Search with RAG
Improve the accuracy of search results with RAG-enhanced retrieval that understands what people mean, not just the words they type.
RAG Development Services for Enterprises
Connect private data to LLMs for accurate & contextual answers
Our end-to-end RAG as a Service handles everything from data pipelines to model integration and ongoing maintenance. We turn your internal knowledge base into a secure, intelligent assistant that answers from your own documents and shows where every answer came from.
How a RAG answer is built
- 1
Your team asks a question
In chat, search, Teams or your app
- 2
Search your data
Hybrid search and reranking across documents and databases
- 3
Pick the relevant passages
Only content the user is allowed to see
- 4
Answer with sources
The LLM answers from those passages and cites each one
RAG as a Service
RAG as a Service Solutions for Enterprises
Retrieval-augmented generation brings together smart data retrieval and content generation. Instead of relying on what a language model memorized, a RAG system first finds the right information in your data, then uses it to write an accurate, up-to-date answer.
Our RAG platform integrates with your existing systems to boost team efficiency, whether you need automated customer service or faster knowledge sharing. We build AI tools that grow with your business.
- 1
AI Retrieval
Access real-time data from your databases and documents, so answers are always based on the latest, relevant information.
- 2
Data Generation
Precise, context-sensitive responses that adapt to what each user needs, improving accuracy in every interaction.
- 3
Contextual Responses
RAG-powered AI plugs into your systems, improving performance without disrupting your workflow.
- 4
Scalable Solutions
Scale your AI applications as your business grows, with RAG solutions tailored to your needs.
What we offer
Our RAG Development Services
From the first document ingested to the answer on screen, we build every part of the RAG pipeline and tune it for accuracy on your own data.
Custom RAG Implementation
Secure RAG pipelines tailored to your data and business workflows, designed for accuracy from day one.
Seamless LLM Integration
We connect your RAG system to GPT, Claude, Gemini or a custom LLM, and can expose your knowledge to AI agents through MCP (Model Context Protocol).
Data Ingestion & Pipelines
PDFs, wikis, tickets, databases and SharePoint, including scanned pages, tables and images (multimodal RAG), are parsed, chunked and kept in sync as content changes.
Vector & Hybrid Search
Embeddings, vector databases and keyword search combined, with reranking so the most relevant passages reach the model.
Agentic RAG
Assistants that plan multi-step lookups, query several sources and use tools before answering complex questions.
GraphRAG & Knowledge Graphs
Relationships between people, products and documents mapped into a knowledge graph, for questions that span many sources.
RAG-Powered Chatbots
Support and internal help-desk assistants that answer from your knowledge base and cite the page each answer came from.
Evaluation & Monitoring
Test sets and automated scoring for retrieval quality, answer accuracy and hallucinations, plus monitoring after launch.
Process
Our RAG Development Process
- 1
Discovery & Data Audit
We agree on the questions the system must answer, find where the data lives and check its quality and access rules.
- 2
Ingestion & Chunking
Documents are parsed, cleaned and split into meaningful chunks, with metadata like source, date and permissions.
- 3
Embeddings & Indexing
We choose an embedding model and vector database, and index your content for fast semantic and keyword search.
- 4
Retrieval & Generation
Hybrid search and reranking find the best passages; the LLM answers from them and cites its sources.
- 5
Evaluation & Monitoring
We measure accuracy on real questions before launch, then monitor quality, cost and user feedback in production.
Industries
Industry-Specific RAG Solutions
Our domain-specific RAG solutions combine real-time data retrieval with intelligent AI models, delivering accurate, context-aware and actionable insights for faster decisions and better business outcomes.
RAG for Healthcare
Enhance patient care with AI-driven knowledge retrieval. Give doctors instant access to medical research and patient history for accurate decisions.
RAG for Finance
Secure, real-time data insights for financial institutions, from fraud detection to portfolio analysis, with accurate reporting and risk management.
RAG for E-commerce
Better product discovery, personalized recommendations and customer support with RAG-driven search and dynamic content.
