Generative AI development is the practice of building applications powered by large language models — chatbots, copilots, RAG assistants and autonomous AI agents — that understand language, reason over your private data and automate work. iMagic Solutions is a generative AI development company in India building production-grade systems on AWS Bedrock (AgentCore, Knowledge Bases, Claude, Nova, Llama), OpenAI and open-source models. We're an AI development company serving clients across India and the USA, with deep specialisation in RAG, AI agents and agentic AI workflows.
Generative AI in 2026 is no longer about impressive demos — it's about production systems that businesses actually run on. The hard problem isn't calling an LLM; it's building something accurate, secure, observable and reliable enough that the customer-success team, the support agents and the CFO all trust it. That's where iMagic comes in. We're a generative AI development company in India that ships AI to production — RAG assistants grounded in your data, AI agents that take multi-step actions, copilots embedded in existing products, and chatbots that resolve real tickets at scale.
We specialise in three categories: RAG (retrieval-augmented generation) for chat-with-your-documents and chat-with-your-database systems; AI agents for autonomous task execution; and AI copilots for in-app assistance. Each of these is a different engineering pattern with different cost, evaluation and observability needs — which is why most failed AI projects we're called in to rescue picked the wrong pattern up-front. We help you choose right, then build right, then scale right.
RAG development is now our most-requested service. The pattern is simple — ground LLM answers in your private data via vector search and retrieval, so the model doesn't hallucinate facts about your business. The execution is harder than it looks. Document quality, chunking strategy, embedding choice, hybrid search vs vector-only, re-ranking, citation surfacing, refresh cadence — every decision affects accuracy. We've shipped RAG systems over 10-document FAQs and 100,000-document enterprise corpora, on AWS Bedrock Knowledge Bases (managed), Pinecone, Weaviate, ChromaDB and AWS OpenSearch (custom). We pick the right one for your data volume, latency budget and ops capacity, not for vendor allegiance.
AI agents are the 2026 emerging vertical. An agent doesn't just answer — it acts. Books the appointment. Files the ticket. Updates the CRM record. Searches the web, synthesises results, drafts the report. We build AI agents on AWS Bedrock AgentCore for production observability and tracing, plus LangGraph and CrewAI for orchestration when AgentCore isn't the right fit. Every agent ships with human-in-the-loop guardrails, evaluation against a held-out test set, and structured logging so you can debug why it did what it did. We're a top AI agent development company in India for enterprises serious about agentic AI — not just experimental demos.
AI copilots are how you put generative AI inside the products your customers already use. We embed copilots into existing SaaS apps, CRMs, internal tools, mobile apps and marketplaces — for drafting, summarising, smart search, recommendations and 'do this for me' actions. Copilot development is mostly about the integration: how do you wire the LLM into the existing data model, the existing auth, the existing UI, without rebuilding the product? We've shipped copilots into healthcare EHRs, legal document review tools, fintech compliance workflows, e-commerce catalog management and SaaS analytics dashboards. The build is typically 6-12 weeks; the ROI is usually 3-10x within the first year.
We work model-agnostic. Claude (especially via AWS Bedrock for security and cost), OpenAI GPT, AWS Nova for cheap-and-fast, Llama 3.3 self-hosted for full data control, Mistral and Qwen for specialised tasks. We pick the right model for your accuracy, cost, latency and privacy requirements — and we use multiple models in the same chatbot when that's cheaper (route simple queries to Haiku/Nova, escalate complex queries to Sonnet/GPT-5). Every project starts with a 2-3 week fixed-scope proof-of-concept so you validate ROI on real data before committing to the full build.
We assess your data, workflows and goals to identify the highest-ROI AI use cases. Output: a written roadmap with prioritised use cases, target architecture, cost projections and compliance considerations — before any code is written.
Autonomous agents that execute multi-step tasks — research, customer support, data entry, internal-tool automation — with human-in-the-loop controls. Built on AWS Bedrock AgentCore, LangGraph or CrewAI depending on your needs. We're a top AI agent development company in India for enterprises moving past chatbots to agentic AI.
Retrieval-augmented generation systems — chat-with-your-documents, chat-with-your-database — grounded in your private data with vector search and citation surfacing. We're a top RAG development company in India, shipping on Bedrock Knowledge Bases (managed) or Pinecone/Weaviate/ChromaDB/OpenSearch (custom).
Embed an AI copilot inside your existing SaaS, mobile app, CRM or internal tool. Drafting, summarising, smart search, recommendations and 'do this for me' actions wired into the data model and auth you already have.
