DataNeuron helps you accelerate and automate human-in-loop annotation for developing AI solutions. Powered by a data-centric platform, we automate data labeling, the creation of models, and end-to-end lifecycle management of ML.
Ingest
Upload Visualization
Users can upload the entire data available with them without performing any filteration to remove out of scope paragraphs.
The data can be uploaded in various file formats supported by the platform.
The platform has an in-built feature that can handle out-of-scope paragraphs and separate them from the classification data. This functionality is optional and can be toggled on or off anytime during the process.
Structure
Structure Visualization
The next step is the creation of the project structure.
Instead of a simple flat structure with just the classes defined, we provide the user with the option to create a multi-level (hierarchical) structure so that he can extract the data grouped into domains, subdomains, and indefinitely continue dividing into further subparts depending on his needs.
Any of the defined nodes can be marked as a class for the data to be classified into irrespective of the level on which it is in the hierarchy. This provides flexibility to create any level of ontology for classification.
Validate
Validate Visualization
User does not have to go through the entire dataset to sort out paragraphs that belong to a certain class and label them to provide training data for the model, which can be a tedious and difficult task.
We propose a validation based approach:
The platform provides the users with suggestions of paragraphs that are most likely to belong to a certain category/class based on an efficient context-based filtering criterion.
The user simply has to validate the suggestions, i.e, check whether or not the suggested class is correct.
This reduces the effort put in by the user in filtering out the paragraphs belonging to a category from the entire dataset by a large margin.
The strategic annotation technique allows the user to adopt a ‘one-vs-all’ strategy, which makes the task far easier than having to take into consideration all the defined classes, which can be a large number depending on the problem at hand, while tagging a paragraph.
Our intelligent filtering algorithm ensures “edge-case” paragraphs, i.e paragraphs that do not have obvious correlation with a class but still belong to that class, are not left out.
This step is broken down into 2 stages:
The validation done by the user in the first stage is used for determining the annotation suggestions offered in the second stage.
Each batch of annotation is used to improve the accuracy of the filtering algorithm for the next batch.
The platform also provide a summary screen after each batch of validation which provides the user with an idea as to many more paragraphs he might need to validate per class in order to achieve a higher accuracy.
It also helps determine when to stop the validation for a specific class and focus more on a class for which the platform projects low confidence.
Train
Train Visualization
User invests virtually no effort into the model training step and the model training can be initiated with the simple click of a button.
The complete training process is automatic which performs preprocessing, feature engineering, model selection, model training, optimization and k-fold validation.
After the final model is trained, the platform shows a detailed report of the trained model is presented to the user which includes the overall accuracy of the model as well as the accuracy achieved per class.
Iterate
Iterate Visualisation
Once the model has been trained, we provide the user with 2 options:
Continue to the deployment stage if the trained model matches their expectations.
If the model does not achieve the desired results, the user can choose to go back and provide more training paragraphs (by validating more paragraphs or uploading seed paragraphs) or alter the project structure to remove some classes and then retrain the model to achieve better results.
Deploy (“No-Code” Prediction Service)
Deploy Visualisation
Apart from providing the final annotations on the data uploaded by the user using the trained model, we also provide a prediction service which can be used to make a prediction on any new paragraphs in exchange for a very minimal fee.
This does not require any knowledge of coding and users can utilize this service for any input data from the platform.
This can also be integrated into other platforms by making use of the exposed prediction API or the deployed Python package.
No Requirement for a Data Science/Machine Learning Expert
The DataNeuron ALP is designed in such a way that no prerequisite knowledge of data science or machine learning is required to utilize the platform to its maximum potential.
For some very specific use cases, a Subject Matter Expert might be required but for the majority of use cases, an SME is not required in the DataNeuron Pipeline.
This is the table that explains the dataset that was used to conduct this case study.
Explaining the DataNeuron Pipeline
This is the DataNeuron Pipeline. Ingest, Structure, Validate, Train, Predict, Deploy and Iterate.
Results of our Experiment
Results of our Experiment
Reduction in SME Labelling Effort
During an in-house project, the SMEs have to go through all the paragraphs present in the dataset in order to figure out which paragraphs actually belong to the 73 classes mentioned above. This would usually take a tremendous amount of time and effort.
When using DataNeuron ALP, the algorithm was able to perform strategic annotation on 15000 raw paragraphs and filter out the paragraphs that belonged to the 73 classes and provide 4303 paragraphs to the user for validation. Taking as little as 45 seconds to annotate each paragraph, an in-house project would take an estimate of 187.5 hours just to annotate all the paragraphs while by using DataNeuron, it only took 35.85 hours.
Difference in paragraphs annotated between an in-house solution and DataNeuron.
Advantage of Suggestion-Based Annotation
Instead of making users go through the entire dataset to label paragraphs that belong to a certain class, DataNeuron uses a validation-based approach to make the model training process considerably easier. The platform provides the users with a list of annotated/ labelled paragraphs that are most likely to belong to the same class by using context-based filtering and analysing the masterlist. The users simply have to validate whether the system labelled paragraph belongs to the class mentioned. This validation-based approach also reduces the time it takes to annotate each paragraph. Based on our estimate, it takes approximately 30 seconds for a user to identify whether a paragraph belongs to a particular class. Based on this, it would take an estimate of 35.85 hours for the users to validate 4303 paragraphs provided by the DataNeuron ALP. When compared to the 187.5 hours it would take for an in-house team to complete the annotation process, DataNeuron offers a staggering 81% reduction in time spent.
