RAG (retrieval-augmented generation) development is the engineering of AI systems that retrieve relevant context from your private data — documents, databases, internal APIs — and pass it to a large language model so answers stay grounded in your content, not the model's training data. iMagic Solutions is a RAG development company building production retrieval systems on AWS Bedrock Knowledge Bases (managed), Pinecone, Weaviate, ChromaDB and AWS OpenSearch (custom). From $8,000 small FAQ-RAG to $120,000+ enterprise corpora — USA, Europe, India delivery with SOC 2 / HIPAA / GDPR-ready design.
RAG is the production answer to LLM hallucination. A general LLM doesn't know your products, your pricing, your internal policies or your client contracts — and it will confidently invent answers when asked. RAG fixes this by retrieving the actual relevant content from your knowledge base, passing it to the LLM as grounded context, and instructing the model to cite sources. The result: an AI assistant that answers from YOUR data, cites which document the answer came from, and admits ignorance when no relevant content is found.
We build RAG systems across five complexity tiers by knowledge-base size: small (10–50 documents, $8K–$18K, 2–3 weeks); small-medium with metadata filtering (50–500 docs, $15K–$30K, 3–5 weeks); medium with hybrid search and re-ranking (500–5,000 docs, $25K–$55K, 5–8 weeks); large with refresh pipelines (5,000–50,000 docs, $45K–$90K, 8–12 weeks); enterprise with access control (50,000+ docs, $80K–$150K+, 10–16 weeks). The defining cost driver is data engineering — document preparation typically accounts for 30–50% of build cost. The LLM and vector database are secondary.
AWS Bedrock Knowledge Bases is our default for under 5,000 documents — it's fully managed (Bedrock handles chunking, embedding, indexing, retrieval) and integrates natively with AWS IAM, KMS and CloudTrail. For larger corpora or specialised retrieval patterns we use Pinecone (best for 5K–500K docs with simple metadata, 99.99% uptime SLA), Weaviate (hybrid search, BYOC, schema control), ChromaDB (prototypes and under-1K-doc cases) or AWS OpenSearch (50K+ doc enterprise with complex access control). We pick by data volume, latency budget and ops capacity, not vendor allegiance.
Production RAG isn't a vector database with an LLM glued on. It's chunking strategy tuned to document type (legal contracts chunk differently from FAQs), hybrid search (vector + BM25 keyword for precise term matching), re-ranking (Cohere Rerank, BGE Reranker, or a smaller LLM call) lifting accuracy 10–25% on most corpora, metadata filtering for security and freshness, citation surfacing so answers are auditable, and refresh pipelines for knowledge bases that change weekly or faster. We engineer each of these as a separate concern, evaluate them against held-out test sets, and ship them with observability.
Every RAG engagement starts with a free 30-minute discovery call and a fixed-price 2–3 week proof-of-concept on your actual data with the actual LLM. You measure retrieval accuracy and answer quality before committing to the full build — eliminating the #1 way RAG projects fail, which is committing to a 100K-document enterprise build before the chunking strategy has been validated.
Production RAG using AWS Bedrock's managed service — chunking, embedding, vector storage, retrieval handled. 60–80% faster to ship than custom. Best for under 5,000 documents. $8K–$18K offshore-delivered, 2–3 weeks.
Production RAG for 5K–500K+ documents, custom retrieval patterns, hybrid search, re-ranking. $25K–$120K depending on corpus size and complexity. 5–12 weeks.
Text-to-SQL and RAG over PostgreSQL, MongoDB, internal APIs — not just documents. LangChain SQL agents on Bedrock with row-level security and citation. $20K–$60K.
Cleaning, chunking, OCR for scanned PDFs, layout-aware extraction for tables/forms, deduplication, metadata enrichment. Often the largest line item — but the foundation for accuracy. $5K–$30K depending on corpus quality.
Add Cohere Rerank, BGE Reranker or LLM-based re-ranking to existing RAG systems. Typical accuracy lift 10–25%. 1–2 weeks.
Event-driven, idempotent document refresh for knowledge bases that change weekly or faster — version tracking, schema evolution, zero-downtime swaps. $5K–$20K addition.