Production chatbots on web, mobile and WhatsApp. LLM-powered with proper RAG over your knowledge base, integration with CRM/helpdesk, and observability. Deflection rates of 50-80% typical for support; 20-30% lead-conversion lift for sales.
Production AI workloads on AWS Bedrock — agents (AgentCore), managed RAG (Knowledge Bases), model portability across Claude/Nova/Llama/Mistral. Deployed in Mumbai region for Indian clients (data residency), us-east-1 for USA clients.
Add smart search, summarisation, drafting and recommendations to apps you already run via OpenAI, Anthropic, Bedrock or self-hosted model APIs. Typically a 6-12 week build that ships measurable product wins without rebuilding the product.
Custom fine-tuned models when off-the-shelf isn't enough — domain-adapted Llama for specialised vocabularies, distilled smaller models for cost-efficient inference, RLHF for response-quality tuning.
Production-grade deployment with monitoring, automated evaluation against held-out test sets, prompt-version control, A/B testing, cost dashboards and structured logging. So you know when quality drifts — and why.
Prompt injection defence, PII redaction, content moderation, audit logging, role-based access control, deployment inside your own AWS account. SOC 2 / HIPAA / PCI-aligned designs for regulated industries.
We build AI that holds up in the real world — with evaluation, guardrails, monitoring and observability — not just a flashy prototype that breaks on the second user.
We've shipped RAG systems over 10-document FAQs to 100,000-document enterprise corpora, on Bedrock Knowledge Bases, Pinecone, Weaviate, ChromaDB and OpenSearch. We know which pattern fits your data.
We're a top AI agent development company in India for enterprise agentic workflows — Bedrock AgentCore for tracing, LangGraph for orchestration, human-in-the-loop guardrails, evaluation harnesses by default.
Claude, OpenAI, Bedrock Nova, Llama 3.3 self-hosted, Mistral, Qwen — we pick what fits your accuracy, cost, latency and privacy needs. Often multiple models routed in one app.
Built on AWS Bedrock for security, cost control and Mumbai-region data residency. Knowledge Bases for managed RAG, AgentCore for agent orchestration, Nova for cost-efficient inference.
Certified AWS Solution Architects on the same team mean your AI ships on infrastructure that's secure, scalable and cost-optimised from day one. No handoff. No 'cloud team blocked us' delays.
Enterprise API tiers that don't train on your data, RAG that keeps documents in your control, deployment inside your own AWS account for full data residency. SOC 2 / HIPAA / PCI-aligned designs available.
Every engagement starts with a 2-3 week fixed-scope PoC on real data — so you measure ROI before committing to the full build. No million-rupee leaps of faith.
A few of the things we deliver under ai & generative ai development:
Map use cases, data sources, success metrics and constraints. Output: a written roadmap with the right pattern (RAG vs agent vs copilot vs fine-tune) and a cost/timeline estimate — before any code is written.
Ship a fixed-scope proof-of-concept in 2-3 weeks on real data with a real model. The goal is to measure accuracy and ROI on the actual use case before committing to the full build.
Engineer the production system: RAG pipeline or agent orchestration, evaluation harness, guardrails, observability, integrations, UI and access control. Typical full build: 6-16 weeks depending on tier.
Automated evaluation against a held-out test set. Prompt-version control, A/B tests, cost-quality tradeoffs. Quality scores you can show your CFO before launch.
Deploy, monitor, optimise cost (route to cheaper models where possible), iterate on user feedback. Most clients move to an ongoing retainer once the system is live.
Yes. iMagic Solutions is an AI development company in India serving clients across India and the USA. We build production-grade generative AI — RAG assistants, AI agents, copilots and chatbots — on AWS Bedrock, OpenAI, Anthropic Claude and open models. Our team combines AWS Certified Solution Architects with senior LLM/RAG engineers, so AI ships on solid cloud infrastructure from day one.
Yes — RAG (retrieval-augmented generation) is one of our top three service areas. We're a top RAG development company in India, shipping production RAG systems on AWS Bedrock Knowledge Bases (managed), Pinecone, Weaviate, ChromaDB and OpenSearch (custom). We've delivered RAG over 10-document FAQs and 100,000-document enterprise corpora, with citation surfacing, hybrid search and refresh pipelines.
Yes — we're a top AI agent development company in India for enterprises moving past chatbots to agentic AI workflows. We build production agents on AWS Bedrock AgentCore (preferred for observability and tracing), LangGraph and CrewAI. Every agent ships with human-in-the-loop guardrails, evaluation against a held-out test set, structured logging and prompt-version control.
