Large Language Models

Build Intelligent Applications With Large Language Models

Build language-driven applications that understand context, generate responses, work with business knowledge and connect with real-world systems.

Web30 India develops LLM-based applications around foundation models, prompt and context engineering, RAG, model customization, inference infrastructure, AI agents and enterprise integrations.

Build With LLMs

Built on LLM

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LLM TECHNOLOGY ECOSYSTEM

Explore the LLM Technology Ecosystem

Large language model applications can be built using hosted foundation models, open-weight models or customized models, depending on application requirements such as capability, latency, cost, privacy and deployment environment. Modern platforms also provide model customization, RAG, evaluation and model routing capabilities.

More Than an AI Model

Build the Engineering Layer Around Your LLM

An LLM provides language understanding and generation capabilities, but a production application needs much more around it. Context management, retrieval, prompts, data access, tool integrations, evaluation, security and inference infrastructure all influence the quality of the final system

Web30 India builds LLM applications around the complete application architecture, selecting the right model and connecting it with business knowledge, workflows and enterprise systems.

For knowledge-intensive applications, retrieval can provide proprietary or current information as context to the model. RAG architectures typically combine document processing, embeddings, retrieval and generation to ground model responses in relevant information.

Finance Technology Ecosystem Illustration

LLM Development Capabilities

Engineer the Language Intelligence Behind Your Product

LLM Application Development

Build applications that use language models for conversational experiences, content generation, summarization, analysis and knowledge workflows.

Foundation Model Integration

Integrate commercial and open models through APIs, model gateways and application-specific inference layers.

LLM Customization

Customize supported models for specialized tasks using approaches such as supervised fine-tuning and other model customization techniques.

RAG Development

Connect LLMs with enterprise documents, databases and knowledge bases through retrieval pipelines that provide relevant context during generation.

Prompt & Context Engineering

Design structured prompts, system instructions, context windows and response formats around specific application requirements.

LLM Agent Development

Connect language models with approved tools, APIs, retrieval systems and business workflows to support multi-step tasks.

LLM Inference Engineering

Design model-serving and inference architectures around latency, throughput, scalability, availability and operating cost.

LLM Evaluation

Evaluate model responses for groundedness, relevance, completeness, correctness, safety and application-specific performance.

LLM-Powered Solutions

Put Language Models to Work Across Your Business

Enterprise AI Assistants

Build internal assistants that work with company knowledge, policies, documents and approved business systems.

Knowledge & Research Platforms

Create conversational knowledge systems that retrieve, organize and summarize information from large collections of business data.

AI Customer Support

Develop LLM-powered support applications that combine conversational intelligence with knowledge bases, customer information and business workflows.

Document Intelligence

Use LLMs to extract, classify, summarize and transform information from contracts, reports, forms and other business documents.

AI Copilots

Build contextual assistants inside existing software products to help users search, analyze, create and complete tasks.

LLM Search Applications

Combine semantic retrieval and language generation to create conversational search and knowledge discovery experiences.

Domain-Specific LLM Applications

Build language applications tailored to specialized terminology, workflows, documents and business requirements.

LLM-Powered Automation

Connect LLMs with APIs and business workflows to automate language-heavy operational tasks.

LLM Architecture

Experience Layer

Application Layer

Retrieval & Knowledge Layer

LLM Layer

Tool & Integration Layer

Evaluation & Observability Layer

Infrastructure Layer

Experience Layer

Web applications
Mobile applications
Chat interfaces
Voice interfaces
Embedded copilots
API consumers

Application Layer

Conversation management
Business logic
Workflow orchestration
Prompt management
Response Processing
User Context

Retrieval & Knowledge Layer

Document processing
Embeddings
Vector search
Hybrid search
Metadata filtering
Knowledge bases

LLM Layer

Foundation models
Open-Weight Models
Fine-Tuned Models
Embedding models
Model Routing

Tool & Integration Layer

Business APIs
Databases
CRM
ERP
Search Systems
External services

Evaluation & Observability Layer

Response Evaluation
Groundedness
Relevance
Correctness
Latency
Token Usage
Cost Monitoring

Infrastructure Layer

Model Endpoints
GPU infrastructure
Containers
Databases
Caching
Logging
Monitoring

Retrieval-Augmented Generation

Ground Your LLM With the Right Information

LLMs have broad pretrained knowledge, but enterprise applications often need access to private, changing or domain-specific information. RAG retrieves relevant information from an external knowledge source and provides it to the model as context.

Data Ingestion

Connect documents, databases, knowledge bases and approved information sources.

Content Processing

Clean, normalize, classify and divide source information into retrieval-ready content.

Embedding Generation

Create numerical representations that allow content to be searched based on semantic similarity.

Retrieval

Use vector, keyword or hybrid search to identify relevant information for each request.

Context Assembly

Combine retrieved information with system instructions, conversation history and application rules.

LLM Generation

Send the structured context to the selected language model to generate the response.

Response Evaluation

Measure groundedness, relevance, completeness and correctness to improve the retrieval and generation pipeline.

