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RAG Development Services for Enterprise AI Solutions

Your company already has the answers. They sit in PDFs, wikis, tickets and databases that nobody has time to search. KriraAI offers RAG development services that connect a large language model to your own business data, so every answer comes from your documents and shows its source. We build enterprise RAG solutions that stay secure, scale with your team and keep working after launch.

RAG development services connecting business data to grounded AI answers.

What Are RAG Development Services?

RAG development services cover everything needed to build AI that reads your data before it answers. That means data setup, search design, model integration, testing and support. The goal is correct answers from your own knowledge, not guesses.

What Is Retrieval-Augmented Generation?

Retrieval-augmented generation (RAG) gives an LLM the right facts at the moment a question is asked. The system finds the most relevant passages in your data and passes them to the model. The model then writes its answer from those passages, not from memory.

How RAG Works

  1. Business Data
  2. Ingestion
  3. Chunking and Embeddings
  4. Retrieval
  5. Reranking
  6. LLM
  7. Grounded Response

Why Businesses Use RAG

A normal LLM does not know your pricing, policies or last week's update. RAG works with private knowledge that changes often, and answers can cite their sources. You get more control over what the AI sees, with no costly retraining every time your data changes.

Our RAG Development Services

Custom RAG Application Development

Our RAG application development work covers document Q&A tools, enterprise search, knowledge assistants and domain-specific AI apps. Each one is shaped by the questions your users actually ask.

Enterprise RAG Solutions and Knowledge Assistants

We turn policies, SOPs and internal documents into one place to ask questions. Retrieval is permission-aware, so staff only see what they are allowed to see.

RAG Chatbot Development

Our RAG chatbot development covers customer support, employee help desks and product Q&A. Every reply cites its source, and the bot says so when it has nothing relevant.

Hybrid Retrieval and RAG Optimization

Vector search alone misses exact terms like part numbers. We combine semantic search, keyword search (BM25), metadata filtering, reranking and query rewriting to find the right passage for the first time.

Multimodal and Agentic RAG Development

We build RAG that reads PDFs, tables, images and structured data. Agentic RAG plans several search steps, calls tools or APIs and joins the results into one answer.

RAG Evaluation, Monitoring and Optimization

We test with real user questions and score retrieval quality, answer relevance and groundedness. Monitoring catches accuracy drops and hallucinations before your users do.

RAG as a Service and Implementation Services

Our RAG as a service option covers deployment, re-indexing, monitoring, maintenance and cost tuning. Our RAG implementation services also fix pilots that worked in testing but fail in production.

RAG Solutions We Build

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Internal Enterprise Search

Search by meaning across drives and wikis, with existing access rights respected.

Research and Decision Support Assistants

Pull facts from large report sets in minutes, with every point traced back to its source.

Product and Sales Knowledge Assistants

Quick, current answers on specs, pricing rules and competitor questions for sales teams.

Enterprise Knowledge Assistants

One chat window for policies, processes and past decisions, with links to every source.

Document Q&A and Document Intelligence

Ask plain questions across contracts, reports and manuals and get the exact clause or table.

Customer Support AI

Instant answers from help articles and past tickets, with hard cases handed to a human.

Internal Enterprise Search

Search by meaning across drives and wikis, with existing access rights respected.

Research and Decision Support Assistants

Pull facts from large report sets in minutes, with every point traced back to its source.

Product and Sales Knowledge Assistants

Quick, current answers on specs, pricing rules and competitor questions for sales teams.

Enterprise Knowledge Assistants

One chat window for policies, processes and past decisions, with links to every source.

Document Q&A and Document Intelligence

Ask plain questions across contracts, reports and manuals and get the exact clause or table.

Customer Support AI

Instant answers from help articles and past tickets, with hard cases handed to a human.

RAG Architecture and Technology Stack

Our RAG Architecture

Data Sources → Data Ingestion → Parsing, Chunking and Metadata → Embeddings and Indexing → Vector or Hybrid Search → Reranking → LLM and Context Generation → Citations and Grounded Response → Evaluation and Monitoring

In our projects, most weak RAG systems fail at the early steps, not at the model. Poor parsing and bad chunk sizes cause more wrong answers than the choice of LLM. So we fix the data before we write a single prompt.

Technologies We Use

We pick tools based on your data, budget and security needs:

LLMs

  • OpenAI GPT models
  • Claude
  • Gemini
  • Mistral and open-source models

Frameworks

  • LangChain
  • LlamaIndex
  • LangGraph

Vector and search

  • Pinecone
  • Qdrant
  • Weaviate
  • pgvector
  • Elasticsearch

Cloud and deployment

  • AWS
  • Azure
  • Google Cloud
  • Docker
  • Kubernetes

Tracing and evaluation

  • LangSmith

Secure and Enterprise-Ready RAG Solutions

Security is built into the retrieval layer, not added at the end.

Permission-aware retrieval

results filtered by document-level access

Role-based access control

different roles see different knowledge

Data encryption and PII protection

sensitive fields masked or removed

Audit logging

every query and source recorded for review

Private or on-premise deployment

run inside your own cloud or servers

Governance and compliance

policies and controls mapped to your industry rules

RAG vs Fine-Tuning

Requirement

RAG

Fine-Tuning

Changing or private knowledge

Strong fit

Less direct

Source citations

Yes, when built in

Not built in

Updating knowledge

Update or re-index data

New training cycle

Style and behavior

Limited

Stronger fit

Retrieval permissions

Supported

Not built in

RAG, fine-tuning or a mix of both should be chosen based on data freshness, behavior needs, security and business goals.

RAG Use Cases and Industries

Industries

  • Healthcare
  • Finance
  • Insurance
  • Retail
  • Manufacturing
  • Legal
  • SaaS
  • Education
  • Logistics

Use cases

  • Customer Support
  • Enterprise Search
  • Document Intelligence
  • Knowledge Management
  • Research
  • HR Assistants
  • Product Q&A
  • Sales Enablement

RAG Development Cost and Engagement Models

What Affects RAG Development Cost? Cost depends on your data sources, document complexity, retrieval design, integrations, security, deployment model, scale and evaluation needs. Engagement Models

  1. Proof of Concept
  2. Custom Development
  3. Dedicated RAG Team
  4. Managed RAG Services

Most clients start with a small proof of concept on one use case, then scale once it proves its value.

Why Choose KriraAI for RAG Development?

End-to-End RAG Engineering

One team owns data, retrieval, models, testing and support, so nothing gets lost in handovers.

Production-Ready Architecture

Built for real traffic and real users from day one, with monitoring planned in.

Evaluation-Driven Development

Every change is measured against test scores, so you always know how accurate the system is.

Secure Enterprise Deployment

Private cloud or on-premise, with access rules that follow your existing permissions.

End-to-End RAG Engineering

One team owns data, retrieval, models, testing and support, so nothing gets lost in handovers.

Build Your RAG Solution With KriraAI

Connect your business data with reliable, context-aware AI. Talk to our RAG development experts about your use case, data sources and deployment needs.

Talk to a RAG Expert