Held-out test set of 100–500 real questions, automated scoring of retrieval accuracy, answer quality and citation correctness. Weekly accuracy dashboards. 2–3 weeks.
RAG that handles documents and queries in 50+ languages — Spanish, German, French, Portuguese, Italian, Dutch, Hindi, Arabic, Mandarin. Cohere multilingual embeddings + locale-aware chunking. Adds 15–25% to base build.
Move existing RAG workloads onto Bedrock for data residency (EU GDPR, US HIPAA), cost optimisation or model portability. 4–8 weeks.
Monthly retainer covering accuracy tuning, chunking refinement, prompt iteration, knowledge-base refresh, model cost optimization (40–60% LLM bill reduction typical). Common after Tier 3+ launch.
Default to managed Bedrock for under 5K docs — eliminates 60–80% of pipeline engineering vs hand-rolled embedding/retrieval setups. Deployed inside your AWS account for data residency.
We've shipped on Bedrock Knowledge Bases, Pinecone, Weaviate, ChromaDB, Qdrant and AWS OpenSearch in production. We pick by data volume, latency budget and ops capacity — not vendor preference.
Vector-only retrieval misses precise term matches (product codes, drug names, contract IDs). Every Tier 3+ RAG ships with hybrid vector + BM25 keyword search plus Cohere Rerank or BGE Reranker — lifts accuracy 10–25%.
Every answer cites the source document and passage. Auditable, builds user trust, and aligns with EU AI Act transparency requirements and SOC 2 traceability.
Production RAG over changing content needs incremental, event-driven refresh — not batch nightly rebuilds. We build idempotent refresh with version tracking, schema evolution and zero-downtime swaps.
GDPR data residency (eu-west-1, eu-central-1), HIPAA-aligned designs with AWS BAA on Bedrock, SOC 2 controls, PHI redaction via Bedrock Guardrails. Used in healthcare, fintech, legal.
Held-out test set of 100–500 real questions, scored on retrieval accuracy (was the right doc found?), answer quality (is the answer correct?) and citation correctness. Quality dashboards from day one.
Every engagement starts with a 2–3 week fixed-price proof-of-concept on real (de-identified) data. You validate retrieval accuracy on the actual corpus before committing to the production build.
A few of the things we deliver under rag development:
Free 30-minute call. We map document types, corpus size, retrieval requirements, refresh cadence and compliance scope. Output: written tier recommendation and price band within 48 hours.
Vector database selection (Bedrock KB / Pinecone / Weaviate / OpenSearch), chunking strategy by document type, embedding model selection, hybrid-search vs vector-only, re-ranking decision, refresh-pipeline design. Written architecture doc before code.
Fixed-price 2–3 week PoC on real (de-identified) data with the actual LLM. Measure retrieval accuracy and answer quality before committing.
Engineer the production RAG — ingestion pipeline, vector DB, retrieval logic, re-ranker, citation surfacing, evaluation harness, observability. 5–12 weeks depending on tier.
Production deploy, weekly accuracy review, monthly chunking refinement, LLM cost optimization. Most clients move to ongoing optimization retainer.
RAG (retrieval-augmented generation) retrieves relevant content from your private data and passes it to an LLM so answers stay grounded in YOUR documents, not the model's training data. Three benefits: accuracy (no hallucination on your domain), auditability (every answer cites its source), and freshness (update knowledge by re-indexing, no model retraining).
RAG development cost in 2026 ranges from $8,000 for a small 10-50 document FAQ-RAG to $120,000+ for enterprise RAG over 100,000+ documents with hybrid search, re-ranking and refresh pipelines. A typical 1,000–5,000 document RAG costs $25K–$55K offshore-delivered. See the full breakdown by knowledge-base size at /blog/rag-chatbot-cost-breakdown.
Bedrock Knowledge Bases for under 5,000 docs and AWS-native stacks (managed, lowest ops). Pinecone for 5K–500K docs with simple metadata and strict uptime SLAs. Weaviate for hybrid search, schema control, BYOC. ChromaDB for prototypes. OpenSearch for enterprise stacks already on AWS with complex access control. We pick by data volume, latency budget and ops capacity.
