exportable as adapters or merged weights
Embedding tokens
Embedding tokens
EmbeddingGemma 300M inference. All models priced per million tokens read.
Pick your path
Harness
Permissions, scoped keys and audit
On-Premise Deployment
Our GPU servers, your site
Tool use
Your private APIs as skills
MCP
Your model in your tools
Webhooks
Signed events to your systems