Generative AI development is building software powered by large language models — chatbots, copilots, RAG assistants and AI agents — that understand language, reason over your data and automate work. In 2026, generative AI has moved from demos to production systems that businesses depend on every day. iMagic builds these on AWS Bedrock, OpenAI, Claude and open-source models.
A chatbot answers questions. An AI agent takes actions — books the appointment, files the ticket, updates the CRM, runs the multi-step research. Agentic AI is the practice of building autonomous or semi-autonomous agents that complete tasks end-to-end, with human-in-the-loop checkpoints. It's the 2026 fast-growing AI vertical because the ROI per agent is much higher than per chatbot — agents displace operational work, not just inbound questions.
Yes — AWS Bedrock is our default platform for production AI in India. Bedrock gives you managed RAG (Knowledge Bases), agent orchestration (AgentCore), model portability across Claude, Nova, Llama, Mistral and Qwen, and deployment inside your own AWS account in the Mumbai region for data residency. We also work with OpenAI direct, Anthropic direct, and self-hosted open models when the use case calls for it.
Yes — AI integration is one of our most popular services. We embed AI copilots inside your existing SaaS, mobile app, CRM, internal tool or marketplace for drafting, summarisation, smart search, recommendations and 'do this for me' actions. Typical build: 6-12 weeks. We work with the data model and auth you already have rather than asking you to rebuild your product.
Most proofs-of-concept take 2 to 4 weeks. The goal is to validate a single high-value use case on real data with the actual model so you can measure ROI before committing to a full production build. We always recommend starting with a PoC — it eliminates the #1 way AI projects fail (committing to a Tier 5 build before knowing the use case works).
It depends on the task. We're model-agnostic and benchmark options against your specific accuracy, latency, cost and privacy requirements. Claude (especially via Bedrock) is our default for nuanced reasoning. GPT-4o/GPT-5 is the strongest general-purpose option. Bedrock Nova is the most cost-efficient. Llama 3.3 self-hosted gives full data control. We often combine multiple models in one chatbot — route simple queries to cheap models, escalate complex queries to expensive ones — for 40-60% cost savings without quality loss.
A focused proof-of-concept costs ₹2-5 lakh and takes 2-4 weeks. A production Tier 3 LLM chatbot costs ₹2.5-6 lakh. A Tier 5 enterprise AI assistant with RAG, agents and deep integrations costs ₹5.5-15 lakh+. AI development in India is typically 60-70% cheaper than equivalent US/UK work at the same quality level, because India has the world's largest pool of AI/ML engineers and the toolchain is global. See our detailed pricing guide at /blog/cost-to-build-an-ai-chatbot.
Hiring AI developers in India typically costs ₹1,500-3,500 per hour ($18-42) for senior LLM/RAG engineers — 4-10x cheaper than equivalent US or UK talent. We offer three engagement models: dedicated AI developers as an extended team (billed monthly), project-based (fixed scope and price), and hourly advisory. Most clients start with a project-based proof-of-concept then move to dedicated team or retainer.
Yes. We use enterprise API tiers (OpenAI, Anthropic, Bedrock) that do not train on your data, deploy RAG so your documents stay in your control, and can run everything inside your own AWS account for full data residency. SOC 2 / HIPAA / PCI-aligned designs are available for regulated industries (healthcare, fintech, BFSI). For maximum control we'll self-host open-source models (Llama 3.3) on dedicated AWS infrastructure with no external API calls.
Yes — WhatsApp AI chatbots are a popular service for Indian D2C, BFSI, healthcare and real-estate clients. We integrate WhatsApp Business API via BSPs like Gupshup, AiSensy or Twilio, then layer LLM-powered conversation, RAG over your knowledge base, and integration with your CRM. Build cost: ₹2-5.5 lakh. We handle Meta Business API approval and template message setup.
Yes. We've delivered AI for healthcare (clinical-decision-support, EHR summarisation, HIPAA-eligible workloads), fintech (PCI-DSS-aligned chatbots, AML triage, compliance copilots), BFSI (lending pre-qualification, customer service) and legal (contract review, document classification). Regulated industries get deployment inside your own AWS account, PII redaction, audit logging, role-based access control and compliance-aligned design patterns from day one.
LLM-powered, RAG-grounded chatbots for web, WhatsApp, Slack and Teams — from $3K rule-based FAQ bots to $150K+ enterprise AI assistants. USD pricing, US/EU/India delivery.
Autonomous AI agents that take actions — not just answer — built on AWS Bedrock AgentCore, LangGraph and CrewAI. From $15K single-action to $300K+ enterprise.
AWS-certified cloud architecture, migration, serverless, DevOps and FinOps cost optimisation — plus AWS Bedrock & generative AI on AWS.
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