Difference in time spent annotating between an in-house solution and DataNeuron.
The Accuracy Achieved
When conducting this case study, the accuracy we achieved for the model trained by the DataNeuron ALP was 87% which, considering the high number of classes and small number of training paragraphs, proves to work very well in real world scenarios. The accuracy of the model trained by the DataNeuron ALP can be improved by validating more paragraphs or by adding seed paragraphs.
Calculating the Cost ROI
The number of paragraphs that needs to be annotated in an in-house project is 15000 and it would cost approximately $3280. The number of paragraphs that needs to be annotated when using the DataNeuron ALP is 4303 since most of the paragraphs which did not belong to any of 73 classes were discarded using context-based filtering. The cost for annotating 4303 paragraphs using the DataNeuron ALP is $976.85.
Difference in cost between an in-house solution and DataNeuron.
No Requirement for a Data Science/Machine Learning Expert
The DataNeuron ALP is designed in such a way that no prerequisite knowledge of data science or machine learning is required to utilize the platform to its maximum potential.
For some very specific use cases, a Subject Matter Expert might be required but for the majority of use cases, an SME is not required in the DataNeuron Pipeline.
DataNeuron is thrilled to announce the official launch of the DataNeuron Automated Learning Platform (ALP). The ALP has been strategically designed to accelerate and automate human-in-loop annotation for developing AI solutions. Powered by a data-centric platform, we automate data labeling, the creation of models, and end-to-end lifecycle management of ML.
We are a team of Data Science enthusiasts having first-hand experience of dealing with data analysts, subject matter experts and data scientists to fulfil the labelled data requirements for building highly accurate contextual algorithms for various use-cases. Our aim is to accelerate the development and provide better explainability of AI.
We are also excited to partner with leading venture capitalists, angel investors and strategic advisors in expanding the horizons of DataNeuron.
But why should we switch from human labelling to the DataNeuron ALP? That’s a great question! Based on our findings from the case studies we have conducted, we have found out that using the DataNeuron ALP can reduce the time spent in annotating by a staggering 89.10%, reduce the number of paragraphs validated by 83.55%, reduce the cost expenditure by 77.83% and yield an ROI of an astounding 372.22%.
The DataNeuron Pipeline
Those numbers sound promising but what more can we do on the DataNeuron ALP? Once Again, that’s a great question! Apart from getting accurately labelled data, the DataNeuron ALP can be used to perform no-code prediction. With just a click of a button, the platform can be used to make a prediction on any new paragraphs in exchange for a very minimal fee. This does not require any knowledge of programming and users can utilize this service for any input data from the platform. This can also be integrated into other platforms by making use of the exposed prediction API or the deployed Python package.
As a cherry on top, the DataNeuron ALP is designed in such a way that no prerequisite knowledge of data science or machine learning is required to utilize the platform to its maximum potential. The users only need some knowledge of the domain they are working on and the details of the project and they’re good to go! For some very specific use cases, a Subject Matter Expert might be required but for the majority of use cases, an SME is not required in the DataNeuron Pipeline.
Large Language Models (LLMs) like Llama and Mistral offer immense potential, but their massive size creates deployment challenges, as slowspeeds and heftyoperationalcosts hinder their real-world applications. When building a real-time application for your enterprise or aiming for budget deployment at scale, running a 13 B+ parameter model is impractical.
This is where model distillation comes into play.
Think of it as extracting the core wisdom of a highly knowledgeable “teacher” model and transferring it to a smaller, more agile “student” model. At DataNeuron, we’re revolutionizing this process with our LLM Studio. Our platform boasts a smooth workflow that integrates intelligent data curation with a powerful distillation engine that delivers:
Up to 10X faster inference speed*
90% reduction in model size*
Significant cost
Saving on GPU infrastructure
High accuracy retention
Why is Distillation a Game Changer?
Deploying billion-parameter LLMs to production introduces four major bottlenecks:
Latency: A few seconds of latency to produce responses from big models is not suitable for real-time use in conversational AI, customer, and real-time interactions
Infrastructure Cost: LLMs are GPU-intensive. Executing one inference on a +13B model doesn’t sound like much until you are dealing with thousands of simultaneous users. Your cloud expenses surge quickly. A 13B parameter model might end up costing 5X more to execute than a distilled 2B version.
Infrastructure Demand: Scaling mass models necessitates more powerful GPUs, scaled serving infrastructure, and continuous performance tuning. Deployment on devices becomes infeasible when model sizes exceed 5B parameters.
Hallucinations: Larger models are more likely to produce inapt or irrelevant answers without proper tuning.
Model distillation removes these limitations by transferring the “knowledge” from a large (teacher) model (e.g., Llama 13B) to a smaller (student) model (e.g., a Llama 1B), retaining performance but vastly improving efficiency.
Navigating the Pitfalls of Traditional Distillation
Traditional model distillation trains a smaller “student” model to mimic a larger “teacher” by matching their outputs. While conceptually simple, valuable distillation is complex, involving careful data selection, proper loss functions (typically based on the teacher’s probability distributions for richer information transfer), and iterative testing with hyperparameters. For example, distilling a large language model for mobile deployment involves training a smaller model on relevant text, possibly incorporating the teacher’s predicted word probabilities to capture style variations.
Without the right tools and technology to manage this complexity, the process can be time-consuming, error-prone, and difficult to scale, limiting the practical implementation of this efficiency-boosting technique.
