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Power your Data for

LLMs

Traditional ML

Labeled Data

LLMs

Platform for complex data annotations, model creation & customizations, fine-tuning and AI lifecycle management.

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Presenting DataNeuron

DataNeuron is revolutionizing the conventional Supervised NLP Workflows through an automation based SaaS product.

Automate Text Data Labeling, Advanced Model Training, LLM Fine-Tuning, and NLP Model Lifecycle Management using a powerful data-centric platform. With DataNeuron decrease time, increase accuracy while also saving money.

Beyond this, DataNeuron reduces the complexities for Data Scientists by integrating Data & NLP components into a single project.

Features that set us apart

LLM Fine-Tuning Workflow

Divisive Sampling and Ensemble Active Learning (DSEAL)

DataNeuron vs Pre-Trained LLM for Data Labeling

DataNeuron vs Human-In-The-Loop

HITL ROI Calculator

Why DataNeuron
Streamline Data and Model Lifecycle with Automation
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Annotation as accurate as pre-trained LLMs or HITL
DataNeuron augments human expertise through various approaches like recognize vs recall with the proprietary ensemble models (unsupervised, semi-supervised and supervised) to achieve comparable/ better accuracies to HITL and pre-trained LLMs.

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DataNeuron DSEAL: Strategic Annotation
DSEAL is a proprietary algorithm developed by DataNeuron scientists to achieve state-of-the-art accuracy with the smallest data sample size possible while utilizing the full potential of active-learning methods. DSEAL integrates seamlessly with Traditional ML models and LLMs.

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Automated Model Training / Fine-Tuning & Deployment
Once the Data Labeling is complete using DataNeuron automate the model training/ fine-tuning pipeline with a no-code workflow. Platform also provides ready-to-use API for prediction and integration with AI products. It further captures the feedback for model improvements / versioning.

Our Resources
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How does DataNeuron compare to the rest?

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DataNeuron’s Active Learning to NLP data labeling outperforms the competitors’ Sequential Learning approach by 20% with 50% less data

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Comparison of NLP Data Sampling Strategies

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