In the rush to implement AI across organizational operations, one must strike a balance between adaptability and accuracy. Should you rely on retrieval-based intelligence to maintain agility, or should you hardwire experience into the model to ensure precision?
This is a strategic decision, and making the right call at the right time can determine the success of everything from automated policy interpretation to conversational AI. Both offer paths to smarter AI; however, they serve different needs, and selecting the wrong one can be the difference between insight and illusion.
RAG: Fast, Flexible, and Context-Aware
Retrieval-Augmented Generation (RAG) is where most organizations begin their journey. Instead of retraining an LLM, RAG enhances its responses by pulling real-timecontext from a vector database. Here’s how it works:
Vector Encoding: Your documents or knowledge base are embedded into a vector store.
Prompt Engineering: At inference time, the user’s query triggers a semantic search.
Dynamic Injection: Relevant documents are retrieved and included in the prompt.
LLM Response: The model uses this injected context to generate a grounded, informed response.
This process is compute-efficient, versionless, and ideal when knowledge is fluid or frequently updated, such as government policies, IoT feeds, or legal frameworks.
Where Does RAG End?
While RAG excels at injecting facts, it has limitations:
It can’t teach the model how to reason.
It doesn’t enforce stylistic consistency.
And when retrieval fails, hallucinations creep in.
That’s your cue: when structure, tone, or deterministic behavior become priorities or when retrieved content isn’t enough to answer correctly, you transition to fine-tuning.
Enter Fine-Tuning: Precision with Permanence
Fine-tuning involves retraining the base model on your domain-specific data, embedding domain-specific language, decision logic, and formatting directly into its parameters.
This is essential when:
You want consistent behavioral patterns (e.g., legal summaries, medical reports).
You need high accuracy where the retrieval is partially optimal or completely absent.
Your workflows involve fixed taxonomies or templates.
Hallucination pt.
Fine-tuning embeds knowledge deep into the model for deterministic output.
Build Both With DataNeuron Without Building Infrastructure
Unlike fragmented ML stacks, DataNeuron lets you orchestrate RAG and fine-tuning in a single interface. Most platforms force teams to juggle disconnected tools just to get a basic RAG or fine-tuning pipeline running. DataNeuron changes that.
Unified no-code interface to design, chain, and orchestrate both RAG and fine-tuning workflows without DevOps dependency
DSEAL powered Dataset Curation to automatically generate high-quality, diverse datasets, structured and ready for fine-tuning with minimal manual prep
Built-in prompt design tools to help structure and adapt inputs for both generation and retrieval use cases
Robust evaluation system that supports multi-layered, continuous testing spanning BLEU/ROUGE scoring, hallucination tracking, and relevance validation, ensuring quality improves over time
Versioned model tracking and performance comparison across iterations, helping teams refine workflows based on clear, measurable outcomes
Use DataNeuron to monitor and iterate across both workflows.
Fine-tune the LLM for tone, structure, and in-domain reasoning.
Layer in RAG to supply the most recent facts or data points.
This hybrid pattern ensures that your AI communicates reliably and stays up to date.
These metrics help ensure both your fine-tuned and RAG-based pipelines stay grounded, efficient, and aligned with real-world expectations.
Start Smart with DataNeuron
A customer support team used fine-tuning on 10,000 Q&A pairs and cut error rates by 40%.
A public sector client layered RAG into live deployments across 50+ policies, with no retraining needed.
Both teams used the same platform. One interface. Multiple workflows. Wherever you are in your AI journey, DataNeuron gets you moving quickly.
If the internet allowed everyone to send data, but there were no rules (like HTTP, TCP/IP, or DNS) on how to format, interpret, or verify it. One site would send text as images, another as binary, and another with no headers. You could connect, but you’d rarely understand what was sent. That’s what MAS looks like without A2A(Agent-to-Agent Protocol).
The Model Context Protocol (MCP) gives multi-agent systems (MAS) a shared communication channel. A2A provides the contractual rules of interaction, making them reliable enough for enterprise and cross-organizational use.
Why Multi-Agent Systems Struggle Without A2A
Even with a strong communication layer (MCP), MAS still face critical shortcomings when there’s no governing protocol like A2A:
Ambiguity of meaning
Lack of trust
Security vulnerabilities
Compliance gaps
Cross-boundary failures
In short, without A2A, multi-agent systems remain prone to misalignment and unsuitable for real-world enterprise environments.