How is DataNeuron Doing Things Differently?
LLM Studio allows you to easily design and manage lightweight, powerful models as per your needs. Our approach promotes intelligent data curation as the foundation for successful information transfer.
Here’s how we streamline the process:
1. Data Selection with Divisive Sampling (D-SEAL)
We deploy our proprietary Divisive Sampling (D-SEAL) system to choose the most informative training data. D-SEAL groups comparable data points, ensuring that your student model learns from a diverse range of examples relevant to its target domain. This curated dataset, potentially built using prompts and responses generated by Retrieval-Augmented Generation (RAG), serves as the bedrock for effective distillation.
Our platform features a user-friendly interface for knowledge distillation. You can easily select the Teacher Model available on the DataNeuron platform, such as a suitable high-performing model like Llama 2 70 B.
For the Student Model, you have flexible parameter options to tailor the distilled output to your deployment requirements. Choose from the DataNeuron provided options such as Llama 2 1B, Llama 2 3B, or Llama 2 13B parameters, balancing model size, computational cost, and performance. These options allow you to optimize for various deployment environments.
3. Distillation Engine
The heart of LLM Studio is our powerful distillation engine, which transfers knowledge from the selected teacher model to the smaller student model. The platform handles the underlying complications, allowing you to focus on your desired outcome.
4. Inference & Deployment
Once the distillation process is complete, LLM Studio allows for rapid lean model testing, evaluation, and deployment. You can easily export them for on-device use, integrate them using an API, or deploy them within your cloud infrastructure.
DataNeuron: Beyond Just Smaller Model
At DataNeuron, distillation does more than just shrinking the model size; we create smarter, cost-efficient, and universally deployable AI solutions.
Real-World Impact: Distillation In Action
Internal Search & RAG on a Budget
Such distilled models can still be used to power an internal search capable of domain-specific answering, effectively implemented on a modest cloud setting.
Why Distillation Is The Future of Scalable AI
As foundation models grow in size, competence, and cost, businesses must address the main challenge of scaling their AI applications economically. Model distillation provides an attractive and accessible way ahead.
With DataNeuron LLM Studio, that path is no longer just for field experts and infrastructure engineers. Whether you’re working on mobile apps, internal tools, or public NLP-facing products, training, distilling, and deploying LLMs is simple when you’re associated with us. Smarter models. Smaller footprints. All made easy by DataNeuron.
A leading D2C business in India and international markets, renowned for its home and sleep products, aimed to enhance customer support. As a major retailer of furniture, mattresses, and home furnishings, they faced a major challenge: inefficiency inhandlingahighvolumeofdiversecustomerinquiriesaboutproductdetails,orderstatus,andpolicies,resultingin slow response times and customer frustration. The company required a solution capable of understanding and responding to definitive customer queries, an area where existing chatbot solutions had fallen short.
The DataNeuron Solution: Smart Query Handling with LLM Studio
To solve this, the team implemented a smart, hybrid retrieval solution using DataNeuron’s LLM Studio, built to understand and respond to diverse customer queries, regardless of how or where the data was stored.
Step 1: Intelligent Classification with the LLM Router
The first stage was a classifier-based router that automatically determined whether a query required structured or unstructured information. For example:
Structured: “What is the price of a king-size bed?”
Unstructured: “What is the return policy if the product is damaged?”
The router leveraged a wide set of example queries and domain-specific patterns to route incoming questions to the right processing pipeline.
Once classified, queries flowed into one of two specialized pipelines:
Structured Query Pipeline: Direct Retrieval from Product Databases
Structured queries were translated into SQL and executed directly on product databases to retrieve precise product details, pricing, availability, etc. This approach ensured fast, accurate answers to data-specific questions.
Unstructured queries were handled via semantic vector search powered by DataNeuron’s RAG framework. Here’s how:
The question was converted into a vector embedding.
This vector was matched with the most relevant documents in the company’s vector database (e.g., policy documents, manuals).
The matched content was passed to a custom LLM to generate grounded, context-aware responses.
Studio Benefits: Customization, Evaluation, and Fallbacks
The LLMs used in both pipelines were customized via LLM Studio, which offered:
Fallback mechanisms when classification confidence was low, such as routing queries to a human agent or invoking a hybrid LLM fallback.
Tagging and annotation tools to refine training data.
Built-in evaluation metrics to monitor performance.
DataNeuron’s LLM Router, transformed our support: SQL‑powered answers for product specs and semantic search for policies now resolve 70% of tickets instantly, cutting escalations and driving our CSAT, all deployed in under two weeks.
– Customer Testimony
The DataNeuron Edge
DataNeuron LLM Studio automates model tuning with:
Built-in tools specifically for labeling and tagging datasets.
LLM evaluations to compare performance before and after tweaking.
Substantive changes introduced:
Specifically stated “service” and “cancellation” to address comments.
Highlighted the “Router capability dataset with lots of questions” to highlight the importance of data diversity for the classifier.
Detailed the process of the “Structure RAG” pipeline, including natural language to SQL and back to natural language.
We’ve all seen how Large Language Models (LLMs) have revolutionized tasks, from answering emails and generating code to summarizing documents and navigating chatbots. In just one year, market growth increased from $3.92 billion to $5.03 billion in 2025, driven by the transformation of customer insights, predictive analytics, and intelligent automation.
However, not every AI challenge can(or should) be solved with a single, monolithic model. Some problems demand a laser-focused expert LLM, customized to your precise requirements. Others call for a team of specialised models working together like humans do.
