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AI-Native Knowledge Systems

Your Private Data,
Analyzed by AI

We build custom Retrieval-Augmented Generation (RAG) systems that connect your documents, databases, and knowledge bases to large language models — giving your Team / Clients instant, accurate answers based on included data.

On-Premise or Cloud
No Data Leaves Your Servers
Any LLM, Any Stack
RAG Pipeline — Live Preview
User Query
"What is our refund policy for Q3?"
Vector Retrieval
Semantic search across your knowledge base
Top-K Results
Context Injection
Relevant chunks injected into prompt
Grounded
LLM Generation
GPT-4o / Claude / Llama / Gemini
Answer Ready
98%
Accuracy Rate
<2s
Response Time
100%
Data Private

• RAG AI

The Technology
What is RAG?

AI System that knows your business, not just the internet

Standard AI models like GPT-4 are trained on public internet data — they don't know your internal policies, product specs, customer history, or proprietary research. RAG (Retrieval-Augmented Generation) bridges this gap.

Custom RAG System Architecture by Medians AI Custom RAG System Architecture

Instead of relying on memorized training data, a RAG system retrieves the most relevant chunks from your private knowledge base in real time, then feeds them into the AI model as context — producing answers that are accurate, up-to-date, and fully traceable to your own sources.

85%

Reduction in AI hallucination
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10x

Faster knowledge retrieval
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100%

Data stays within infrastructure
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The RAG Pipeline

Three steps from question to trusted answer

Our RAG architecture is engineered for speed, accuracy, and full auditability — every answer is traceable to a source.

01
Ingest & Index

Your knowledge base, transformed into searchable vectors

We connect to your data sources — PDFs, Word docs, databases, SharePoint, Confluence, S3, APIs — chunk and embed the content using state-of-the-art embedding models, then store it in a high-performance vector database (Pinecone, Qdrant, Weaviate, or pgvector).

  • Supports 50+ file formats & live data connectors
  • Automatic incremental re-indexing when data changes
  • Metadata filtering for access control & precision
RAG data ingestion and vector indexing
02
Retrieve & Rank

Find the exact context from millions of documents in milliseconds

When a user asks a question, our retrieval layer converts it to an embedding vector and performs lightning-fast semantic similarity search — returning the most relevant content chunks, re-ranked by our proprietary relevance scorer for maximum accuracy.

  • Hybrid search (vector + keyword BM25) for higher recall
  • Cross-encoder re-ranking for precision
  • Sub-200ms retrieval on collections with millions of records
RAG vector similarity search and re-ranking RAG vector similarity search and re-ranking
03
Generate & Cite

Grounded, source-cited answers your team can trust

The retrieved context is injected into a carefully engineered prompt and sent to your chosen LLM. The model generates a precise, human-readable answer — accompanied by source citations so users can verify every claim against the original document.

  • Automatic inline citations with page/section references
  • Confidence scoring & fallback messaging
  • Full audit log for compliance & traceability
RAG LLM answer generation with citations RAG LLM answer generation with citations

• RAG Pipeline

What We Build

Data is the King, So we build Data-Intensive Applications

Every RAG system we build is architected from scratch to fit your exact use case, data structure, and security requirements.

Multi-Source Data Ingestion

Connect PDFs, Word files, Excel sheets, SQL databases, REST APIs, SharePoint, Notion, Google Drive, and more. We build custom connectors for any data source your team relies on.

Hybrid Vector + Keyword Search

Combines dense vector semantic search with sparse BM25 keyword matching for best-of-both-worlds retrieval. Higher recall, better precision, fewer missed answers.

Role-Based Access Control

Enforce document-level permissions inside the RAG pipeline. Users only retrieve content they're authorized to see — fully synchronized with your existing IAM, SSO, or LDAP.

Any LLM, Your Choice

We integrate with OpenAI GPT-4o, Anthropic Claude, Google Gemini, Mistral, and self-hosted open-source models (Llama 3, Phi-3, Qwen). No vendor lock-in.

Conversational Memory

Multi-turn conversation support with session memory and context window management. Users can ask follow-up questions naturally — the system remembers the thread.

Analytics & Feedback Loop

Built-in dashboard tracks query volume, retrieval accuracy, user satisfaction scores, and unanswered questions — enabling continuous improvement of your knowledge base.

• Industry

Industry Applications

Transforming how industries access knowledge

RAG systems are not one-size-fits-all. Here is how we tailor them for different sectors.

RAG for Reports and analysis
Reports & Analysis

Smart Reports with Charts

Generate reports with interactive charts and visualizations, making data analysis intuitive and actionable. Perfect for financial analysis, market research, and any scenario where insights need to be communicated clearly.

