LLM Development and Fine Tuning Services for Enterprises

KriraAI builds custom large language models and fine tunes leading open source and proprietary models on your own data. Our LLM development services help enterprises move from generic AI tools to private, domain specific models that understand their terminology, workflows and compliance requirements. We handle the full lifecycle, from use case discovery and data preparation to deployment, monitoring and retraining in production.

What Is LLM Development and Fine Tuning?

LLM development is the process of designing, training and deploying a large language model for a specific business purpose, while LLM fine tuning is the process of further training a pre trained model on curated, domain specific data so it performs a defined task more accurately. Most enterprises do not need to train a model from scratch. Fine tuning an existing base model such as Llama, Mistral or Qwen delivers higher accuracy on your tasks at a fraction of the cost and time.

A general purpose model knows a little about everything, but it does not know your products, your documents or the way your teams write. Fine tuning closes that gap by teaching the model your vocabulary, output formats and decision patterns. The result is a custom LLM that produces consistent, reliable outputs and can be deployed privately within your own infrastructure.

Our LLM Development and Fine Tuning Services

Every engagement is scoped around a measurable business outcome rather than a model for its own sake. Our team combines machine learning engineering, data engineering and MLOps experience to deliver production grade systems. Below are the core services we offer as an LLM development company.

Custom LLM Development

We design and build custom LLMs tailored to a single domain or a family of related tasks. This includes selecting the right architecture and base model, building training datasets and setting up evaluation benchmarks that reflect real business usage. Custom LLM development suits organizations with large proprietary datasets and use cases that off the shelf models cannot handle well.

LLM Fine Tuning Services

Our LLM fine tuning services adapt a pre trained model to your tasks using supervised fine tuning, instruction tuning and parameter efficient methods. We fine tune LLMs for classification, extraction, summarization, document drafting, code generation and conversational agents. Each fine tuned model is benchmarked against the base model so you can see the exact improvement before deployment.

Private LLM Development and Deployment

A private LLM runs entirely inside your cloud account or on premise servers, so sensitive data never leaves your environment. We deploy models with optimized inference using tools such as vLLM and quantization to keep latency and hosting costs under control. This approach is ideal for regulated sectors including healthcare, banking, insurance and legal services.

Enterprise LLM Integration

An enterprise LLM delivers value only when it connects to the systems your teams already use. We integrate models with CRMs, ERPs, document management systems, internal knowledge bases and customer support platforms through secure APIs. Access controls, audit logs and usage monitoring are built in from the start.

RAG Pipeline Development

Retrieval augmented generation connects a language model to your live knowledge sources so answers stay current and traceable. We build RAG pipelines with document chunking, embedding models, vector databases and reranking to improve answer accuracy. Many of our enterprise deployments combine RAG with a fine tuned model for the best balance of accuracy and freshness.

LLM Consulting and Strategy

Our LLM consulting service helps leadership teams decide where generative AI will deliver real returns. We assess your data readiness, compare build versus buy options and estimate infrastructure costs before any development begins. You receive a clear roadmap with prioritized use cases, model recommendations and success metrics.

Fine Tuning Methods We Use

Choosing the right fine tuning method affects cost, accuracy and how often a model can be updated. We select the method based on dataset size, hardware budget and the depth of behavior change required. The table below summarizes when each approach fits best.

Method

Best For

Compute Cost

Data Needed

Full parameter fine tuning

Deep domain adaptation

High

Large

LoRA and QLoRA

Fast, cost efficient task adaptation

Low

Small to medium

Instruction tuning

Following formats and instructions

Medium

RLHF and DPO

Aligning tone, safety and preferences

Medium to high

Preference pairs

LoRA fine tuning trains small adapter layers instead of every model weight, which reduces GPU memory needs significantly. QLoRA goes further by quantizing the base model, making it possible to fine tune large models on modest hardware. For most enterprise tasks, LoRA based methods offer the strongest return on investment.

RAG vs Fine Tuning: Which Approach Fits Your Business?

RAG is better when answers depend on frequently changing information, while fine tuning is better when the model must learn a consistent style, format or specialized reasoning pattern. RAG retrieves facts at query time, so updating knowledge only requires updating documents. Fine tuning changes the model itself, which makes it stronger for behavior, tone and structured outputs.

Factor

RAG

Fine Tuning

Knowledge freshness

Updates instantly

Requires retraining

Output style control

Limited

Strong

Source traceability

High

Low

Inference latency

Slightly higher

Lower

Upfront cost

Lower

Higher

In practice, the strongest enterprise systems use both approaches together. A fine tuned model handles domain language and output structure, while a RAG layer supplies verified, up to date facts. We help you decide the right mix during the discovery phase.

Our LLM Fine Tuning

Process

A clear process keeps projects predictable and prevents costly rework later. Every stage ends with a defined output that your team reviews before we move forward. This is how we fine tune an LLM from idea to production.

Use Case Discovery

We define the task, success metrics and feasibility based on your available data.

Data Preparation

We collect, clean, deduplicate and label training data, and create held out evaluation sets.