At DataNeuron, we recognize this distinction in your business needs and empower enterprises with both advanced fine-tuning options and flexible multi-agent systems. Let’s understand how DataNeuron’s unique offerings set a new standard.
What is a Fine-Tuned LLM, Exactly?
Consider adopting a general-purpose AI model and training it to master a specific activity, such as answering healthcare queries, insurance questions, or drafting legal documents. That is fine-tuning. Fine-tuning creates a single-action specialist, an LLM that consistently delivers highly accurate, domain-aligned responses.
Publicly available models (such as GPT-4, Claude, and Gemini) are versatile but general-purpose. They are not trained using your confidential data. Fine-tuning is how you close the gap and turn generalist LLMs into private-domain experts.
With fine-tuning, you use private, valuable data to customize an LLM to your unique domain needs.
Medical information (clinical notes, patient records, and diagnostic protocols is safely handled for HIPAA/GDPR compliance.
Financial compliance documents
Legal case libraries
Manufacturing SOPs
Fine-Tuning Options Offered by DataNeuron
Parameter-Efficient Fine-Tuning: PEFT is a more efficient fine-tuning method that only changes a portion of the model’s parameters. PEFT (Prefix-Tuning for Efficient Adaptation of Pre-trained BERT) is a widely used approach with promising outcomes.
Direct Preference Optimization: DPO aligns models to human-like preferences and ranking behaviors. Ideal for picking multiple types of responses.
DataNeuron supports both PEFT and DPO workflows, providing scalable, enterprise-grade model customisation. These solutions enable enterprises to quickly adapt to new use cases without requiring complete model retraining.
If your work does not change substantially and the responses follow a predictable pattern, fine-tuning is probably your best option.
What is a Multi-Agent System?
Instead of one expert, you have a group of agents performing tasks in segments. One person is in charge of planning, another collects data, and another double-checks the answer. They work together to complete a task. That’s a multi-agent system, multiple LLMs (or tools) with different responsibilities that work together to handle complicated operations.
A multi-agent system involves multiple large language models (LLMs) or tools, each with distinct responsibilities, collaborating to execute complex tasks.
At DataNeuron, our technology is designed to allow both hierarchical and decentralized agent coordination. This implies that teams may create workflows in which agents take turns or operate simultaneously, depending on the requirements.
Agent Roles: Planner, Retriever, Executor, and Verifier
In a multi-agent system, individual agents are entities designed to perform specific tasks as needed. While the exact configuration of agents can be built on demand and vary depending on the complexity of the operation, some common and frequently deployed roles include:
Planner: Acts like a project manager, responsible for defining tasks and breaking down complex objectives into manageable steps.
Retriever: Functions as a knowledge scout, tasked with gathering necessary data from various sources such as internal APIs, live web data, or a Retrieval-Augmented Generation (RAG) layer.
Executor: Operates as the hands-on worker, executing actions on the data based on the Planner’s instructions and the information provided by the Retriever. This could involve creating, transforming, or otherwise manipulating data.
Verifier: Plays the role of a quality assurance specialist, ensuring the accuracy and validity of the Executor’s output by identifying discrepancies, validating findings, and raising concerns if issues are detected.
These roles represent a functional division of labor that enables multi-agent systems to handle intricate tasks through coordinated effort. The flexibility of such systems allows for the instantiation of these or other specialized agents as the specific demands of a task dictate.
Key Features:
Agents may call each other, trigger APIs, or access knowledge bases.
They could be specialists (like a search agent) or generalists.
Inspired by how individuals delegated and collaborated in teams.
Choosing Between Fine-Tuned LLMs and Multi-Agent Systems: What Points to Consider
Data In-Hand
If you have access to clean, labeled, domain-specific data, a fine-tuned LLM can generate high precision. These models thrive on well-curated datasets and learn only what you teach them.
Multi-agent systems are better suited to data that is dispersed, constantly changing, or unstructured for typical fine-tuning. Agents such as retrievers may extract essential information from APIs, databases, or documents in real time, eliminating the need for dataset maintenance.
Task Complexity
Consider task complexity as the number of stages or moving pieces involved. Fine-tuned LLMs are best suited for targeted, repeated activities. You teach them once, and they continuously perform in that domain.
However, when a job requires numerous phases, such as planning, retrieving data, checking outcomes, and initiating actions, a multi-agent method is frequently more suited. Different agents specialize and work together to manage the workflow from start to finish.
Need for Coordination
Fine-tuned models may be quite effective for simple reasoning, especially when the prompts are well-designed. They can use what they learnt in training to infer, summarize, or produce.
However, multi-agent systems excel when the task necessitates more back-and-forth reasoning or layered decision-making. Before the final product goes into production, a planner agent breaks down the task, a retriever recovers information, and a validator verifies for accuracy.
Time to Deploy
Time is typically the biggest constraint. Fine-tuning needs some initial investment: preparing data, training the model, and validating results. It’s worth it if you know the assignment will not change frequently.
Multi-agent systems provide greater versatility. You can assemble agents from existing components to get something useful up and running quickly. Need to make a change? Simply exchange or modify an agent; no retraining is required.
Use Cases: Fine-Tune Vs. Multi-Agent
The best way to grasp a complicated decision is through a few tangible stories. So here are some real-world scenarios that make the difference between fine-tuned LLMs and multi-agent systems as clear as day.
Scenario 1: Customer Support Chatbot
Company: HealthTech Startup
Goal: Develop a chatbot that responds to patient queries regarding their platform.
