RAG Development Services in India
Build production RAG systems with Aimodels.in. We offer RAG architecture, vector database setup, pipeline optimization, multimodal RAG, and production deployment for Indian businesses.
Service Overview
Retrieval-augmented generation, or RAG, is the most reliable way to make large language models answer questions accurately from your own data without hallucination. Aimodels.in designs and deploys production-grade RAG systems that connect your documents, knowledge bases, and databases to LLMs via semantic search and vector databases. Our team has built RAG pipelines for legal research, customer support, internal knowledge portals, and multimodal document analysis, and we know the engineering details that separate a demo from a system that handles millions of queries reliably. We handle the full stack including document ingestion, chunking strategies, embedding model selection, vector database tuning, retrieval optimization, reranking, citation, and observability. Whether you are building a customer-facing chatbot that must cite sources, an internal assistant for employees to query policies, or a research tool for analyzing large document corpora, we tailor the architecture to your data volume, query patterns, accuracy requirements, and latency budget. Every engagement ends with a deployed, monitored, and documented RAG system your team can maintain and extend, plus evaluation suites that prove retrieval accuracy and answer faithfulness before and after go-live.
How We Work — Our Process
A structured, transparent engagement model that ensures delivery quality at every step.
Use Case & Data Analysis
We analyze your query patterns, document types, data volume, accuracy requirements, and latency budget to design the right RAG architecture and retrieval strategy.
Week 1Vector Database Setup
We select and configure the right vector database for your scale and query patterns, with indexing, sharding, and metadata filtering tuned for performance.
Week 2Ingestion & Chunking Pipeline
We build document ingestion pipelines with intelligent chunking, embedding generation, metadata extraction, and incremental update support for live data sources.
Weeks 3-4Retrieval & Reranking Optimization
We optimize retrieval with hybrid search, cross-encoder reranking, query expansion, and dynamic chunk selection to maximize answer accuracy and relevance.
Week 5Answer Generation & Citation
We engineer the generation layer with prompt templates, citation extraction, confidence scoring, and guardrails to ensure grounded, traceable answers.
Week 6Production Deployment & Monitoring
We deploy the RAG system with autoscaling, query logging, retrieval quality monitoring, and feedback loops for continuous improvement.
Weeks 7-8Why Choose Us
Our key differentiators that set us apart in the AI services landscape.
Hallucination-Free Answers
Our RAG architectures ground every answer in retrieved source documents with citations, reducing hallucination rates by up to 78% versus raw LLM generation.
Retrieval Accuracy Focus
We optimize every layer from chunking to reranking, measuring retrieval precision and recall on your data so answers cite the right sources every time.
Data Privacy & Access Control
We implement document-level access controls and metadata filtering so users only retrieve and receive answers from documents they are authorized to see.
Multi-Source Connectors
We connect RAG pipelines to PDFs, Confluence, SharePoint, Notion, databases, websites, and APIs with incremental sync for live, up-to-date knowledge.
Multimodal RAG Support
We build RAG systems that handle text, tables, images, and scanned documents, so your knowledge base is not limited to clean text files.
Cost-Efficient at Scale
We optimize embedding costs, vector search latency, and LLM token usage so your RAG system stays affordable even with millions of documents and queries.
What We Offer
Detailed breakdown of each offering within this service category.
RAG System Architecture
End-to-end architecture design covering embedding models, vector databases, retrieval strategies, reranking, generation, and observability tailored to your use case.
- Architecture design document with component rationale
- Retrieval strategy recommendation with benchmark projections
- Scalability and cost model for expected query volume
Vector Database Setup
Selection, configuration, and optimization of vector databases including Pinecone, Weaviate, Qdrant, Milvus, or pgvector for your scale and query patterns.
- Vector database deployment with indexing and sharding
- Metadata schema design for filtering and access control
- Performance benchmarking report with latency and throughput
RAG Pipeline Optimization
Optimization of chunking, embedding, retrieval, and reranking to maximize answer accuracy, reduce latency, and lower cost per query.
- Chunking strategy evaluation with semantic and structural options
- Hybrid retrieval and cross-encoder reranking implementation
- Retrieval accuracy benchmark with precision and recall metrics
Multimodal RAG
RAG systems that retrieve and reason over text, tables, images, and scanned documents using multimodal embeddings and vision-language models.
- Multimodal ingestion pipeline for text, images, and tables
- Vision-language model integration for image understanding
- Cross-modal retrieval evaluation suite
Production RAG Deployment
Production deployment with autoscaling APIs, query logging, retrieval quality monitoring, feedback collection, and CI/CD for continuous improvement.