Base Model Selection

We benchmark candidate models on your tasks to choose the best accuracy to cost ratio.

Fine Tuning and Training

We train the model using the selected method and track experiments for reproducibility.

Evaluation

We test accuracy, hallucination rate, latency and safety against agreed benchmarks.

Deployment and Monitoring

We deploy the model, monitor performance in production and retrain as data evolves.

Train Your LLM on Your Own Data, Securely

Data security is the most common concern we hear from enterprise buyers, and it shapes every architecture decision we make. When you train an LLM on your own data with KriraAI, training and inference can run inside your private cloud or on premise environment. Your proprietary data is never used to train shared or public models.

We apply encryption in transit and at rest, role based access control and detailed audit logging across the pipeline. Sensitive fields can be masked or anonymized before training to reduce privacy risk. Every engagement is covered by strict confidentiality agreements.

Models and Technology We Work With

We are model agnostic, which means we recommend the model that fits your task rather than a single vendor. Our team works with open source models including Llama, Mistral, Qwen and Gemma, as well as proprietary models from OpenAI, Anthropic and Google. For training and deployment, we use Hugging Face, PyTorch, DeepSpeed, vLLM, LangChain and leading vector databases.

Industries We Build LLMs For

KriraAI delivers AI solutions to enterprise clients across more than 22 industry verticals. Our LLM work spans healthcare, finance, insurance, legal, retail, manufacturing, logistics and customer service. Each industry brings its own terminology and regulatory requirements, and we design training data and evaluation around them.

Why Choose KriraAI as Your LLM Development Company

We focus on production grade systems that keep working after launch, not proof of concept demos. Our team owns the full lifecycle, from strategy and data engineering to deployment and ongoing optimization, so you work with one accountable partner. Our India based delivery model also gives global enterprises access to senior AI engineering talent at a competitive cost.

Every model we deliver is benchmarked, documented and handed over with clear ownership of weights and code. We set realistic expectations during discovery and tell you when a simpler solution will work better. That honesty is why enterprises trust us with their most sensitive AI initiatives.

Ready to Build Your Custom LLM?

Tell us about your use case and data, and our team will assess feasibility and recommend the right approach. You will receive a clear plan covering model choice, fine tuning method, timeline and infrastructure requirements. Book a consultation with KriraAI to start building an LLM that truly understands your business.

Frequently Asked

Questions

LLM fine tuning is the process of further training a pre trained large language model on a smaller, domain specific dataset so it performs a particular task more accurately. It teaches the model your terminology, formats and decision patterns. Fine tuning is faster and far cheaper than training a model from scratch, which makes it the preferred approach for most enterprise use cases.

LLM fine tuning cost depends on model size, fine tuning method, dataset preparation effort and hosting requirements. Parameter efficient methods such as LoRA cost much less than full parameter fine tuning because they need less GPU compute. Data preparation is often the largest cost component. KriraAI provides a detailed estimate after a short discovery session.

A typical LLM fine tuning project takes four to ten weeks from discovery to deployment. The training run itself may take only hours or days, while data preparation and evaluation take most of the time. Projects with clean, labeled data move faster. Complex custom LLM development with multiple tasks can take longer.

Many enterprise tasks can be fine tuned effectively with a few thousand high quality examples. Data quality matters more than volume, so clean, consistent and representative examples produce better results than large noisy datasets. Deeper domain adaptation may require larger datasets. We assess your existing data during discovery and can help generate or label additional examples.

RAG retrieves relevant information from external documents at query time, while fine tuning changes the model’s internal weights through additional training. RAG is best for frequently changing knowledge and source traceability. Fine tuning is best for consistent style, structured outputs and specialized reasoning. Many enterprise systems combine both for higher accuracy.

LoRA, or Low Rank Adaptation, is a parameter efficient fine tuning method that trains small adapter layers instead of updating every weight in the model. It significantly reduces GPU memory and training cost while delivering accuracy close to full fine tuning for most tasks. Adapters are small files, so multiple task specific versions can share one base model.

Yes, KriraAI builds private LLMs that run entirely inside your cloud account or on premise infrastructure. Your data and model weights stay within your environment, and no information is shared with third party model providers. We optimize inference with quantization and efficient serving so private deployment remains fast and cost effective.

The best base model depends on your task, language requirements, latency targets and deployment constraints. Open source models such as Llama, Mistral and Qwen suit private deployments and fine tuning, while proprietary models suit rapid prototyping. KriraAI benchmarks several candidate models on your actual data before recommending one.

Yes, your data is protected through encryption, role based access control, audit logging and strict confidentiality agreements. Training can run entirely inside your own environment, and sensitive fields can be anonymized before training. Your proprietary data is never used to train shared or public models, and you retain full ownership of the resulting model.

Yes, KriraAI provides ongoing monitoring, maintenance and retraining after deployment. We track accuracy, latency and drift in production and retrain the model as your data and business needs change. Support plans can include performance reports, model upgrades and integration of new use cases as your AI adoption grows.