RAG for Manufacturing
Real-time retrieval from IoT devices, production logs and supply chain records to streamline processes and reduce downtime.
RAG for Legal & Compliance
Search contracts, policies and regulations in plain language, with every answer linked to the exact clause it came from.
RAG for Travel & Hospitality
Assistants that answer booking, policy and destination questions from your own content, around the clock.
Use cases
What Businesses Build With RAG
Customer Support
Automate and personalize responses with dynamic knowledge retrieval, so agents and customers get correct answers faster.
Content Creation
AI-driven generation for content that's tailored and contextually relevant, grounded in your own approved material.
Technology
RAG Technologies We Work With
We aren't tied to one vendor. We pick the database, framework and model that fit your data, scale and privacy rules.
- Vector Databases
- pgvectorPineconeWeaviateQdrantMilvusElasticsearch & OpenSearch
- Frameworks
- LangChainLlamaIndexHaystackLangGraph
- Models & Embeddings
- OpenAI GPTAnthropic ClaudeGoogle GeminiLlama & MistralCohere Embed & Rerank
- Evaluation & Observability
- RagasDeepEvalLangfuseLangSmith
Why Infilon
Have a Vision for Your AI Solution?
At Infilon, we build retrieval-augmented generation solutions across a wide range of industries, including business, healthcare, travel, banking, gaming, construction, e-commerce and sales. Whether you want better search, automated customer support or faster content generation, we'll work with you to turn your vision into a successful AI-driven reality.
Our development process is thorough and efficient, delivering intelligent, scalable and context-aware systems that meet your business needs. You focus on your core operations while we implement the AI.
Why teams choose us
- Answers grounded in your data, with source citations
- Permission-aware retrieval, so users only see what they're allowed to
- Hosted in your cloud or on-premise when data can't leave
- Accuracy measured before launch and monitored after
FAQ
RAG Development FAQs
What is retrieval-augmented generation (RAG)?
RAG is a way of building AI assistants that look up relevant information in your own data before answering. The system retrieves the most relevant passages from your documents or databases, gives them to a large language model, and the model writes an answer based on them, usually with links to the sources.
What does RAG as a Service include?
Everything needed to run RAG in production: data ingestion and pipelines, embeddings and a vector database, retrieval and reranking, LLM integration, a chat or search interface or API, evaluation, hosting, monitoring and ongoing maintenance as your data changes.
Should we use RAG or fine-tune an LLM?
Use RAG when answers depend on information that changes often or needs a source. Fine-tune when you need a specific style, format or vocabulary. Many systems combine both: a fine-tuned model answering from documents retrieved with RAG.
Do we still need RAG now that models have long context windows?
Usually, yes. Long context windows let a model read more at once, but sending your whole knowledge base with every question is slow and expensive, ignores user permissions and has no clear sources. RAG sends only the relevant passages, which keeps answers faster, cheaper, permission-aware and citable. For small document sets, we sometimes combine both.
How do you reduce hallucinations in a RAG system?
We improve retrieval with hybrid search and reranking, instruct the model to answer only from the retrieved passages, show citations, and let it say when it doesn't know. We then test on real questions and track accuracy after launch.
What data sources can a RAG system use?
Almost any: PDFs and Word files, websites and wikis, SharePoint and Google Drive, help-desk tickets, CRM and ERP records, and SQL databases. We keep the index in sync so answers reflect the latest content.
Is our data secure?
Yes. Retrieval respects each user's permissions, personal data can be masked, and the whole system can run in your own cloud or on-premise with open-source models when data must not leave your environment.
How long does it take to build a RAG solution?
A working proof of concept on your own documents usually takes a few weeks. A production system with integrations, permissions, evaluation and monitoring typically takes two to three months, depending on the number of data sources.
Ready to Build a Smarter, More Accurate AI?
Tell us which questions your team or customers ask most, and where the answers live. We'll show you what a RAG assistant on your own data could do.
Get a RAG Solution