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Generative AI

Build Intelligent Applications With Generative AI

Build applications that understand, generate and work with text, code, images, audio, video and business data using modern generative AI models.

Web30 India develops Generative AI solutions around foundation models, LLMs, multimodal models, RAG, model customization, AI agents, enterprise data and production-grade application infrastructure.

Build With Generative AI
Built on Generative AI
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GENERATIVE AI TECHNOLOGY ECOSYSTEM

Explore the Generative AI Ecosystem

Generative AI applications can combine foundation models with enterprise data, retrieval systems, tools, application APIs and evaluation layers. Modern platforms also support multimodal inputs such as text, images, audio and video.

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More Than a Foundation Model

Build the Application Layer Around Generative AI

A foundation model provides the intelligence, but production applications require much more around it. Data pipelines, retrieval, prompts, application logic, tool integrations, security, evaluation and monitoring all influence how useful and reliable the final system becomes.

Generative AI applications can be built using hosted models, open models, customized models or a combination of models selected according to capability, latency, cost, privacy and deployment requirements. Multimodal models can extend these applications beyond text to images, audio, video and other inputs.

Web30 India engineers the complete application layer around Generative AI, from model selection and knowledge grounding to user experiences, enterprise integrations, evaluation and production deployment.

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Generative AI Development Capabilities

Engineer the Intelligence Behind Your Product

01

Foundation Model Integration

Integrate hosted and open foundation models into applications through APIs, model gateways and application-specific orchestration.

02

Large Language Model Development

Build LLM-powered applications for conversational interfaces, content generation, summarization, reasoning, classification and knowledge workflows.

03

Multimodal AI Development

Build applications that work across text, images, documents, audio and video using models capable of handling multiple modalities.

04

Retrieval-Augmented Generation

Connect generative AI models with enterprise knowledge bases and external information through retrieval pipelines that provide relevant context to the model.

05

AI Model Customization

Customize model behavior using prompt engineering, supervised tuning, fine-tuning, distillation or other supported approaches when the application requires more specialized behavior.

06

AI Agent Development

Build applications where models can reason through tasks, retrieve information and interact with approved tools or business systems.

07

Generative AI API Integration

Connect AI models with enterprise applications, databases, internal APIs, business workflows and external services.

08

Generative AI Evaluation

Design evaluation datasets and test AI applications for relevance, groundedness, correctness, completeness, safety and task performance.

Solutions Built With Generative AI

Turn Generative Models Into Useful Products

01

Enterprise AI Assistants

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

02

AI Knowledge Platforms

Create conversational knowledge systems that retrieve relevant information from structured and unstructured business data.

03

Intelligent Document Applications

Extract, summarize, classify and transform information from documents, reports, contracts, forms and other business content.

04

AI Customer Support

Build conversational support experiences that combine generative responses with knowledge bases, customer information and business workflows.

05

Content Generation Platforms

Create systems for generating, transforming and reviewing marketing, editorial, product and business content.

06

AI-Powered Search

Build semantic and conversational search experiences that combine keyword, vector and generative retrieval.

07

Generative AI Copilots

Develop contextual copilots for software products, professional workflows, operations, analytics and knowledge-intensive teams.

08

Multimodal AI Applications

Build applications that understand and generate content across text, images, documents, audio and video.

Generative AI Architecture

Build the Layers Behind an AI Application

A production Generative AI system typically combines application interfaces, orchestration, retrieval, model inference, data infrastructure, integrations and evaluation rather than relying on the model alone. RAG architectures commonly use embeddings, retrieval and contextual generation to connect models with specific or proprietary information.

Generative AI Architecture
01

Experience Layer

02

Application & Orchestration Layer

03

Retrieval & Knowledge Layer

04

Model Layer

05

Tool & Integration Layer

06

Evaluation & Observability Layer

06

Infrastructure Layer

Experience Layer

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

Application & Orchestration Layer

  • Prompt management
  • Conversation management
  • Workflow orchestration
  • Tool routing
  • Business rules
  • Response handling

Retrieval & Knowledge Layer

  • Document processing
  • Chunking
  • Embeddings
  • Vector search
  • Hybrid search
  • Metadata filtering
  • Knowledge bases

Model Layer

  • Foundation models
  • Large language models
  • Multimodal models
  • Embedding models
  • Fine-tuned models
  • Open-source models

Tool & Integration Layer

  • Business APIs
  • Databases
  • Search systems
  • CRM
  • ERP
  • Enterprise applications
  • External services

Evaluation & Observability Layer

  • Quality evaluation
  • Groundedness
  • Relevance
  • Correctness
  • Latency
  • Token usage
  • Cost monitoring
  • Safety monitoring

Infrastructure Layer

  • Cloud infrastructure
  • Model endpoints
  • Containers
  • GPU infrastructure
  • Databases
  • Caching
  • Logging
  • Monitoring
Retrieval-Augmented Generation

Connect Generative AI With Your Business Knowledge

Generative AI becomes more useful for enterprise applications when models can access relevant and verifiable information instead of relying only on their pretrained knowledge. RAG retrieves relevant information and provides it to the model as context during generation.

01

Document Ingestion

Collect and process documents, knowledge articles, databases and other approved information sources.

02

Data Processing

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

03

Embedding Generation

Convert content into numerical representations that can be used for semantic retrieval.

04

Knowledge Retrieval

Use vector, keyword or hybrid search to identify information relevant to the user's request.

05

Context Assembly

Select and structure retrieved information before passing it to the generation model.

06

Grounded Generation

Generate a response using the retrieved context and application-specific instructions.

07

Evaluation & Feedback

Measure groundedness, relevance, completeness and correctness to continuously improve the system.