Approach: Fine-Tuned LLM
They trained the model on:
Historical support tickets
Internal product documentation
HIPAA-compliant response templates
Why it works: The chatbot provides responses that read on-brand, maintain compliance rules, and do not hallucinate because the model was trained in the company’s precise tone and content.
Scenario 2: Market Research Automation
Business: Online Brand
Objective: Be ahead of the curve by automating product discovery.
Approach: Multi-Agent System
The framework includes:
Search Agent to crawl social media for topically relevant items
Sentiment and Pattern Recognition Analyzer Agent
Strategic Agent to advise on product launch angles
Why it works: The system constantly monitors the marketplace, learns to adjust to evolving trends, and gives actionable insights that are free from human micromanagement.
At DataNeuron, we built our platform to integrate fine-tuned intelligence with multi-agent collaboration. Here’s what it looks like: Various agents, both pre-built and customizable, can be used for NLP tasks like NER, document search, and RAG. Built-in agents offer convenience for common tasks, while customizable agents provide flexibility for complex scenarios by allowing fine-tuning with specific data and logic. The choice depends on task complexity, data availability, performance needs, and resources. Simple tasks may suit built-in agents, whereas nuanced tasks in specialized domains often benefit from customizable agents. Advanced RAG applications frequently necessitate customizable agents for effective information retrieval and integration from diverse sources.
So, whether your activity is mundane or dynamically developing, you get the ideal blend of speed, scalability, and intelligence. You don’t have to pick sides. Instead, choose what best suits your business today. We are driving this hybrid future by making it simple to design AI that fits your workflow, not the other way around.
The adoption of Large Language Models (LLMs) has transformed how industries function, unlocking capabilities from customer support automation to improving human-computer interactions. Their adoption is soaring, with MarketsandMarkets projecting the global LLM market to grow at a compound annual growth rate (CAGR) of over 35% in the next five years. Yet, many businesses that rush to adopt these models are discovering a critical insight: the model itself isn’t what sets you apart your data does.
While impressive, pre-trained LLMs are fundamentally generalists. They are trained on a broad, diverse pool of public data, making them strong in language understanding but weak in context relevance. A well-curated dataset ensures that an LLM understands industry jargon, complies with regulatory constraints, and aligns with the client’s vision.
At DataNeuron, we’ve built our approach around this idea. Our Divisive Sampling for Efficient Active Learning (DSEAL) framework redefines what it means to prepare data for fine-tuning. Rather than throwing thousands of generic examples at a model, DSEAL enables the creation of focused, instructive, and diverse datasets while maintaining speed and confidentiality with minimal manual intervention.
Why Data Curation is the Hidden Engine Behind Fine-Tuning
You wouldn’t train a legal assistant with engineering textbooks. Yet many enterprises expect LLMs trained on internet data to perform highly specialized tasks with minimal adaptation. This mismatch leads to a familiar set of issues: hallucination, shallow reasoning, and a lack of domain fluency.
The data that the model has or hasn’t seen contributes to these challenges. Fine-tuning a model with domain-specific examples allows it to grasp the nuances of your vocabulary, user expectations, and compliance norms. Nonetheless, fine-tuning is sometimes misinterpreted as a process concentrated on coding. In practice, 80% of successful LLM fine-tuning depends on one factor: the correct data. We provide two fine-tuning options: PEFT and DPO, both of which are fully dependent on the quality of the incoming dataset.
Without sufficient curation, fine-tuning can provide biased, noisy, or irrelevant results. For instance, a financial LLM trained on poorly labeled transaction data may misidentify fraud tendencies. A healthcare model analyzing unstructured clinical notes may make harmful recommendations.
LLM Customization Starts with Curation, Not Code
Enterprises often approach LLM customization like a software engineering project: code first, optimize later. But with LLMs, data>code is where the transformation begins. Fine-tuning doesn’t start with scripts or API’s, it begins with surfacing the right example from your data sources. Whether you employ open-source models or integrate with closed APIs, the uniqueness of the dataset makes our platform an ideal place to collaborate. Your support tickets, policy documents, email logs, and chat exchanges include an array of concealed data. However, they are distorted, inconsistent, and unstructured.
Curation turns this raw material into clarity. It is the process of identifying relevant instances, clearing up discrepancies, and aligning them with task requirements. At scale, it enables LLMs to progress from knowing a lot to understanding what matters.
This is why our clients don’t start their AI journey by deciding whether to use GPT or Llama; they begin by curating a dataset that reflects the tasks they care about. With the correct dataset, any model can be trained into a domain expert.
DataNeuron’s platform automates 95% of dataset creation, allowing businesses to prioritize strategic sampling and validation over human labeling. And the output? DataNeuron’s prediction API enables faster deployment, improved accuracy, and smoother integration.
Why Scaling Data Curation is Challenging Yet Important
For most companies, data curation is a bottleneck. It’s easy to underestimate how labor-intensive this procedure may be. Manually reviewing text samples, labeling for context, and ensuring consistency is an inefficient procedure that cannot be scaled.
We focus on quality over volume. Models trained using irrelevant or badly labeled samples frequently perform worse than models that were not fine-tuned at all. Add to this the complexities of data privacy, where sensitive internal documents cannot be shared with external tools, and most businesses find themselves trapped.
This is where we invented the DSEAL framework, which revolutionized the equation.