Model Customization

Adapt the Model When Prompting Alone Is Not Enough

Not every LLM application needs model training. Some requirements can be addressed through prompts, retrieval and application logic. When a model needs more specialized behavior, supported customization methods can include supervised fine-tuning and other training approaches.

Domain Adaptation

Adapt supported models toward specialized terminology, domain language and industry-specific requirements.

Instruction Fine-Tuning

Train models using examples that demonstrate the desired input and output behavior.

Task Optimization

Optimize model behavior for specific application tasks such as classification, structured generation or response formatting.

Response Style

Improve consistency around tone, structure, formatting and application-specific response patterns.

Model Distillation

Use model customization and distillation approaches where a smaller model is appropriate for a particular production workload.

Evaluation Before Deployment

Compare the customized model against a defined evaluation set before introducing it into production.

LLM Security

Protect Data, Prompts, Models and AI Workflows

LLM security extends beyond the model itself. Applications need controls around data access, model inputs, generated responses, tool permissions and operational monitoring.

01 // DATA CONTROLS

Data Access Controls

Restrict which users and AI workflows can access specific enterprise information.

02 // PROTECTION

Prompt Protection

Establish controls against malicious instructions, prompt injection and unintended context exposure.

03 // MODEL ACCESS

Model Access

Protect model credentials, API keys, endpoints and inference permissions.

04 // VALIDATION

Output Controls

Validate generated responses before they are displayed, stored or used in downstream workflows.

05 // PERMISSIONS

Tool Permissions

Control which APIs, databases and business functions an LLM can access.

06 // AUDITABILITY

Monitoring & Auditability

Track requests, model usage, retrieval activity, tool calls, errors and relevant operational events.

LLM Use Cases

Where Large Language Models Fit

Enterprise Knowledge

Give employees conversational access to approved internal knowledge and documentation.

Customer Support

Automate information-heavy customer conversations while connecting responses to approved knowledge sources.

Document Processing

Extract, summarize, classify and transform information from large volumes of business documents.

Software Engineering

Support code generation, documentation, testing, debugging and developer workflows.

Research & Analysis

Retrieve, compare, summarize and organize information for research-intensive processes.

Content Operations

Assist with content creation, editing, summarization, translation and transformation.

Professional Copilots

Embed contextual language assistance into applications used by employees and professional teams.

Business Workflow Automation

Connect LLMs with APIs and business systems to automate language-driven operational processes.

LLM Integrations

Connect Language Intelligence With Existing Systems

Enterprise Data

Databases, data warehouses, document repositories and internal knowledge systems.

Business Applications

CRM, ERP, HRMS, support, finance and operational platforms.

Search Infrastructure

Vector databases, search engines, embedding services and enterprise retrieval systems.

Integration Core

LLM Integrations

Communication Platforms

Email, collaboration tools, messaging platforms and customer communication channels.

APIs & Tools

Internal APIs, third-party services and controlled business functions.

AI Platforms

OpenAI, Azure AI, Google Vertex AI, Amazon Bedrock and other model infrastructure.

From User Input to AI Response

A Controlled Path From Question to Action

User Input

A user submits a question, instruction, document, image, audio input or other supported information.

Context & Intent Analysis

The application identifies the task, relevant context and required workflow.

Retrieval & Tool Selection

The system retrieves relevant knowledge or determines which approved tools and APIs are required.

Prompt & Context Assembly

The application combines user input, retrieved information, system instructions and business rules.

Model Generation

The selected generative model processes the request and produces the required output.

Validation & Response

The application evaluates the response, applies required controls and returns the result or triggers the next workflow step.

Our Generative AI Development Approach

From AI Use Case to Production Application

01 // DISCOVERY

Use Case & Data Discovery

Identify the business problem, users, expected outcomes, available data and appropriate role for generative AI.

02 // SELECTION

Model & Architecture Selection

Evaluate models, modalities, hosting options, retrieval requirements, integrations, latency and cost considerations.

03 // ARCHITECTURE

Knowledge & Prompt Architecture

Design prompts, context handling, data pipelines, retrieval strategies and application-specific instructions.

04 // DEVELOPMENT

Application Development

Build the user experience, orchestration layer, model integrations, retrieval system, APIs and business workflows.

05 // VALIDATION

Evaluation & Security Validation

Test response quality, groundedness, relevance, correctness, safety, performance and failure scenarios.

06 // DEPLOYMENT

Enterprise Integration

Connect the AI application with business systems, data sources, identity providers and approved tools.

07 // OPTIMIZATION

Production Deployment

Deploy the application, model infrastructure and supporting services with appropriate monitoring and operational controls.

08 // EXPANSION

Continuous Optimization

Improve prompts, retrieval, model selection, evaluation datasets, latency, cost and application performance as usage evolves.

OpenAI

Google Gemini

Anthropic Claude

Meta Llama

Mistral

Amazon Nova

Microsoft Azure AI

Hugging Face

LangChain

LlamaIndex

PyTorch

Python

TypeScript

JavaScript

Node.js

React

Next.js

PostgresSQL

Pinecone

Redis

Elasticsearch

AWS

Microsoft Azure

Google Cloud

Docker

Kubernetes