Discuss Your Reports Use Case
RAG for Customer Support and Helpdesk
Customer Support

AI Support on Your Knowledge Base

Replace generic chatbots with an AI agent that reads your actual product docs, FAQs, and ticketing history. Resolve 70% of tier-1 queries instantly, with accurate, on-brand responses and automatic escalation for complex issues.

Build Your Support RAG
RAG for Healthcare and Clinical Documentation
Healthcare

Clinical Docs & Research Q&A

Query clinical guidelines, medical literature, patient records (HIPAA-compliant, on-premise), and research papers. Surface evidence-based answers faster than any manual review process.

Explore Healthcare RAG
RAG for Enterprise Internal Knowledge Management
Enterprise

Knowledge Assistant for Teams

Give every employee a brilliant internal search assistant. New hires onboard 3x faster. Senior staff stop fielding repeated questions. HR policies, runbooks, SOPs, and meeting notes all become instantly searchable.

Build Your Team RAG
Why Medians AI

We don't present ideas. We produce real results.

Medians AI RAG system engineering team
50+ RAG Projects
Delivered across 12 industries

Built From data-intensive Principles

We don't use code wrappers or pre-built scripts. Every system is custom-engineered for your data schema, data sources, and business requirements.

Security-First by Design

On-premise deployment options, end-to-end encryption, RBAC at the retrieval layer, and full OWASP compliance. Your data never leaves your infrastructure unless you choose cloud.

Reliability & Scalability

Doesn't matter the amount or count of data. Our architecture uses distributed vector stores and async re-indexing queues to handle any scale without degrading response quality.

Consistent + Ongoing Support

We don't disappear after launch. We provide documented APIs, admin dashboards, team training, and a dedicated support channel — so your team can own and evolve the system.

50+

RAG Systems Deployed

Across legal, healthcare, fintech, and enterprise sectors

<200ms

Average Retrieval Latency

Sub-200ms P95 on collections with 10M+ vectors

98%

Answer Accuracy Rate

Measured on client-defined evaluation benchmarks

• Medians

Technologies we work with

OpenAI / GPT-4o Anthropic Claude Llama 3 / Mistral LangChain / LlamaIndex Pinecone / Qdrant pgvector / Weaviate FastAPI / Python Docker / Kubernetes AWS / Azure / GCP
Common Questions

Everything you need to know about RAG

Can't find your answer? Talk to our team →

How is RAG different from fine-tuning an LLM on my data?
Fine-tuning bakes knowledge into the model weights — it's expensive, slow to update, and can't handle data that changes frequently. RAG keeps your knowledge base separate and retrieves from it dynamically at query time. This means your AI always answers based on the latest version of your documents, without retraining. It's also far cheaper and gives you source attribution that fine-tuned models can't provide.
Can I run the RAG system fully on-premise with no cloud dependency?
Yes, absolutely. We specialize in fully air-gapped, on-premise deployments using open-source LLMs (Llama 3, Mistral, Phi-3) running on your own GPU servers, with self-hosted vector databases (Qdrant, Weaviate) and embedding models. No data ever leaves your network. This is particularly common for healthcare, legal, and government clients.
How long does it take to build and deploy a custom RAG system?
A standard RAG system (single knowledge base, one interface, 1–3 data connectors) typically takes 4–8 weeks from kickoff to production. Complex multi-tenant enterprise systems with custom retrieval logic, advanced RBAC, and analytics dashboards can take 10–16 weeks. We provide a detailed project timeline during the discovery phase.
What file formats and data sources does Medians RAG support?
We support 50+ formats: PDF, DOCX, PPTX, XLSX, CSV, HTML, Markdown, plain text, JSON, XML — plus live connectors to SQL/NoSQL databases, REST APIs, Google Drive, Dropbox, SharePoint, Confluence, Notion, Jira, and S3-compatible storage. If your data source has an API or filesystem access, we can index it.
How do you ensure the AI doesn't hallucinate or make up answers?
RAG dramatically reduces hallucination by anchoring the model's response to retrieved documents. We add additional safeguards: (1) the prompt explicitly instructs the model to only answer from provided context, (2) a confidence threshold triggers an (I don't know) response when no relevant content is found, (3) every answer includes source citations that users can verify, and (4) our evaluation harness runs automated hallucination detection tests in CI/CD.
Can the RAG system be customized to fit specific use cases or industries?
Yes. We tailor the RAG architecture, retrieval strategies, and prompt engineering to your specific use case and industry. For example, a legal RAG system might use a custom retriever that understands legal citations and a prompt that instructs the model to answer in a formal tone with precise references. We also offer domain-specific LLMs and embedding models for industries like healthcare, finance, and customer support.