How A2A Works
A2A operates through a set of principles that bring clarity and governance to MAS:
Structured Messages Every message comes with a strict schema with defined types, context, and intent, so ambiguity is removed.
Authentication & Trust Messages can be cryptographically signed, allowing agents to verify the sender’s identity and authority.
Validation Rules Before acting, agents validate whether a message conforms to agreed-upon standards.
Governance Layer A2A encodes rules of interaction: who can do what, under what conditions, and with what accountability.
Cross-Boundary Collaboration Agents across organizations or domains can work together without being tightly coupled, thanks to standardized contracts.
A2A in Action:
With A2A, every step is standardized, signed, and auditable.
By building A2A into our platform, we ensure that agent-to-agent communication isn’t just possible, but governed and reliable. This approach helps organizations:
Operate multi-department workflows with confidence.
Collaborate securely with external vendors’ agents.
Maintain compliance without adding manual oversight.
Our mission is to make MAS not only intelligent but also accountable. A2A is the step that makes that possible.
Why A2A Shapes the Future of Agentic AI
Looking ahead, we believe A2A will define how agent ecosystems evolve in three key ways:
Governed Autonomy Agents won’t just act independently; they’ll act within enforceable rules and standards.
Cross-Organizational Collaboration As businesses connect agents across ecosystems, A2A will be the “link language” that ensures safe cooperation.
Trusted Intelligence Enterprises will demand explainable, auditable AI- A2A provides the contractual layer to deliver it.
At DataNeuron, we move toward ecosystems of interoperable agents, and we believe A2A will be the reason they can do so with confidence.
For years, enterprise AI conversations have revolved around agents. The autonomous entities that plan, reason, and act. In slide decks and product pitches, the agent is portrayed as a brain: it processes inputs, makes decisions, and produces outputs. But when you peel back the layers of a real system, a different story emerges. The agent is only as powerful as the tools it can call.
The new Agentic AI systems are expected not only to reason but also to execute. Before we talk about tools, let’s clarify what an agent really is and why, at DataNeuron, we believe the toolbook deserves just as much attention as the agent itself.
What an Agent Really Does
An agent handles the thinking and decision-making, while tools handle the doing. Tools perform the actual actions, such as classifying text, scraping websites, sending emails, pulling data from CRMs, or writing into dashboards. Without tools, an agent can process information but can’t take action. In short, the agent decides what needs to be done and when.
FromReasoning to Action
This is where the execution layer comes in. Tools translate an agent’s intent into real-world action. Crucially, the agent doesn’t have to know how each tool works internally; it only needs to know three things:
What the tool does
What input to give it
What output to expect
This clean separation of reasoning (agent) and execution (tools) keeps systems modular, interpretable, and easy to govern. You can upgrade or swap out tools without retraining the agent, catering to what large enterprises need: faster iteration cycles and safer deployments.
A Quick Scenario–Customer Support
Suppose your AI receives the task “analyze complaints and send a summary to the team.” A traditional chatbot would try to handle everything within a single model. An agentic system built on DataNeuron does it differently:
Fetches customer history from the CRM using an API-based tool.
Classifies the complaint and extracts order IDs using DataNeuron Native Tools, such as multiclass classifiers and NER.
Retrieves troubleshooting steps via Structured RAG.
Summarizes the case with a custom tool configured by your support ops team.
Sends an acknowledgment using an external mail connector.
The result is an automated pipeline that used to require manual coordination across multiple teams.
Inside the DataNeuron Toolbook
At DataNeuron, we built the Toolbook to make this orchestration simple and scalable. Instead of hand-coding workflows, users can select from a library of pre-built tools or define their own. Everything is callable through standard input/output schemas so that the agent can pick and mix tools without brittle integrations.
We organize our toolbook into four pillars, each extending the agent’s reach differently.
1. DataNeuron Native Tools
These are our first creation in studio-high-utility, pre-configured tools optimized for AI workflows, often known as the “intelligence primitives” of your agent. They’re ready to call as soon as you deploy an agent:
Structured RAG (Retrieval-Augmented Generation): Combines document indexing with structured memory, letting agents pull curated data sets in real time. Ideal for regulatory documents, knowledge bases, or customer support manuals.
