Specialized Small Models for Specialized Core Tasks
Specialized small models, one per task, for each of the moving parts inside your production AI stack — query rewriting tuned to your enterprise API's exact shape, context compression for long-running agentic coding harnesses, semantic grep that finds the function whose name you forgot. Cheaper, better, and much faster than steering an LLM into the same job.
Custom models inherit the same compliant platform — see our Trust Center.
Generalist LLMs are the wrong tool for narrow repeated tasks.
An LLM has to relearn the task from instructions on every call. It's huge, slow, and expensive at production traffic. A specialized model — small, with the behaviour baked into the weights — runs circles around it on the narrow thing it was trained to do, often by two or even three orders of magnitude on cost and latency.
Where most of our specialized models land. Cheap to host, fast to call, easy to deploy in a VPC.
Typical cost gap between a custom-trained model and a frontier LLM doing the same narrow task.
White-glove — hand us an eval set and task definition. We make the data and ship the model.
“We had an enterprise customer whose agent was hitting an awkward search API and losing recall to mismatched query syntax they simply couldn't prompt out of their LLM. We trained a small rewrite model on (intent, observed-API-recall) pairs; recall on the agent's downstream task went up substantially and latency halved.”
zELO — preferences, not labels
The same statistical idea behind zerank-2 generalizes. For any task where ground truth is 'which of these candidates is better for the goal,' we collect pairwise preferences from frontier-LLM ensembles, recover continuous scores via Thurstone fit, and fine-tune a small specialized model against those scores. Transfers cleanly to query rewriting, context compression, and domain-specific retrieval.
Pairwise preferences > absolute labels
Asking 'which of these two is better for the task?' is far more stable than asking 'how good is this on a 0–1 scale?'. Lower noise, higher signal, scales to LLM ensembles.
Thurstone-fit continuous targets
Many pairwise outcomes feed a Thurstone fit (the same math behind chess Elo) that recovers a continuous score per item — the supervision signal we train against.
Distill into a small specialized model
Fine-tune a 1–7B model with MSE against the recovered scores. Open weights available; managed inference for low-latency deployments.
Per-rater calibration (zerank-2 evolution)
Each LLM rater gets a (μ, κ) Beta calibration that we iteratively mix, weighting each rater's judgment by how reliable it has been. Same trick generalizes to custom-task training.
Integrate ZeroEntropy models in minutes. Production-ready, latency-optimized, available everywhere.
Access all models through a single, latency-optimized API, or through our partner providers.
# Create an API Key at https://dashboard.zeroentropy.dev
from zeroentropy import ZeroEntropy
zclient = ZeroEntropy()
response = zclient.models.rerank(
model="zerank-2",
query="What is Retrieval Augmented Generation?",
documents=[
"RAG combines retrieval with generation...",
],
)
for doc in response.results:
print(doc)
Deploy in your own cloud with dedicated infrastructure. Available on AWS Marketplace and Azure.
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