How DataNeuron’s DSEAL Framework Makes High-Quality Curation Possible
DSEAL is our solution to the most common problems in AI data preparation. DSEAL solves a basic issue in machine learning: the inefficiency and domain limitation of classic active learning methods. It’s a system designed to automate what’s slow, eliminate what’s unnecessary, and highlight the things that matter.
What makes DSEAL different from others?
95% Curation Automation: From ingestion to labeling, the system does the majority of the labor.
Task-aligned sampling: DSEAL strategically samples across edge cases, structures, and language trends rather than random examples.
Instruction-First Formatting: The curated data is organized to match instruction-tuned models, increasing relevance and accuracy.
Private by Design: All processes run inside the enterprise environment; no data leaves your perimeter.
The change from brute-force annotation to smart, minimum, domain-adaptive sampling distinguishes DSEAL in today’s noisy and model-saturated market.
Key Takeaways
From raw to model-ready in four steps:
Raw Data Ingestion: Whether it’s email threads or chat logs, the data enters the system untouched.
Cleaning and Structuring: We remove duplicates, normalize formats, and extract only the content that is relevant to your aims.
Instruction formatting: It involves converting data into prompt-response pairs or structuring it for preference-based training.
Model-Ready Dataset: The completed dataset is ready for fine-tuning procedures, complete with traceability and metrics.
Fine-tuning is no longer about model design but about context and detail. Your business already has everything it needs to create world-class AI: its data. The difficulty lies in converting the data into a structured, informative resource from which an LLM may learn.
With DSEAL, DataNeuron turns curation from a manual bottleneck to a strategic advantage. We help you go from data chaos to clarity, providing your models the depth and focus they require to operate in the real world.
The sample is a collection of people, things, or things used in the study that is taken for analysis from a wider population. To enable us to extrapolate the research sample’s findings to the entire population, the sample must be representative of the population.
Let’s go through a real-world scenario.
We’re looking for Mumbai’s adult population’s average annual salary. Up till 2022, Mumbai has a population of about 30 million. Males and females in this population would roughly be split 1:1 (these are simple generalizations), and they might have different averages. Similarly, there are numerous more ways in which various adult population groupings may have varying income levels. As you may guess, it is incredibly difficult to determine the average adult income in Mumbai.
Since it’s impossible to reach every adult in the whole population, what can be the solution? We can collect numerous samples and determine the average height of the people in the chosen samples.
How can we take a Sample?
Taking the same scenario from above, imagine we only take samples from the people in managerial positions. This won’t be regarded as a decent sample because, on generalizing, a manager earns more than the average adult, and it will provide us with a poor estimation of the income of the average adult. A sample must accurately reflect the universe from which it was drawn.
There are various different potential solutions, but we’ll be looking at three major techniques.
Sampling strategies :
Most uncertain probability
Most certain data points
The basic mixture from different confidence intervals
Most Uncertain Probability
The aim behind uncertainty sampling is to focus on the data item that the present predictor is least certain about. To put it another way, uncertainty sampling typically finds points that are located near thWhat is Sampling?
The sample is a collection of people, things, or things used in the study that is taken for analysis from a wider population. To enable us to extrapolate the research sample’s findings to the entire population, the sample must be representative of the population.
Let’s go through a real-world scenario.
We’re looking for Mumbai’s adult population’s average annual salary. Up till 2022, Mumbai has a population of about 30 million. Males and females in this population would roughly be split 1:1 (these are simple generalizations), and they might have different averages. Similarly, there are numerous more ways in which various adult population groupings may have varying income levels. As you may guess, it is incredibly difficult to determine the average adult income in Mumbai.
Since it’s impossible to reach every adult in the whole population, what can be the solution? We can collect numerous samples and determine the average height of the people in the chosen samples.
How can we take a Sample?
Taking the same scenario from above, imagine we only take samples from the people in managerial positions. This won’t be regarded as a decent sample because, on generalizing, a manager earns more than the average adult, and it will provide us with a poor estimation of the income of the average adult. A sample must accurately reflect the universe from which it was drawn.
There are various different potential solutions, but we’ll be looking at three major techniques.
Sampling strategies :
Most uncertain probability
Most certain data points
The basic mixture from different confidence intervals
Most Uncertain Probability
The aim behind uncertainty sampling is to focus on the data item that the present predictor is least certain about. To put it another way, uncertainty sampling typically finds points that are located near the decision boundary of the current model.
Uncertainty Sampling
Assume that a student is preparing for an exam and has 1000 questions to go through. The student only has time to go through 100 of them. Naturally, the student should prepare 100 questions on which the individual is least confident. With the new questions, students should get smarter, and faster.
Most Certain Data Points
This method chooses the data points with the highest certainty ie. data points that are predicted by the model with the highest confidence. These data points have maximum chances of getting correctly predicted by the model. Such data points may or may not add a lot of new information to the model learning.
Basic mixture of different Confidence Intervals
Data points are grouped according to their confidence scores, and sampling is done from all of these intervals or groups. This way, we can make sure that no kind of data is missed out upon. This ensures that the sampled data points are having a balance of certain and uncertain data points. This way the model can learn the decision boundary well without missing out on already learned information.
Code
Now, let’s use these sampling methods and see their application using a simple code in Python!
We’ll be working with a binary classification problem, using two datasets:
IMDB Movie Review Dataset for sentiment analysis. Two classes in this dataset: Positive, Negative
Emotion Dataset. Two classes in this dataset: Joy, Sadness
We have performed this experiment on Jupyter Notebook.