Almost always yes, and far easier to maintain. Fine-tuning costs $5K–$50K just for the tuning run and locks you to that model version; updating means re-tuning. RAG updates cost nothing — re-index changed documents. Fine-tuning is right only for narrow style/tone matching, specialised vocabularies the model lacks, or strict latency requirements where retrieval overhead is unacceptable.
Yes — but it requires real data engineering. Hybrid search becomes mandatory (vector-only misses precise terms in large corpora), re-ranking lifts accuracy 15–25%, metadata filtering by source/date/permission gets non-trivial, and refresh pipelines must be incremental and idempotent. We deploy these on OpenSearch or Pinecone with custom chunking strategies. $80K–$150K+ offshore for full enterprise.
Three layers. First, document preparation — clean chunks, removed duplicates, accurate metadata. Second, retrieval quality — hybrid search, re-ranking, evaluation against held-out test sets. Third, answer-generation guardrails — explicit grounding prompts, citation requirements, 'I don't know' fallback when no relevant content is found. We measure all three on weekly accuracy dashboards.
Yes when built correctly. We deploy HIPAA-aligned RAG on AWS Bedrock with the AWS BAA, KMS encryption, audit logging, PHI redaction via Bedrock Guardrails. GDPR RAG runs in eu-west-1 / eu-central-1 with DPAs and EU-resident models. SOC 2 Type II controls (encryption, access control, monitoring, change management) are designed in from day one.
RAG retrieves and answers. An AI agent retrieves, reasons, calls tools, takes actions and verifies. A RAG chatbot answers "what's our refund policy?"; an agent reads the policy AND processes the refund. Most production AI assistants combine both — RAG for grounded answers, agent capabilities for actions. See /services/ai-agent-development for the agent side.
Yes — Bedrock migration is a common engagement. Drivers: data residency (EU GDPR, US HIPAA), cost optimization via Bedrock provisioned throughput, model portability across Claude / Nova / Llama, or consolidating onto one AWS-native AI stack. Typical migration: 4–8 weeks for an existing Pinecone or OpenAI-direct RAG to Bedrock Knowledge Bases.
Small RAG (10–50 docs, Bedrock KB): 2–3 weeks. Medium (500–5,000 docs, hybrid search + re-ranker): 5–8 weeks. Large (5K–50K docs with refresh pipeline): 8–12 weeks. Enterprise (50K+ docs with RBAC and audit): 10–16 weeks. All preceded by a 2–3 week fixed-price PoC.
Yes — RAG over structured data (PostgreSQL, MongoDB, internal APIs) using LangChain SQL agents on Bedrock. Includes row-level security, query validation, result citation. Particularly useful for analytics chat ("what was Q3 revenue by region?") and customer service over CRM/billing data. $20K–$60K offshore.
Book a free 30-minute discovery call via /contact. We'll walk through your document corpus, retrieval requirements, refresh cadence and compliance scope — then send a written tier recommendation and price band within 48 hours. Most engagements start with the 2–3 week fixed-price proof-of-concept on real data.
Generative AI agents, RAG assistants, copilots and chatbots built on AWS Bedrock, Claude, OpenAI and open models — for India and the USA.
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.
RAG chatbots answer from your own documents, not just an LLM's training data. Here's how to build one that's accurate, secure and production-ready.
Read article →RAG chatbot cost in 2026 ranges from $8,000 for a small 10-doc FAQ system to $120,000+ for enterprise RAG over 100,000+ documents. Here's the cost breakdown by KB size.
Read article →Enterprise AI assistants cost $100K–$300K+ with a US team or $30K–$80K offshore-delivered. Here's what drives the price and where to spend your budget.
Read article →AI chatbot cost in 2026 ranges from $3,000 for a rule-based FAQ bot to $300,000+ for an enterprise AI assistant. Here's the full breakdown — by tier, by region, by model.
Read article →There's no single best LLM. Here's how OpenAI, Claude and open-source models compare on accuracy, cost, privacy and control.
Read article →Tell us what you're working on and we'll get back within one business day.