Core service
LLM Fine-Tuning
Supervised fine-tuning with LoRA and QLoRA, plus preference tuning (DPO), so the model learns your terminology, format and tone without training from scratch.
LLM Development & Fine-Tuning Services
Build a powerful AI that understands your industry
Move beyond the limits of public AI. We fine-tune language models on your own data and run them in a private environment, so you get accurate, context-aware answers that automate complex work.
Can we ship order SO-4471 before the Q3 price change?
General model
I don't have access to your orders or pricing. Please check your ERP system or contact your sales team.
Fine-tuned on your data
Yes. SO-4471 is picked and packed, and the carrier slot is Thursday. That's 6 days before the Q3 price list takes effect, so the current prices apply.
What we offer
Our Large Language Model Development Services
We provide end-to-end custom LLM development that turns your proprietary data into a powerful, secure AI asset. Our AI consultants and engineers combine deep technical skill with industry knowledge to build models aligned with your business needs.
Custom LLM Development
Domain-specific language models built around your business: the right base model, your data and your tasks, delivered as a secure AI asset you own.
LLM Integration
We connect language models to your ERP, CRM, help desk and internal tools, so answering, drafting and summarizing happen where your team already works.
Learn more about LLM IntegrationLLM Consulting
An honest view of whether you need fine-tuning, RAG or just better prompts, which model to start with, and what it will cost to run.
Learn more about LLM ConsultingPrompt & Context Engineering
System prompts, examples and structured outputs designed and tested against real questions, so responses are accurate and consistent.
LLM API Development
Secure, well-documented APIs around your model, with authentication, rate limits, streaming and usage tracking, ready for your apps to call.
LLM-Powered Chatbots & Agents
Assistants that talk naturally, look up your data and take actions through tools, for customer support, sales and internal help desks.
Learn more about LLM-Powered Chatbots & AgentsEvaluation & LLMOps
Test sets, automated scoring and monitoring for accuracy, cost and drift, so the model keeps performing after launch.
Learn more about Evaluation & LLMOpsWhy a custom LLM
A Proprietary Competitive Advantage
Standard AI models work well for many tasks, but a model trained on your own data gives you a real edge. It knows your terms and understands exactly how you operate.
That deep knowledge means better accuracy and results generic models can't match, and a custom LLM is an asset you own and control.
- 01
Enhanced Data Security
Your model runs in a private, isolated environment, in your cloud or on-premise, and your data is never used to train anyone else's AI.
- 02
Unmatched Accuracy
An AI that speaks your business language, so results are more accurate than generic models can give.
- 03
Proprietary AI Asset
Move beyond off-the-shelf AI with a model you own, giving you a lasting competitive advantage.
- 04
Fits Your Workflows
The model plugs into your existing systems and processes, so your team gets the benefit without learning a new tool.
Choosing the approach
Fine-Tuning, RAG or Prompt Engineering?
Not every project needs a fine-tuned model. We start with the simplest approach that meets your goals, and often combine them.
| Prompt Engineering | RAG (Retrieval) | Our focusFine-Tuning | |
|---|---|---|---|
| Use it when | The model already knows the subject and just needs clear instructions | Answers depend on documents or data that change often | You need your terminology, a fixed format or tone, or a smaller model |
| Strength | Fastest and cheapest to start, easy to change | Always up to date, and every answer can cite its source | Consistent output, shorter prompts, lower cost per request |
| Limit | Can't teach new knowledge or a very specific style | Doesn't change how the model writes or reasons | Needs good example data and retraining over time |
Process
Our LLM Development Process
Strategy & Roadmap
We pick the tasks worth solving, agree how success will be measured and choose the approach and base model.
Data Preparation
We collect, clean, label and secure your proprietary data, and remove personal information before any training.
Fine-Tuning
We fine-tune the model for domain-specific expertise, comparing runs and base models on your own test set.
Evaluation & Guardrails
Accuracy, safety and bias are tested before launch, with filters and fallbacks for questions the model shouldn't answer.
Deployment & LLMOps
We integrate the LLM with your systems, then monitor quality and cost and retrain as your data changes.
Industries
Our LLM Expertise Across Key Industries
We fine-tune custom LLMs with your industry's data, so they understand your terminology, compliance needs and operational details for more accurate, relevant results.
Technology
Models and Tools We Work With
We aren't tied to one AI provider. We test the options on your task and pick what gives the best balance of accuracy, speed, cost and privacy.
- Open-Source Models
- LlamaMistralQwenGemmaDeepSeek
- Hosted Models
- OpenAI GPTAnthropic ClaudeGoogle Gemini
- Fine-Tuning
- Hugging FacePEFT (LoRA, QLoRA)TRLUnslothAxolotl
- Serving & Cloud
- vLLMOllamaAWS Bedrock & SageMakerAzure AI FoundryGoogle Vertex AI
Why Infilon
Why Choose Infilon for Custom LLMs?
As a leading AI development company in Ahmedabad, India, we specialize in building truly custom large language models. Our end-to-end process turns your proprietary data into a secure, intelligent, high-performance AI asset.
We start by understanding your business challenges, then deliver LLM solutions that improve operational efficiency and open new opportunities for innovation and growth.
Our development partnership
- We integrate seamlessly with your in-house team
- Deep collaboration ensures shared goals and success
- Clear communication keeps you informed and in control
- Ongoing support guarantees your LLM's long-term success
FAQ
LLM Development & Fine-Tuning FAQs
What is custom LLM development?
Custom LLM development means adapting a large language model to your business: choosing a base model, training or fine-tuning it on your own data, connecting it to your systems and deploying it securely. The result understands your terminology and tasks far better than a general-purpose chatbot.
What is LLM fine-tuning?
Fine-tuning continues a pre-trained model's training on a smaller set of your own examples, such as past support replies, reports or documents. Techniques like LoRA and QLoRA only train a small set of extra weights, so it's much faster and cheaper than training a model from scratch.
Should we fine-tune or use RAG?
Use RAG when answers depend on information that changes often and needs a source. Fine-tune when you need a specific style, format or vocabulary, or a smaller, cheaper model. Many production systems use both: a fine-tuned model that answers from documents retrieved with RAG.
How much data do we need to fine-tune a model?
Less than most people expect. A few hundred to a few thousand high-quality examples is often enough for a focused task. Quality matters more than quantity, and we help you collect, clean and label the data.
Which base model will you use?
It depends on your task, budget and privacy rules. We often compare open-source models like Llama, Mistral, Qwen or Gemma with hosted models like GPT, Claude or Gemini on your own test set, then recommend the one with the best balance of accuracy, speed and cost.
Can a small language model do the job instead of a large one?
Often, yes. For a focused task, a small open-source model fine-tuned on your data (or distilled from a larger model's answers) can match a large model's accuracy at a fraction of the cost and latency, and it can run on your own servers. We test both on your task before recommending one.
Will our data stay private?
Yes. Open-source models can be fine-tuned and hosted in your own cloud or on-premise, so data never leaves your environment. When we use hosted models, we use enterprise terms that exclude your data from provider training.
How long does a custom LLM project take?
A proof of concept on your data usually takes a few weeks. A production rollout with integrations, evaluation and monitoring typically takes two to three months, depending on scope.
Ready to Start Your Custom LLM Project?
Tell us the task and where your data lives. We'll tell you whether fine-tuning is the right approach, and what a first version would look like.
Discuss Your Project