Contextual Search: Allows agents to query within a bounded knowledge base, perfect for domain-specific applications like legal, customer service, or biomedical agents.
Multiclass & Multilabel Classifiers: Let agents tag or categorize inputs, such as sorting customer feedback by sentiment and urgency or routing tickets to the right department.
Named Entity Recognition (NER): Extracts names, locations, products, and other entities, essential for parsing resumes, contracts, or customer emails.
You don’t code these tools; you configure them. The agent calls them as needed, with predictable inputs and outputs.
2. External Tools
These extend the agent’s reach into the broader digital ecosystem. Think of them as bridges between your agent and the open web or third-party services. Examples include:
Web Scraper to pull structured data from webpages, prices, job postings, and event schedules.
Google, Wikipedia, and Arxiv Search for real-time knowledge retrieval, essential for summarizing or validating claims.
Mail Sender to automate communications, acknowledgments, follow-ups, and onboarding instructions.
With external tools, your agent can enrich its answers, validate facts, and trigger outward-facing actions.
3. Custom Tools
Not every enterprise workflow fits into an off-the-shelf template. That’s why we let you create custom tools by simply defining:
name (e.g., “SummarizeComplaint”)
description (“Summarizes customer complaint emails into action items”)
input/output schema
Based on this metadata, the DataNeuron platform generates a callable tool automatically. This is especially powerful in domains where business logic is unique, such as parsing health insurance claims, configuring automated compliance checks, or running internal analytics.
You define what the tool does, not how it does it, while the system handles the integration.
4. API-Based Tools
These connect agents to external systems or databases, turning your AI from a smart assistant into an operational actor. You define the tool’s:
Name and purpose
API endpoint and method
Auth/token structure
Request/response format
From there, the platform generates a tool that the agent can call. This enables workflows like:
Fetching real-time data from a food delivery backend.
Pushing recommendations into a CRM.
Triggering marketing campaigns.
API-based tools let agents interact with your production systems securely and at scale.
Let’s consider another scenario of a Digital Health Assistant
To see how these pieces fit together, imagine a hospital deploying a digital health assistant for its doctors. A patient logs in and requests an explanation of their latest blood test report:
API-Based Tool fetches the patient’s lab results from the hospital’s CRM or EHR database.
DataNeuron Native Tools (NER + multilabel classifier + Structured RAG) extract key metrics, flag abnormal values, and pull relevant medical guidelines from an internal knowledge base.
Custom Tool created by the hospital’s analytics team generates a plain-language summary of the patient’s health status and next steps.
External Tools email the report to the patient and physician, and optionally pull the latest research articles to confirm if the doctor requests supporting evidence.
All of this happens automatically. The agent decides the sequence of actions; each tool performs its specific function. Data is fetched, analyzed, explained, enriched with context, and delivered without the doctor or patient stitching the pieces together manually.
Why This Matters?
Moving from model-first to tool-first thinking turns AI from a smart assistant into an operational actor. Modular tools let agents take sequential actions toward complex goals while giving enterprises governance and flexibility: tools can be audited or swapped without altering the agent’s logic, new capabilities can be added like apps on a phone, and clear input/output schemas simplify security and compliance integration.
The most valuable AI tool in the future won’t be the one that “knows” everything. It will be the one that knows how to get things done, and that’s exactly what the DataNeuron Agentic AI Toolbook is built for.
At DataNeuron, we’re not trying to replace engineers, but giving them a new medium. Workflows can be designed using reusable tools, customized by intent, and executed by agents who know when and why to use them. Instead of one massive, brittle model, you get a living ecosystem where each component can evolve independently.
AI progress has been upgraded with larger models, more parameters, and bigger datasets. This created powerful Large Language Models (LLMs), but exposed their limits: even the best models falter in multi-domain workflows, hallucinate facts, lose context, and struggle with complex coordination.
Multi-Agent Systems (MAS) emerged to address these gaps by deploying specialized agents for tasks like summarization, search, compliance checking, and analysis. Together, they can outperform a single model but only if they work coherently. In enterprise customer support, for example, one agent may retrieve knowledge, another analyze sentiment, and a third draft a reply. Without shared context, they duplicate work, contradict each other, or miss critical data.
