LLM Development Services for Enterprise AI Built for Production

KriraAI is an LLM development company delivering custom LLM development, fine-tuning on your own data and private deployment on your servers or cloud. Your model speaks your language, and your data stays with you. We serve enterprise clients across 22+ industries and build systems that go live and stay reliable, not demos that sit in a notebook.

22+

industries served

100+

AI systems running in production

99.9%

average accuracy gain after fine-tuning

Our LLM Development Services

Every business needs something different from an LLM. Some need advice, some need a model trained on their data. Here is what we build and what you get.

LLM Consulting Services

LLM consulting services help you decide if an LLM is the right fix before you spend money on it. We look at your use case, your data and your budget, then tell you which model, which approach and what it will cost to run each month. This suits leaders who have a long list of AI ideas but no clear first step. You walk away with a written plan, a shortlist of models and a simple return on investment estimate you can take to your board.

Custom LLM Development

Custom LLM development means building a language model system around your exact workflow instead of forcing your team to work around a generic chatbot. We pick the right base model for the job, whether that is GPT, Claude, Llama, Mistral or Qwen, and build the prompts, retrieval, guardrails and APIs around it. This is for companies whose tasks are too specific for off-the-shelf tools. The result is a model that answers in your format, follows your rules and plugs straight into your stack.

LLM Fine-Tuning Services

Our LLM fine-tuning services teach an existing model your tone, your terms and your output format using your own examples. We fine-tune open models like Llama, Mistral and Qwen with efficient methods such as LoRA, and use hosted fine-tuning for GPT when that fits better. Teams come to an LLM fine-tuning company like us when prompting alone keeps giving uneven answers. You get a smaller, faster model that performs better on your task and often costs less per request to run.

LLM Model Training Services

LLM model training goes a step deeper than fine-tuning. Our LLM training services cover continued pre-training on large volumes of your text, and full training from scratch when your language or data is truly unique, such as a regional language, a rare technical field or proprietary code. We build the data pipeline, run training on GPU clusters and track every experiment so results can be repeated. This is for businesses with large private datasets and a long-term AI plan. You end up owning a model no competitor can copy.

Domain-Specific LLM Development

A domain-specific LLM is trained to understand one field very well, such as insurance claims, pharma regulations or machine maintenance logs. General models guess when they see industry terms, and that guesswork is where most errors come from. We combine domain data, expert review and targeted test sets so the model learns what a correct answer looks like in your field. This suits regulated and technical industries where a wrong answer has a real cost. The result is fewer errors and answers your experts actually trust.

LLM Application Development

LLM application development turns a model into a product people use every day. We build internal assistants, document review tools, support copilots, search across company knowledge and AI agents that complete multi-step tasks on their own. Each app ships with a clean interface, user roles, usage tracking and cost controls from day one. This is for teams that want working software, not just an API key and a prompt. You get an app your staff can open on Monday morning and use without weeks of training.

LLM Integration Services

LLM integration services connect language models to the systems you already run, such as your CRM, ERP, helpdesk, document store or data warehouse. We build secure APIs, handle authentication, set up retrieval from your databases and keep response times low enough for live use. This suits businesses that want AI inside existing workflows rather than in a separate tool nobody opens. The result is AI that works where your team already works, with full logs of what it read and what it said.

Fine-Tuning vs RAG vs Training From Scratch

Use RAG when your knowledge changes often, fine-tuning when you need a fixed tone, format or domain language, and training from scratch only when no existing model understands your language or data. Most business projects start with RAG, add fine-tuning later, and never need full training. The table below shows how the three approaches compare on the factors that matter most.

Factor

Fine-Tuning

RAG

Training From Scratch

Best for

Tone, format, domain language

Changing knowledge

Unique language or data

Data needed

Hundreds to thousands of examples

Your documents

Very large corpus

Updating knowledge

Retraining needed

Update the index

Retraining needed

Cost

Medium

Low to medium

Very high

Time to production

Weeks

Months

Choose fine-tuning if your model knows the facts but keeps answering in the wrong style, length or format. Choose RAG if your answers depend on policies, prices or documents that change every week. Choose training from scratch if you work in a language or data type that no public model handles well. In practice, the strongest systems often mix two approaches. RAG supplies the latest facts, and a fine-tuned model decides how those facts are written. We test the options on a sample of your real data before you commit any budget to one path.

Private LLM Development and Data Security

Your data is never used to train public models. That is the first line in every KriraAI contract, and it is the reason many of our clients choose private LLM development over public AI APIs. We deploy open models on your own servers or inside your private cloud account on AWS, Azure or Google Cloud, so prompts, documents and outputs never leave your network. Security is built into the design from the first week, not added after a procurement review asks for it. We also map where each piece of data flows, who can see it and how long it is kept, so your security team can review the full setup before launch.

On-premise and private cloud deployment

so the model runs inside infrastructure you control.

Role-based access control

so each user only sees the data and features their role allows.

Encryption in transit and at rest

for training data, stored documents and model outputs.

Full audit logs

that record every prompt, retrieved source and response for review.

Compliance support

for ISO 27001, GDPR, HIPAA and India's DPDP Act, including data residency inside India when required.