- API service with streaming responses and citation metadata
- Monitoring dashboards for retrieval quality and latency
- Feedback loop pipeline for retraining and evaluation updates
Technology Stack
The tools, platforms, and frameworks we use to deliver this service.
| Pinecone | Managed vector database for semantic search | Expert |
|---|---|---|
| Qdrant | Open-source vector search engine | Expert |
| Weaviate | Hybrid vector and keyword search | Advanced |
| Milvus | Scalable vector database for large scale | Advanced |
| pgvector | PostgreSQL extension for embeddings | Expert |
| OpenAI Embeddings | Text and multimodal embeddings | Expert |
| Cohere Rerank | Cross-encoder reranking for retrieval | Expert |
| LangChain / LlamaIndex | RAG orchestration frameworks | Expert |
| Unstructured / LlamaParse | Document parsing and chunking | Advanced |
| FastAPI / Ray Serve | API serving and orchestration | Expert |
Use Cases & Industry Applications
Real-world scenarios where this service delivers measurable business impact.
Engagement Timeline & Impact Metrics
Project Timeline
| Phase | Duration | Key Deliverable |
|---|---|---|
| Analysis | Week 1 | Use case and architecture design |
| Vector DB | Week 2 | Configured and benchmarked vector store |
| Ingestion | Weeks 3-4 | Document pipeline with chunking |
| Optimization | Week 5 | Retrieval and reranking tuned |
| Deploy | Weeks 6-8 | Production API with monitoring |
Business Impact
| Metric | Before AI | After AI |
|---|---|---|
| Retrieval accuracy | 54% | 89% |
| Hallucination rate | 22% | 5% |
| Query latency (P95) | 2,400ms | 450ms |
| Citation coverage | 0% | 100% |
| Cost per 1,000 queries | ₹480 | ₹140 |
Our Capabilities
| Capability | Status |
|---|---|
| Hybrid retrieval | Available |
| Cross-encoder reranking | Available |
| Multimodal RAG | Available |
| Access control | Available |
| Citation extraction | Available |
| Live data sync | Available |
Pricing & Packages
Transparent pricing for every engagement size. All packages include post-delivery support.
| Tier | Price | Timeline | Includes |
|---|---|---|---|
| Starter | ₹79,000 | 4 weeks | RAG pipeline with single vector DB and basic retrieval for 1 data source |
| Growth | ₹1,99,000 | 8 weeks | Optimized RAG with reranking, multimodal, and 3 data source connectors |
| Enterprise | ₹4,49,000 | 12 weeks | Production RAG at scale with access control, monitoring, and CI/CD |
What Is Included
- Use case analysis and architecture design
- Vector database setup and optimization
- Document ingestion and chunking pipeline
- Embedding model selection and evaluation
- Hybrid retrieval and cross-encoder reranking
- Answer generation with citation extraction
- Production API with streaming and monitoring
- Retrieval quality evaluation suite
If your RAG system retrieval accuracy does not exceed 85% on your evaluation dataset, we will optimize retrieval and reranking at no additional cost until it does.
Book a Free Consultation
Speak with our AI experts about your specific requirements. We will assess your needs, recommend the right approach, and provide a detailed proposal within 48 hours.
Book Your Free Consultation →Frequently Asked Questions
What is the difference between RAG and fine-tuning a model?
Fine-tuning bakes knowledge into model weights, which is expensive to update and prone to hallucination. RAG retrieves relevant documents at query time and grounds the answer in them, making it easier to update knowledge, cite sources, and control accuracy. We often combine both, using fine-tuning for style and RAG for facts.
Which vector database should we use?
It depends on your scale, query patterns, budget, and whether you need managed or self-hosted. Pinecone is great for managed simplicity, Qdrant and Milvus for scale and control, and pgvector if you already use PostgreSQL. We benchmark options against your data and recommend the best fit.
How do you handle access control so users only see authorized documents?
We implement metadata filtering at the vector database level, tagging each document with access permissions and filtering queries by user identity. This ensures users only retrieve and receive answers from documents they are authorized to access, matching your existing permission system.
Can RAG work with scanned PDFs and images?
Yes. We use document parsing tools like LlamaParse and Unstructured for OCR and table extraction, and multimodal embeddings or vision-language models for image understanding. Our multimodal RAG offering handles text, tables, images, and scanned documents in a unified pipeline.
How do you measure RAG retrieval accuracy?
We build evaluation suites with ground-truth query-document pairs and measure retrieval precision, recall, and mean reciprocal rank. For answer quality, we measure faithfulness to sources, answer relevance, and citation accuracy using both automated metrics and human review.
What happens when our documents change or new ones are added?
We build incremental sync pipelines that detect new and updated documents, re-embed them, and update the vector store without full reindexing. For live sources like Confluence or databases, we configure scheduled or event-driven sync to keep the knowledge base current.
How much does it cost to run a RAG system in production?
Costs include embedding generation, vector database hosting, and LLM inference. For a system handling 100,000 queries per month over 500,000 documents, typical cloud costs range from ₹40,000 to ₹1,20,000 per month. We optimize each layer to minimize spend and provide a cost model during architecture design.
Can we use our own fine-tuned LLM with the RAG system?
Absolutely. Our RAG pipelines are model-agnostic and work with any LLM exposed via an OpenAI-compatible API, including custom fine-tuned models deployed with vLLM or TensorRT-LLM. We can integrate your model during the deployment phase.
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