The Model Context Protocol (MCP) closes this gap. It standardizes how agents exchange state, intent, and outputs, turning isolated components into a coordinated, auditable system capable of reliable multi-step outcomes at scale.
Why Current Multi-Agent Systems Fall Short
Before understanding MCP, let’s look at what MAS misses without it:
Today’s MAS often acts like loosely coupled tools rather than a synchronized team. The result is unpredictability, an unacceptable outcome for enterprise use cases where accuracy, compliance, and auditability matter.
MCP: A Protocol Born of Necessity
The Model Context Protocol (MCP) is a standardized communication framework that enables agents in a multi-agent system to “speak the same language.” Acting as both a universal translator and a message bus, MCP lets any agent, whether an LLM, retrieval engine, API connector, or compliance checker, exchange context reliably and consistently.
How MCP Works
At its core, MCP provides five foundational capabilities:
Standardized Messaging
Shared Memory Access
Publish/Subscribe Coordination
Dynamic Composi tion
Medium-Agnostic Transport
How would this work in Financial Compliance?
Consider a bank’s compliance workflow:
One agent ingests regulatory documents.
Another checks transactions against relevant rules.
A third summarizes the findings for auditors.
With MCP, the pipeline is traceable, resilient, and composable: each agent publishes standardized outputs into a shared context, while downstream agents subscribe and act on verified data.
MCP in Action at DataNeuron
At DataNeuron, MCP is treated as the connective tissue of intelligent automation. MCP lets them expose functionality via an HTTP server, a studio server, or a custom API and register it under the MCP schema. From that moment, MCP handles orchestration: routing intent, synchronizing state, and coordinating workflows.
This design allows us to:
Integrate LLMs with retrieval engines and domain-specific APIs seamlessly.
Orchestrate cross-departmental workflows without losing auditability.
Scale agent ecosystems without creating central bottlenecks.
By formalizing how agents communicate and share context, MCP converts fragmented tools into a unified, auditable, and scalable multi-agent system ready for real-world deployment.
Why MCP Is Foundational to the Next Wave of Agentic AI
Enterprise AI is moving away from monolithic, one-size-fits-all models toward modular, composable systems. In this new architecture, the MCP functions as the critical communication backbone, allowing intelligent agents to coordinate, adapt, and scale reliably.
By standardizing how context, state, and intent flow between agents, MCP lays the groundwork for future-proof AI ecosystems. Three shifts illustrate this impact:
Composable Intelligence
Governed Autonomy
Cross-Ecosystem Interoperability
Taken together, these shifts position MCP as a cornerstone of scalable, auditable, and future-ready multi-agent systems. MCP is the infrastructure layer that enables businesses to design AI workflows that are as dynamic and trustworthy as the environments in which they operate.
As large language models transition from lab experiments to real-world applications, the way we evaluate their performance must evolve. A casual thumbs-up after scanning a few outputs might be fine for a weekend project, but it doesn’t scale when users depend on models for accuracy, fairness, and reliability.
LLM evaluations or evals do this job for you. They turn subjective impressions into structured, repeatable measurements. More precisely, evals transform the development process from intuition-driven tinkering into evidence-driven engineering, a shift that’s essential if we want LLMs to be more than just impressive demos.
The Eval-Driven Development Cycle: Train, Evaluate, Repeat
At DataNeuron, evaluation (Eval) is the core of our fine-tuning process. We follow a 5-step, iterative loop designed to deliver smarter, domain-aligned models:
1. Raw Docs
The process starts with task definition. Whether you’re building a model for summarization, classification, or content generation, we first collect raw, real-world data, i.e., support tickets, reviews, emails, and chats, directly from your business context.
2. Curated Evals
We build specialized evaluation datasets distinct from the training data. These datasets are crafted to test specific capabilities using diverse prompts, edge cases, and real-world scenarios, ensuring relevance and rigor.
3. LLM Fine-Tune
We fine-tune your model (LLaMA, Mistral, Gemma, etc.) using task-appropriate data and lightweight methods like PEFT or DPO, built for efficiency and performance.
