Which AI model do you build on?

We are model-agnostic and benchmark for your specific task. Claude, GPT and Gemini differ meaningfully on long-context handling, latency and cost per token, and the right pick changes by workload — so we test rather than default.

How do you stop the AI making things up?

We ground answers in your own content through retrieval, constrain output formats, and run an evaluation set before launch. Where a wrong answer would be costly, we add a confidence threshold that routes to a human instead of guessing.

How much does LLM app development cost in India?

Cost depends on scope: a focused RAG chatbot over your documents is far lighter than a fine-tuned, multi-source production system. We scope your use case, model choice, and data volume, then quote a fixed milestone-based budget. Contact Avani Enterprises at +91 84487 63134 for an estimate.

How long does it take to build an LLM application?

A working RAG prototype on your data can be ready in a few weeks. Production-grade LLM apps with fine-tuning, evaluation pipelines, and deployment typically take longer. We work in milestones so you see and test progress early.

What is your LLM development process?

We start with use-case scoping and model selection, build a RAG or fine-tuned prototype, create an evaluation suite to measure accuracy and faithfulness, then harden the app with guardrails, caching, and monitoring for production deployment.

What is the difference between RAG and fine-tuning?

RAG retrieves your live documents at query time so answers stay grounded and current without retraining. Fine-tuning adapts the model itself to your domain, tone, or format. We often combine both, and recommend the right mix for your accuracy, privacy, and cost goals.

Do you support and maintain LLM apps after launch?

Yes. We provide 24/7 monitoring, prompt and model updates, ongoing evaluation against regressions, cost optimisation, and feature enhancements so your LLM app stays accurate and reliable as your data and needs evolve.

Do you build LLM solutions for companies in India and the Gulf?

Yes. Avani Enterprises is headquartered at Unitech Cyber Park, Sector 39, Gurugram, and has served clients across India and the Gulf across India, the Gulf, and international markets for 8+ years, delivering LLM and AI solutions remotely and on-site.

LLM Development Company in India

AI Quick Summary

Avani Enterprises provides AI development from its offices in Gurugram and Rohtak, delivering across India and internationally.
It is aimed at teams adding AI to a product rather than buying an off-the-shelf tool.
What is delivered: custom ai features built into your existing product, retrieval-augmented generation over your own documents and model selection and cost/latency benchmarking.
Built with Anthropic Claude, OpenAI GPT, Google Gemini and Vector databases.
Typical timeline: typically 3–10 weeks.
Pricing: fixed-scope for a defined feature, retainer for continuous AI work.

What an AI Development engagement with us includes

What you get

Custom AI features built into your existing product
Retrieval-augmented generation over your own documents
Model selection and cost/latency benchmarking
Evaluation sets and guardrails before launch
Monitoring, logging and ongoing tuning

How we run it

Use-case scoping and feasibility
Data preparation
Build and evaluate
Guardrails and red-teaming
Deploy and monitor

Tools and stack

Anthropic Claude
OpenAI GPT
Google Gemini
Vector databases
Python
Node.js

Typical timeline: Typically 3–10 weeks
How we price it: Fixed-scope for a defined feature, retainer for continuous AI work

How a ai development engagement actually runs

The sequence is use-case scoping and feasibility, data preparation, build and evaluate, guardrails and red-teaming and deploy and monitor. Each stage ends with something you can look at rather than a status update — a scope document, a design, a staging link — so progress is visible instead of reported.

Typically 3–10 weeks. That range is wide because scope drives it: the difference between the low and high end is usually the number of integrations and how much of the content already exists. We narrow it in the scoping call rather than quoting a midpoint and revising later.

What we build it, and why that matters to you

We work with Anthropic Claude, OpenAI GPT, Google Gemini, Vector databases, Python and Node.js. The specific tools matter less than two things you should insist on from any supplier: that you own the accounts and the code at the end, and that nothing is built on a platform only that supplier can maintain.

You receive the repository and the deployment configuration on handover, so changing supplier later is a commercial decision rather than a technical trap.

When we are not the right choice

Fixed-scope for a defined feature, retainer for continuous AI work. If your budget is well below that, a smaller supplier or an off-the-shelf product will serve you better, and we would rather say so on the first call than three weeks in.

We are also the wrong choice if you need a single discipline delivered at the deepest possible level and nothing else — a dedicated specialist will usually beat a full-service team on one narrow axis. Where we are strong is when the work crosses boundaries: when the campaign needs the site rebuilt, or the AI needs the data pipeline fixed first.

