Custom Intelligence for Your Enterprise.

LLM Fine-Tuning Company in Delhi

Generic AI models don't speak your industry's language. Tech startups and enterprise companies in Delhi need AI that understands their specific domainâ€”whether it's medical terminology, complex legal phrasing, or proprietary coding syntax. We fine-tune open-source LLMs to perform specialized tasks with high accuracy, running securely on your own infrastructure.

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About Model Training in Delhi

Teaching AI Your Exact Workflows

Off-the-shelf APIs from OpenAI or Anthropic are powerful, but they have limitations. They charge per token, which gets expensive at scale, and you have to send your sensitive data to their servers. More importantly, they often fail at highly specialized tasks without extensive prompt engineering.

We take foundational models like Llama 3 or Mistral and adapt their underlying weights using your curated datasets. If you run a healthcare tech company in Nehru Place, we can fine-tune a model to read radiology reports and extract exactly the JSON format your software needs, every single time, without hallucinating.

Working closely with Delhi clients, our machine learning engineers handle the complex pipeline of data cleaning, formatting, distributed training, and quantization. The final output is an AI model that you own completely, ready to be deployed on your AWS, Azure, or local GPU clusters.

Our Services

What Will You Get?

Dataset Preparation

We clean, format, and structure your raw data into high-quality instruction-response pairs required for training.

Domain Fine-tuning

Using techniques like LoRA and QLoRA to efficiently adapt large models to your specific industry knowledge.

Instruction Tuning

Teaching the model to follow specific output formats, like generating exact JSON structures or code snippets.

RLHF Implementation

Aligning the model's behavior with human preferences to reduce toxicity and improve helpfulness.

Model Evaluation

Rigorous benchmarking against custom metrics to ensure the fine-tuned model outperforms generic APIs.

Model Deployment

Quantizing the final model and deploying it on cost-effective cloud instances via custom inference endpoints.

Why Choose Us?

Why Businesses in Delhi Trust Our AI Solutions?

Local Understanding

We build AI that fits how Delhi actually works.

Real Use Cases

We implement AI that saves staff time and resolves queries.

Seamless Integration

Connects perfectly with your CMS and telecom providers.

How Do We Work?

Our team members follow a step-by-step process to fine-tune a language model. Here's the process

Client Communication

We start by understanding your domain, the kind of queries you handle, and where a general AI model currently falls short for you.

Planning

We map out what data is available, what needs cleaning, and the fine-tuning approach, then share the plan with you before starting.

Model Development

We prepare your domain data and fine-tune the model, checking its outputs against real examples from your field as we go.

Performance and Bug Testing

We test the fine-tuned model against tricky, real questions from your domain and compare its accuracy to the base model.

Final Launch

Once the model performs reliably, we hand it over connected to wherever your team or product needs to use it.

Evaluation

We benchmark the fine-tuned model's answers against your domain requirements so you can see the measurable improvement.

Maintenance

We stay available to retrain or extend the model as your documents, products or terminology change over time.

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Build Domain Specific AI

Why choose us?

Why Choose Us?

Learn about all the reasons why you should choose Seven Web Tech as your LLM fine-tuning company in Delhi

Domain-First Approach

We fine-tune around the specific terminology and query patterns of your field, not a generic dataset.

Measurable Accuracy Gains

We test and compare the fine-tuned model against the base model, so you can see the actual improvement before rollout.

Careful Data Handling

Your training data, often sensitive, like support transcripts or client records, is handled under strict confidentiality throughout the process.

Realistic Timelines

We give you a clear, honest timeline based on how much data needs preparing, not an inflated promise.

Built for Regulated Fields

We understand the extra care needed when fine-tuning for legal, healthcare-adjacent or financial use cases.

Ongoing Support

We remain available after handover to retrain the model as your domain data or requirements change.

Projects We Delivered

What We Create

Need Any Help?

Frequently Asked Question

Drop us a line and we will get back to you.

Which foundational models do you fine-tune?

We fine-tune open-source models like Llama 3, Mistral, and Falcon depending on your computational budget and specific use case.

How much data is required for fine-tuning?

While you can do lightweight tuning (like LoRA) with a few hundred high-quality examples, a robust domain-specific model usually requires thousands of curated data points.

What is the difference between RAG and Fine-Tuning?

RAG pulls information from external documents to answer questions. Fine-tuning actually changes the underlying model's behavior, tone, and deep domain knowledge.

Do we need to buy expensive GPUs to run the model?

Not necessarily. We optimize models using quantization (like 4-bit or 8-bit) so they can run efficiently on more affordable cloud instances or even local servers.

Who owns the fine-tuned model?

You do. We provide you with the final model weights which you can host on your own AWS, Azure, or local servers.

How do you ensure the model doesn't hallucinate?

Through rigorous data cleaning, RLHF (Reinforcement Learning from Human Feedback), and strict evaluation metrics during the training loop.