Loading Data & Preprocessing
The availability of data is always a determining factor in the field of machine learning, so loading data should be done first. After loading the dataset and the necessary modules, the dataframe should be looking like this.
Clean the data by replacing all occurrences of breaks with single white space.
for idx in range(len(df['review'])):
df['review'][idx] = df['review'][idx].replace('<br /><br />', ' ')
For ease of the experiment, we’re using 10k paragraphs out of the whole dataset of 50k paragraphs.
Since the train and test sets have been constructed, the pipeline can be instantiated. The pipeline consists of three steps: data transformation, resampling, and model creation at the end.
# The resulting matrices will have the shape of (`nr of examples`, `nr of word n-grams`)
vectorizer = CountVectorizer(ngram_range=(1, 5))
X_100_train = vectorizer.fit_transform(df_100_train.review)
X_stage1_test = vectorizer.transform(df_stage1_test.review)
X_test = vectorizer.transform(df_test.review)
labelencoder = LabelEncoder()
df_100_train['sentiment'] = labelencoder.fit_transform(df_100_train['sentiment'])
df_stage1_test['sentiment'] = labelencoder.transform(df_stage1_test['sentiment'])
df_test['sentiment'] = labelencoder.transform(df_test['sentiment'])
Before moving on to sampling strategies, an initial model is trained
1000 or more paragraphs are picked from a window of probability with the highest degree of uncertainty. These 1000 paragraphs are sorted increasingly in a dataframe. Then we compute the predicted probability’s mean value.
The index of the row with the predicted probability value closest to the mean value is calculated.
The paragraph sets are chosen using the mean value index row (Half of them from greater than part and half of them from less than part of the probability). To choose the most uncertain sets of paragraphs, use the same method as minimizing and maximizing the uncertain probability window range.
#index of the row closest to the mean value of predicted probability
mid_idx = int(len(df_uncertain_sorted)/2)
mean_idx = mid_idx-12
df_uncertain_sorted['predict_proba'].mean()
[Out]: 0.4936057560087512
num_of_para = [100,200,300,400,500,600,700,800,900,1000]
score_uncertain_list = []
for para in num_of_para:
para_idx = int(para/2)
#training set
df_uncertain_new = df_uncertain_sorted.iloc[mean_idx-para_idx:mean_idx+para_idx]
#preprocessing
X_train_uncertain = vectorizer.transform(df_uncertain_new.review)
#defining the classifier
logreg_uncertain = LogisticRegression()
#training the classifier
logreg_uncertain.fit(X=X_train_uncertain, y=df_uncertain_new['sentiment'].to_list())
#calculating the accuracy score on the test set
score_uncertain = logreg_uncertain.score(X_test, df_test['sentiment'].to_list())
score_uncertain_list.append(score_uncertain)
score_uncertain_list
[Out]: [0.547, 0.5845, 0.584, 0.612, 0.6215, 0.6335, 0.6415, 0.663, 0.659, 0.6755]
Most Certain Probability Sampling
The dataframe with 7900 paragraphs is sorted in descending order of their predicted probabilities. The top [100,200,300,400,500,600,700,800,900,1000] sets of paragraphs are selected as the most certain paragraphs.
num_of_para = [100,200,300,400,500,600,700,800,900,1000]
score_certain_list = []
for para in num_of_para:
#training set
df_certain = df_proba_sorted[:para]
#preprocessing
X_train_certain = vectorizer.transform(df_certain.review)
#defining the classifier
logreg_certain = LogisticRegression()
#training the classifier
logreg_certain.fit(X=X_train_certain, y=df_certain['sentiment'].to_list())
#calculating the accuracy score on the test set
score_certain = logreg_certain.score(X_test, df_test['sentiment'].to_list())
score_certain_list.append(score_certain)
score_certain_list
[Out]: [0.5215, 0.54, 0.536, 0.5755, 0.5905, 0.6245, 0.641, 0.6735, 0.7355, 0.7145]
Confidence Interval Grouping Sampling
In this method, the 25th and 75th percentile of the predicted probabilities are calculated. Then the 7900 paragraphs are separated into 3 groups.
From these 3 groups [100,200,300,400,500.600,700.800.900,1000] sets of paragraphs are sampled out according to these fractions:
#calculating the 25th and 75th percentile
proba_arr = df_proba['predict_proba']
percentile_75 = np.percentile(proba_arr, 75)
percentile_25 = np.percentile(proba_arr, 25)
print("25th percentile of arr : ",
np.percentile(proba_arr, 25))
[Out]: 25th percentile of arr : 0.28084100127515504
print("75th percentile of arr : ",
np.percentile(proba_arr, 75))
[Out]: 75th percentile of arr : 0.7063559972435552
#grouping of the paragraphs for following window
# group 1 : >= 75
df_group_1 = df_proba[df_proba['predict_proba'] >= percentile_75]
# group 2 : <75 and >= 25
df_group_2 = df_proba[(df_proba['predict_proba'] >= percentile_25) & (df_proba['predict_proba'] < percentile_75)]
# group 3 : < 25
df_group_3 = df_proba[(df_proba['predict_proba'] < percentile_25)]
df_group_1.shape, df_group_2.shape, df_group_3.shape
[Out]: ((1975, 3), (3950, 3), (1975, 3))
Four different models are then trained on each set of paragraphs for each of the 3 sampling techniques. [total 10 x 3 = 30 models]. The accuracy score is calculated for each of the cases by fitting the models on the 2000-paragraph test set.