Key capabilities

Grounded, Not Guessing: Our RAG architecture keeps answers anchored to your real documents and data, cutting hallucinations and giving every response a traceable source.
Evaluation Before You Trust It: We do not ship on vibes. Every LLM app ships with a test suite measuring accuracy, faithfulness, and latency so you know it works before users do.
Built for Production, Not Demos: We engineer for cost, speed, and reliability with caching, guardrails, and monitoring, so your LLM app survives real traffic, not just a polished walkthrough.
RAG Development: Retrieval-augmented generation over your PDFs, wikis, and databases, with vector search, chunking, and re-ranking tuned for accurate, cited answers.
LLM Fine-Tuning: Fine-tune and adapt open or hosted models on your data and tone, so outputs match your domain, format, and brand voice consistently.
Evaluation Pipelines: Automated eval suites scoring faithfulness, relevance, and regression on every change, so quality is measured, not assumed.
Production Deployment: Secure APIs, prompt versioning, cost controls, caching, observability, and guardrails so your LLM app runs reliably 24/7.

From RAG Prototype to a Production LLM App

Most LLM projects stall after an impressive demo. The gap is engineering: chunking strategy, retrieval quality, prompt design, evaluation, latency, and cost all decide whether an app is usable in production. As an LLM development company, we close that gap by building RAG systems that retrieve the right context from your knowledge base and return grounded, cited answers your team can trust.

We architect each layer deliberately — the vector store, embedding model, retrieval and re-ranking logic, prompt templates, and fallback behaviour — then wrap it in monitoring and guardrails. The result is an LLM app that handles real questions on real data, with sub-2-second response times and answers you can defend to a customer or auditor.

Fine-Tuning, Evaluation, and the Right Model Choice

Not every problem needs fine-tuning, and not every model fits every budget. We help you choose between prompting, RAG, and LLM fine-tuning based on your accuracy targets, data volume, privacy needs, and cost ceiling. When fine-tuning makes sense, we curate datasets, train and adapt the model, and benchmark it against your baseline so the gains are real and measurable.

Underneath it all sits evaluation. We build test sets from your actual use cases and score every model and prompt change for faithfulness, accuracy, and regression. This evaluation-first discipline is what lets us ship LLM applications confidently and keep improving them safely once they are live.

Frequently asked questions

Which AI model do you build on?: We are model-agnostic and benchmark for your specific task. Claude, GPT and Gemini differ meaningfully on long-context handling, latency and cost per token, and the right pick changes by workload — so we test rather than default.
How do you stop the AI making things up?: We ground answers in your own content through retrieval, constrain output formats, and run an evaluation set before launch. Where a wrong answer would be costly, we add a confidence threshold that routes to a human instead of guessing.
How much does LLM app development cost in India?: Cost depends on scope: a focused RAG chatbot over your documents is far lighter than a fine-tuned, multi-source production system. We scope your use case, model choice, and data volume, then quote a fixed milestone-based budget. Contact Avani Enterprises at +91 84487 63134 for an estimate.
How long does it take to build an LLM application?: A working RAG prototype on your data can be ready in a few weeks. Production-grade LLM apps with fine-tuning, evaluation pipelines, and deployment typically take longer. We work in milestones so you see and test progress early.
What is your LLM development process?: We start with use-case scoping and model selection, build a RAG or fine-tuned prototype, create an evaluation suite to measure accuracy and faithfulness, then harden the app with guardrails, caching, and monitoring for production deployment.
What is the difference between RAG and fine-tuning?: RAG retrieves your live documents at query time so answers stay grounded and current without retraining. Fine-tuning adapts the model itself to your domain, tone, or format. We often combine both, and recommend the right mix for your accuracy, privacy, and cost goals.
Do you support and maintain LLM apps after launch?: Yes. We provide 24/7 monitoring, prompt and model updates, ongoing evaluation against regressions, cost optimisation, and feature enhancements so your LLM app stays accurate and reliable as your data and needs evolve.
Do you build LLM solutions for companies in India and the Gulf?: Yes. Avani Enterprises is headquartered at Unitech Cyber Park, Sector 39, Gurugram, and has served clients across India and the Gulf across India, the Gulf, and international markets for 8+ years, delivering LLM and AI solutions remotely and on-site.