Custom LLM Development Services in India
Build custom large language models with Aimodels.in. We offer LLM architecture design, training pipelines, fine-tuning, evaluation, and production deployment for Indian businesses.
Service Overview
Large language models are the engine behind modern AI products, but off-the-shelf APIs often fall short on domain accuracy, data privacy, cost at scale, and latency. Aimodels.in builds custom LLM solutions that give Indian businesses full control over model behavior, training data, and deployment infrastructure. Our team has hands-on experience with the full LLM lifecycle, from architecture selection and pretraining data curation to supervised fine-tuning, alignment with RLHF or DPO, rigorous evaluation, and high-throughput serving. Whether you need a domain-specialized model for legal document analysis, a multilingual model for Indian languages, or a cost-optimized model that runs on your own GPUs, we tailor the architecture and training strategy to your accuracy, latency, and budget targets. We work with open-source foundations like Llama, Mistral, and Qwen, and we know when a fine-tuned open model outperforms a paid API. Every engagement includes reproducible training pipelines, comprehensive evaluation suites, and deployment configurations that your team can maintain. The result is a language model that understands your domain, respects your data boundaries, and delivers measurable improvements over generic alternatives.
How We Work — Our Process
A structured, transparent engagement model that ensures delivery quality at every step.
Requirements & Architecture Selection
We analyze your use case, accuracy targets, latency budget, data availability, and infrastructure to select the right base model and architecture strategy.
Week 1Data Curation & Preparation
We collect, clean, deduplicate, and format training and evaluation data, with quality filtering, toxicity removal, and domain-specific tokenization where needed.
Weeks 2-3Training Pipeline Build
We engineer reproducible training pipelines covering pretraining continuation, supervised fine-tuning, and alignment with checkpointing and experiment tracking.
Weeks 4-5Fine-Tuning & Alignment
We apply supervised fine-tuning on domain data and align the model using RLHF, DPO, or preference optimization to improve helpfulness and safety.
Week 6Evaluation & Benchmarking
We run the model against domain benchmarks, standard LLM evaluations, and custom test suites to validate accuracy, safety, and performance before deployment.
Week 7Deployment & Serving
We deploy the model with optimized inference using vLLM, TGI, or TensorRT-LLM, with autoscaling, monitoring, and CI/CD for future retraining.
Week 8Why Choose Us
Our key differentiators that set us apart in the AI services landscape.
Domain-Specialized Accuracy
We fine-tune models on your domain data so they outperform generic APIs on accuracy, terminology, and task-specific instructions by a measurable margin.
Inference Cost Optimization
We optimize serving with quantization, batching, and speculative decoding so your custom model costs less per token than commercial API alternatives.
Data Privacy & Sovereignty
Training and inference can run entirely in your cloud or on-premises, so sensitive data never leaves your infrastructure or reaches third-party APIs.
Reproducible Pipelines
Every training run is versioned, tracked, and reproducible with DVC and MLflow, so your team can retrain, audit, and roll back with confidence.
Indian Language Support
We build multilingual models with strong performance on Hindi, Tamil, Telugu, Bengali, Marathi, and other Indian languages, not just English-centric baselines.
Production-Grade Serving
We deploy with vLLM or TensorRT-LLM for high throughput, streaming support, and autoscaling, with monitoring and CI/CD for continuous improvement.
What We Offer
Detailed breakdown of each offering within this service category.
Custom LLM Architecture
Architecture selection and design covering base model choice, parameter sizing, context length optimization, and tokenizer adaptation for your domain and languages.
- Architecture recommendation report with tradeoff analysis
- Tokenizer evaluation and custom vocabulary if needed
- Context length and attention strategy documentation
LLM Training Pipeline
Reproducible training pipelines for pretraining continuation and supervised fine-tuning, with data curation, distributed training, checkpointing, and experiment tracking.
- Data curation pipeline with quality and toxicity filtering
- Distributed training scripts with DeepSpeed or FSDP
- MLflow experiment tracking with full reproducibility
LLM Fine-Tuning
Supervised fine-tuning and alignment using LoRA, QLoRA, RLHF, and DPO to specialize models on your domain tasks and align them to your quality and safety standards.
- Fine-tuning pipeline with LoRA and full-parameter options
- RLHF or DPO alignment with preference dataset curation
- Checkpoint selection and model merging strategy
LLM Evaluation & Benchmarking
Comprehensive evaluation covering domain accuracy, standard LLM benchmarks, safety testing, latency profiling, and comparison against baseline APIs.
- Custom evaluation suite with 200+ domain test cases
- Standard benchmark scores across MMLU, MT-Bench, and domain sets
- Latency and throughput profiling report
LLM Deployment & Serving
Production deployment with optimized inference servers, autoscaling, streaming APIs, monitoring, and CI/CD pipelines for seamless retraining and rollout.