Generative AI Security

Build AI Systems With Controls Around Every Model Interaction

Generative AI introduces application-level risks alongside traditional software and infrastructure risks. Security architecture should therefore address data access, prompts, model interactions, tools, generated content and operational monitoring.

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100% Audited Smart Contracts
01 // DATA CONTROLS

Data Access Controls

Control which users, applications and AI workflows can access specific enterprise information.

02 // PROTECTION

Prompt & Input Protection

Validate inputs and establish controls against prompt injection, malicious instructions and unintended data exposure.

03 // CONTROLS

Model Access Controls

Manage model access, API credentials, usage policies and model routing.

04 // VALIDATION

Output Validation

Apply application rules and safety checks before generated content is returned or used downstream.

05 // SECURITY

Tool & API Security

Control which tools an AI application can call and what permissions those tools receive.

06 // AUDITABILITY

Monitoring & Auditability

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

Generative AI Use Cases

Where Generative AI Creates Practical Value

01

Enterprise Knowledge

Give employees conversational access to approved internal knowledge and documentation.

02

Customer Experience

Build AI assistants that answer questions, summarize information and support customer workflows.

03

Software Engineering

Use generative models for code generation, documentation, testing, debugging and developer assistance.

04

Document Intelligence

Transform large volumes of business documents into searchable, structured and actionable information.

05

Content Operations

Support content creation, editing, summarization, translation, classification and content transformation.

06

Research & Analysis

Build systems that retrieve, compare, summarize and organize information for research-heavy workflows.

07

Healthcare & Life Sciences

Support knowledge retrieval, documentation, research workflows and other controlled AI applications where appropriate safeguards are in place.

08

Financial Services

Build assistants, document workflows, research tools, customer support and operational applications around controlled financial data.

Generative AI Integrations

Connect AI With the Systems Your Business Already Uses

Enterprise Data Sources

01

Connect databases, data warehouses, document repositories, knowledge bases and internal content systems.

Business Applications

02

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

Communication Platforms

03

Connect email, chat, collaboration and customer communication systems.

Integration Core

Generative AI Integrations

Search & Retrieval Infrastructure

04

Integrate vector databases, search engines, embedding services and enterprise retrieval systems.

AI Model Platforms

05

Connect multiple model providers and model endpoints to support application-specific model selection.

External Tools & APIs

06

Allow controlled AI workflows to interact with approved business APIs, third-party services and operational tools.

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

Use Case & Data Discovery
01 // DISCOVERY

Use Case & Data Discovery

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

Model & Architecture Selection
02 // SELECTION

Model & Architecture Selection

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

Knowledge & Prompt Architecture
03 // ARCHITECTURE

Knowledge & Prompt Architecture

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

Application Development
04 // DEVELOPMENT

Application Development

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

Evaluation & Security Validation
05 // VALIDATION

Evaluation & Security Validation

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

Enterprise Integration
06 // DEPLOYMENT

Enterprise Integration

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

Production Deployment
07 // OPTIMIZATION

Production Deployment

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

Continuous Optimization
08 // EXPANSION

Continuous Optimization

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

OpenAI
OpenAI
Google Gemini
Google Gemini
Anthropic Claude
Anthropic Claude
Amazon Nova
Amazon Nova
Meta Llama
Meta Llama
Mistral
Mistral
Microsoft Azure AI
Microsoft Azure AI
Hugging Face
Hugging Face
LangChain
LangChain
LlamaIndex
LlamaIndex
PyTorch
PyTorch
TensorFlow
TensorFlow
TypeScript
TypeScript
JavaScript
JavaScript
Node.js
Node.js
React
React
Next.js
Next.js
PostgresSQL
PostgresSQL
Redis
Redis
Elasticsearch
Elasticsearch
AWS
AWS
Docker
Docker
Kubernetes
Kubernetes
Google Cloud
Google Cloud
Pinecone
Pinecone
Knowledge Hub & Insights

Explore Our Latest Blockchain & Enterprise Technology Insights

Explore practical insights on Generative AI architecture, LLM applications, RAG, multimodal AI, model customization, AI agents, evaluation, enterprise AI and production deployment.

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Generative AI FAQs

Everything you need to know about our enterprise blockchain engineering process, costs, security, and architectures.

Generative AI refers to AI systems that can create new content such as text, images, audio, video and code based on learned patterns and user inputs. Foundation models provide the underlying capabilities for many generative AI applications.
Traditional AI is often designed around specific prediction, classification or decision tasks. Generative AI focuses on producing new content or responses, although modern applications can combine both approaches.
A Large Language Model is a type of foundation model trained primarily on language data and designed to understand and generate natural language. LLMs are one category within the broader Generative AI ecosystem.
Retrieval-Augmented Generation connects a generative model with an external knowledge source. Relevant information is retrieved and supplied as context before the model generates its response
No. Many applications can be built using prompting, retrieval, tools and application-level controls. Fine-tuning or other model customization approaches may be appropriate when specialized behavior cannot be achieved effectively through those methods.
Yes. RAG and other grounding architectures can connect models with enterprise documents, databases and knowledge sources while allowing application-level access controls and retrieval policies to determine what information is available.
Yes. Multimodal models can process combinations of text, images, audio and video depending on the model and platform being used.
Evaluation can include groundedness, relevance, completeness, correctness, retrieval quality, safety, latency and cost. For RAG systems, evaluation should consider both retrieval and generated responses.
Yes. AI applications can be integrated with APIs, databases, enterprise systems and approved tools. Tool calling and function-based integrations can allow an AI application to retrieve information or perform controlled actions.
Yes. Applications can be designed around a single model provider or a model-agnostic architecture that supports multiple foundation models. Platforms such as Amazon Bedrock provide access to models from multiple providers through a managed infrastructure layer.
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