Our LLM Development Process

Discovery
Data Preparation
Training and Fine-Tuning
Evaluation
Deployment

Our process has five stages. Each one ends with something you can sign off. This keeps budgets predictable and weak models away from users.

Our LLM Development

Process

Discovery and Feasibility

We start by defining the exact task, the users and what a correct answer looks like. We check your data, test two or three models on real samples and estimate monthly running costs. The deliverable is a feasibility report with the recommended approach, a success benchmark and a fixed project plan.

Data Preparation and Annotation

Most LLM projects succeed or fail at this stage. We collect and clean your documents, remove personal data, then label training examples with your domain experts so the model learns from correct answers. The deliverable is a clean, versioned dataset plus a separate test set held back for honest evaluation.

Training and Fine-Tuning

We run fine-tuning or training on the chosen base model, testing different settings and keeping a record of every run. Where RAG is part of the design, we build and tune the retrieval layer at the same time. The deliverable is a trained model version with training reports and side-by-side comparison scores.

Evaluation and Hallucination Control

We test the model against your benchmark before anyone uses it. That includes accuracy scoring, hallucination checks against source documents, bias and safety tests, and red teaming with tricky prompts. The deliverable is an evaluation report showing where the model is strong, where it fails and which guardrails are in place.

Deployment and Integration

We deploy to your cloud, your servers or a managed setup, connect the model to your systems and set up monitoring for quality, speed and cost. Your team gets full documentation and a handover session. The deliverable is a live system with dashboards, alerts and a clear plan for regular retraining.

Domain-Specific LLM Use Cases by Industry

An LLM earns its cost only when it solves a real problem. Here is what we build by sector. Each project is tied to one clear number.

1 of 5

Manufacturing

Plants use LLMs to search years of maintenance logs and equipment manuals in plain language, and to draft root cause reports after a machine breaks down. Technicians on the floor get answers in seconds instead of waiting for a senior engineer to call back. Measured result: faster fault diagnosis on the shop floor.

Legal and Compliance

Legal teams use LLMs to compare contracts against a standard playbook and flag risky clauses, and to track new regulations and map them to internal policies. Lawyers review every flagged item, so the model speeds up the work without making final calls. Measured result: cut in first-pass contract review time.

Customer Support

Support teams use LLMs to draft replies based on past tickets and help articles, and to sort and route new tickets by urgency and topic. The model hands the conversation to a human agent, with full context, whenever its confidence is low. Measured result: of tier-one tickets resolved without human help.

Banking and Financial Services

Banks use our LLMs to read loan files and pull out key terms for credit teams, and to answer customer product questions using approved policy wording aligned with RBI guidelines. Every answer links back to its source document so auditors can check it. Measured result: [X]% less time spent on manual loan file review.

Healthcare

Hospitals use domain-specific LLMs to turn doctor notes into structured discharge summaries, and to help coding teams assign billing codes from clinical records. Models run in a private setup, so patient data stays inside the hospital network at all times and meets HIPAA rules. Measured result: minutes saved per discharge summary.

Manufacturing

Legal and Compliance

Customer Support

Banking and Financial Services

Healthcare

Manufacturing

Why Choose KriraAI as Your Custom LLM Development Company

Experience across 22+ industries

We have delivered AI systems from manufacturing to healthcare, so you spend less time explaining your business to us.

Frequently Asked

Questions

The cost of custom LLM development depends mostly on the approach, with RAG the cheapest, fine-tuning in the middle and training from scratch the most expensive. Other big factors are how much data needs cleaning, how many systems the model must connect to and where it will be hosted. We give a fixed quote after a short discovery phase, so you know the full cost before any build work begins.

Most fine-tuning and RAG projects reach production in a matter of weeks, while training a model from scratch takes several months. The longest step is often data preparation, not the training itself. Starting with a pilot on one clear use case is the fastest way to see real value early.

Use RAG if your model needs up-to-date facts, and fine-tune if it needs to follow a specific style, format or domain language. RAG is cheaper to keep current because you only update the documents, not the model. Many strong systems use both, with RAG supplying the facts and a fine-tuned model shaping how answers are written.

For most tasks, a few hundred to a few thousand high-quality examples are enough to fine-tune an LLM. Quality matters far more than volume, so a small set of clean, expert-checked examples usually beats a huge pile of messy ones. If you have very little data, we can create synthetic examples and have your experts review them before training.

Yes, we can deploy an open model like Llama, Mistral or Qwen fully on your own servers or private cloud. Nothing leaves your network, and no data is sent to outside AI providers. We size the GPU hardware for your traffic and set up access control, encryption and audit logs as part of the build.

Choose an LLM development company that can show models running in production and explain exactly how it measures accuracy. Ask about its evaluation method, who owns the model and data after the project, and how it controls hallucinations. A good partner will also tell you honestly when you do not need a custom LLM at all.

LLM Development Services for Enterprise AI Built for Production

Our LLM Development Services

LLM Consulting Services

Custom LLM Development

LLM Fine-Tuning Services

LLM Model Training Services

Domain-Specific LLM Development

LLM Application Development

LLM Integration Services

Fine-Tuning vs RAG vs Training From Scratch

Private LLM Development and Data Security

On-premise and private cloud deployment

Role-based access control

Encryption in transit and at rest

Full audit logs

Compliance support

Our LLM Development Process