- vLLM or TensorRT-LLM serving configuration with autoscaling
- OpenAI-compatible API endpoint with streaming support
- Monitoring dashboards for latency, throughput, and error rates
Technology Stack
The tools, platforms, and frameworks we use to deliver this service.
| PyTorch | Deep learning framework for training | Expert |
|---|---|---|
| Hugging Face Transformers | Model loading and fine-tuning | Expert |
| DeepSpeed / FSDP | Distributed training acceleration | Expert |
| LoRA / QLoRA | Parameter-efficient fine-tuning | Expert |
| TRL (Transformer RL) | RLHF and DPO alignment | Expert |
| vLLM | High-throughput inference serving | Expert |
| TensorRT-LLM | NVIDIA-optimized inference | Advanced |
| MLflow / DVC | Experiment tracking and versioning | Expert |
| Weights & Biases | Training visualization and collaboration | Advanced |
| Ray / Kubernetes | Distributed compute orchestration | Advanced |
Use Cases & Industry Applications
Real-world scenarios where this service delivers measurable business impact.
Engagement Timeline & Impact Metrics
Project Timeline
| Phase | Duration | Key Deliverable |
|---|---|---|
| Architecture | Week 1 | Model selection and design doc |
| Data Prep | Weeks 2-3 | Curated training and eval datasets |
| Training | Weeks 4-5 | Reproducible training pipeline |
| Alignment | Week 6 | Fine-tuned and aligned model |
| Eval & Deploy | Weeks 7-8 | Benchmarked and served model |
Business Impact
| Metric | Before AI | After AI |
|---|---|---|
| Domain accuracy | 68% | 89% |
| Inference cost per 1M tokens | ₹2,400 | ₹860 |
| Latency (P95) | 1,800ms | 420ms |
| Hallucination rate | 14% | 4% |
| Data sovereignty | None | Full on-prem |
Our Capabilities
| Capability | Status |
|---|---|
| Custom architecture design | Available |
| Pretraining and fine-tuning | Available |
| RLHF and DPO alignment | Available |
| Multilingual training | Available |
| Evaluation and benchmarking | Available |
| Production serving | Available |
Pricing & Packages
Transparent pricing for every engagement size. All packages include post-delivery support.
| Tier | Price | Timeline | Includes |
|---|---|---|---|
| Starter | ₹99,000 | 4 weeks | Fine-tuning on open model with LoRA and basic evaluation |
| Growth | ₹2,49,000 | 8 weeks | Full training pipeline with alignment and benchmarking |
| Enterprise | ₹5,99,000 | 12 weeks | Custom architecture, multilingual, on-prem deployment, and CI/CD |
What Is Included
- Architecture selection and design documentation
- Data curation pipeline with quality filtering
- Training scripts with distributed computing support
- Fine-tuning with LoRA, QLoRA, and full-parameter options
- RLHF or DPO alignment with preference dataset curation
- Evaluation suite with domain and standard benchmarks
- Production serving configuration with vLLM
- Monitoring dashboards and CI/CD for retraining
If your custom model does not outperform your current API baseline on domain accuracy benchmarks, we will provide an additional fine-tuning cycle at no cost.
Book a Free Consultation
Speak with our AI experts about your specific requirements. We will assess your needs, recommend the right approach, and provide a detailed proposal within 48 hours.
Book Your Free Consultation →Frequently Asked Questions
Do we need our own GPUs to train and serve a custom LLM?
Not necessarily. Training can run on cloud GPU providers like AWS, GCP, Azure, or Lambda Labs, and we can use spot instances to reduce cost. For serving, we help you choose between cloud GPU rental, dedicated GPU instances, or on-premises hardware based on your usage volume and latency needs.
How much training data do we need for fine-tuning?
For LoRA fine-tuning, as few as 500 to 2,000 high-quality examples can produce meaningful improvements. For full-parameter fine-tuning or pretraining continuation, we typically work with 50,000 to 500,000 examples. We assess your data and recommend the right approach during the architecture phase.
Can a fine-tuned open model really beat GPT-4 on our domain?
Yes, for domain-specific tasks. Fine-tuned open models regularly outperform GPT-4 on specialized accuracy because they are trained on your domain data and aligned to your task definitions. GPT-4 remains stronger for broad general knowledge, but for focused workflows a custom model wins on accuracy, cost, and latency.
How do you prevent hallucinations in custom models?
We use multiple techniques including high-quality training data curation, RLHF or DPO alignment to penalize unsupported claims, retrieval-augmented generation at inference time, and evaluation suites that specifically test for hallucination rates on your domain facts.
What about Indian language support?
We have deep experience building multilingual models for Hindi, Tamil, Telugu, Bengali, Marathi, and other Indian languages. We evaluate tokenizers for Indian script coverage, curate multilingual training data, and benchmark on Indic language tasks to ensure real performance, not just tokenization support.
How do you handle model versioning and retraining?
Every training run is tracked in MLflow with full data and code versioning via DVC. We build CI/CD pipelines that can retrain on new data, run evaluation gates, and deploy only if benchmarks pass, so your model improves continuously without manual overhead.
What is the difference between LoRA, QLoRA, and full fine-tuning?
LoRA trains lightweight adapter layers on top of a frozen base model, making it fast and memory-efficient. QLoRA adds 4-bit quantization to reduce memory further. Full fine-tuning updates all model parameters for maximum performance but requires significantly more compute. We recommend based on your data, budget, and accuracy targets.
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