num_of_para = [100,200,300,400,500,600,700,800,900,1000]
score_conf_list = []
#fractions
frac1 = 0.4
frac2 = 0.3
frac3 = 0.3
#sampling paragraphs from the 3 groups
df_group_1_frac = df_group_1.sample(frac=frac1, random_state=1).reset_index(drop = True)
df_group_2_frac = df_group_2.sample(frac=frac2, random_state=1).reset_index(drop = True)
df_group_3_frac = df_group_3.sample(frac=frac3, random_state=1).reset_index(drop = True)
for para in num_of_para:
#sampling paragraphs from the 3 groups to build the training set
df_group_1_new = df_group_1_frac[:int(frac1 * para)]
df_group_2_new = df_group_2_frac[:int(frac2 * para)]
df_group_3_new = df_group_3_frac[:int(frac3 * para)]
df_list = [df_group_1_new, df_group_2_new, df_group_3_new]
#training set
df_conf = pd.concat(df_list).reset_index(drop = True)
#preprocessing
X_train_conf = vectorizer.transform(df_conf.review)
#defining the classifier
logreg_conf = LogisticRegression()
#training the classifier
logreg_conf.fit(X=X_train_conf, y=df_conf['sentiment'].to_list())
#calculating the accuracy score on the test set
score_conf = logreg_conf.score(X_test, df_test['sentiment'].to_list())
score_conf_list.append(score_conf)
score_conf_list
[Out]: [0.6525, 0.6835, 0.7235, 0.7525, 0.766, 0.7735, 0.778, 0.7875, 0.796, 0.807]
Results and Conclusion
The accuracies of the three sampling strategies can now be compared, and it is clear that a combination of different confidence intervals performs better than the others. This shows that along with learning new information from uncertain paragraphs the model also requires retaining the previously learned information. Therefore a balance of data from different confidence intervals helps the model learn, maximizing the resulting overall accuracy.
Teams in nearly all fields, spend a majority of their time on research and finding chunks of important information from the huge bulk of unfiltered data and documents that is present within the organization. This process is very time consuming and tedious.
In fields like data science and machine learning, getting annotated data is one of the biggest hurdles and one that the teams tend to spend the most time on.
Apart from this, data annotation can often prove to be expensive as well. Multiple human annotators might need to be hired and this can increase the overall cost of the project.
The DataNeuron platform enables organizations to get accurately annotated data, while minimizing the time, effort and cost expenditure.
DataNeuron’s Semi-Supervised Annotation
What does the platform provide?
The user is provided with an option to define a project structure, which is not limited to a flat classification hierarchy but can incorporate a multilevel hierarchical structure as well with indefinite levels of parent-child relationships between nodes.
This aids research, since the data is essentially divided into groups and further sub-groups depending on the user preference and defined structure which enables the team to adopt a “top-down” approach for getting to the desired data.
The platform takes a semi-supervised approach to data annotation in the sense that the user is required to annotate only about 5–10% of the entire data and the platform annotates the remaining data automatically for the user by detecting contextual similarity and patterns in the data.
How the semi-supervised approach works?
Even for the 5–10% of the total data that still needs to be annotated, the time and effort spent is reduced by a large margin by adopting a suggestion-based validation technique.
The platform provides auto-labeling to the users and suggests the paragraphs that are likely to belong to a specific class based on label heuristics and contextual filtering algorithm; users have to accept or reject at the validation stage.
The semi-supervised approach for validation is broken down into stages:
In the first stage, the user is provided with suggestions based on an intelligent context-based filtering algorithm. The validations done by the user in the first stage are used to improve the accuracy of the filtering algorithm used to provide suggestions for validations.
In the second stage the validation is then further broken down into ‘batches’. This process is repeated for each batch of the second stage, i.e. the validations done in each batch are used to increase the accuracy of the filtering algorithm for the succeeding batch.
This breaks down the problem of annotating a data point into a “one-vs-all” problem which makes it far easier for the user to arrive at an answer(annotation) than if they had to consider all the classes (which might be a huge number depending on the complexity of the problem) for making each individual annotation.
Our platform is a “No-Code” platform and anyone with basic knowledge of the domain they are working on can use the platform to its maximum potential.
Testing On Various Datasets
The platform chooses from among multiple models trained on the same training data, to provide the best possible results to the users.
The average K-Fold accuracy of the model is presented as the final accuracy of the trained model.
We incur a relatively small drop in accuracy as a result of the decreased size of the training data as highlighted. This dip in accuracy is within 12% and can be controlled by the user by annotating more data, or choosing to add seed paragraphs during the validation or feedback and review stage.
Comparisons with an In-House Project
Difference in Paragraphs Annotated. We observe it is possible to reduce annotation effort by up to 96%.
Difference in Time Required. We observe it is possible to reduce time required for annotation by up to 98%.
Difference in Accuracy.
We observe that the DataNeuron platform can decrease the annotation time up to 98%. This vastly decreases the time and effort spent annotating huge amounts of data, and allows teams to focus more on the task at hand.
Additionally it can also help reduce the Subject Matter Expert effort up to 96%, while incurring a marginal cost. Our platform also helps reduce the overall cost of the project by a significant margin, by nearly eradicating the need for data labeling/annotation teams.
In most cases, the need for appointing an SME is also diminished, as the process of annotation is made much simpler and easier and anyone with knowledge of the domain and the project they are working on can be able to perform the annotations through our platform.