4. Eval Results
We evaluate your model using curated prompts and subjective metrics like BLEU, ROUGE, and hallucination rate, tracking not just what the model generates, but how well it aligns with intended outcomes.
5. Refinement Loop
Based on eval feedback, we iterate, refining datasets, tweaking parameters, or rethinking the approach. This cycle continues until results meet your performance goals.
Evals guide you towards better models by providing objective feedback at each stage, ensuring a more intelligent and efficient development cycle. So, what exactly goes into a robust LLM evaluation framework?
Core Components of a Robust LLM Evaluation Framework
Human Validation
We recognize the invaluable role of human expertise in establishing accurate benchmarks. Our workflow enables the generation of multiple potential responses for a given prompt. Human validators then meticulously select the response that best aligns with the desired criteria. This human-approved selection serves as the definitive “gold standard” for our evaluations.
Prompt Variations
DataNeuron empowers users to define specific “eval contexts” and create diverse variations of prompts. This capability ensures that your model is rigorously evaluated across a broad spectrum of inputs, thereby thoroughly testing its robustness and generalization capabilities.
Auto Tracking
Our evaluation module automatically compares the responses generated by your fine-tuned model against the human-validated “gold standard.” This automated comparison facilitates the precise calculation of accuracy metrics and allows for the consistent tracking of how well your model aligns with human preferences. The fundamental principle here is that effective fine-tuning should lead the model to progressively generate responses that closely match those previously selected by human validators.
Configurable Pipelines
We prioritize flexibility and control. DataNeuron’s entire evaluation process is highly configurable, providing you with comprehensive command over every stage from data preprocessing and prompt generation to the selection of specific evaluation metrics.
DataNeuron: Your Partner in Eval-Driven Fine-Tuning
At DataNeuron, we’re building a comprehensive ecosystem to streamline your LLM journey, and Evals are a central piece of that puzzle. While we’re constantly evolving, here’s a glimpse of how DataNeuron empowers you with eval-driven fine-tuning:
Core Tenets of DataNeuron’s Evaluation Methodology
Human Validation:
We recognize the invaluable role of human expertise in establishing accurate benchmarks. Our workflow enables the generation of multiple potential responses for a given prompt. Human validators then meticulously select the response that best aligns with the desired criteria. This human-approved selection serves as the definitive “gold standard” for our evaluations.
Prompt Variations:
DataNeuron empowers users to define specific “eval contexts” and create diverse variations of prompts. This capability ensures that your model is rigorously evaluated across a broad spectrum of inputs, thereby thoroughly testing its robustness and generalization capabilities.
Auto Tracking:
Our evaluation module automatically compares the responses generated by your fine-tuned model against the human-validated “gold standard.” This automated comparison facilitates the precise calculation of accuracy metrics and allows for the consistent tracking of how well your model aligns with human preferences. The fundamental principle here is that effective fine-tuning should lead the model to progressively generate responses that closely match those previously selected by human validators.
Configurable Pipelines:
We prioritize flexibility and control. DataNeuron’s entire evaluation process is highly configurable, providing you with comprehensive command over every stage from data preprocessing and prompt generation to the selection of specific evaluation metrics.
Best Practices & Avoiding the Potholes
Here are some hard-earned lessons to keep in mind when implementing eval-driven fine-tuning:
Don’t Overfit to the Eval:
Just like you can overfit your model to the training data, you can also overfit to your evaluation set. To avoid this, diversify your evaluation metrics and periodically refresh your test sets with new, unseen data.
Beware of Eval Drift:
The real-world data your model encounters can change over time. Ensure your evaluation datasets remain representative of this evolving reality by periodically updating them.
Balance Latency and Quality:
Fine-tuning can sometimes impact the inference speed of your model. Carefully consider the trade-off between improved quality and potential increases in latency, especially if your application has strict performance SLAs.
With its focus on structured workflows and integration, DataNeuron urges users to build more reliable and effective LLM-powered applications. Moving beyond subjective assessments is crucial for unlocking the full potential of LLM fine-tuning. Evals provide the objective, data-driven insights you need to build high-performing, reliable models.
At DataNeuron, we’re committed to making this process seamless and accessible, empowering you to fine-tune your LLMs and achieve remarkable results confidently.
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.
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.
