Generate
Data agents produce text, images, audio, and multimodal samples on demand.
AGENTIC DATA PIPELINE
Build, label, and verify your AI training data
The agentic data platform that generates, labels, cleans, and verifies AI training data and LLM evaluation sets, built to your schema and your quality bar.
TALK TO SALES →1M+ SAMPLES EVERY DAY · TRULY UP TO 60% DATA EXTENTS
Fleet of Agents, One
Automated Data Pipeline
Four steps. One versioned output. Every row scored.
Orchestrate
Generator, critic, and verifier agents check each other. No sample passes on one agent's judgment.
Gate
Dedup, PII scrubbing, labeling, and QA, with confidence scores on every row.
Ship
Versioned, snapshot-able datasets. S3, GCS, or JSONL, exported to your bucket.
AI Training Data, Built and
Gated Before It Ships
The full range of AI training data a fine tuning run needs, gated before it ships.
Automated data labeling
Every row scored. You set the accept threshold.
AI data annotation
Text, image, audio, and multimodal in one schema.
Synthetic data generation
Edge cases, rare classes, and long tails on demand.
Fine tuning dataset preparation
Logs, docs, and transcripts into versioned JSONL.
Preference data for RLHF
Response pairs scored for RLHF, DPO, or reward models.
AI data cleaning
Dedup, PII scrubbing, leakage checks, schema repair.
Labeling cost scales with headcount. Agent labeling scales with compute.
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Your Criteria. Your Data.
Real Scores.
Build your evaluation data on the platform, then run LLM evaluation and benchmarking against it.
Custom eval dataset
Test cases from your criteria and failure history.
LLM evaluation
One set, every model. Correctness and judge scores.
LLM benchmarking on your own data
Quality, cost, and latency on your workload.
AI agent evaluation
Scored on the whole run, not just the final answer.
Model regression testing
Re-run on every version or prompt change, then diff.
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Where it runs
Our data centers. Your instances. Inferel owns and operates the hardware end to end, so deployment and data handling are one conversation.
Private data labeling in
your VPC
Source data and outputs stay in your boundary.
On premise data
annotation alternative
Single-tenant capacity, without the capex.
Retention, residency,
and ownership
Nothing stored, logged, or trained on. You own it.
Model choice and
export portability
Our models or your own fine-tunes. Standard exports.
FAQ
Start with a pilot
Bring a sample, a schema, or the criteria you want a model judged against.
START YOUR PILOT →