Expletech designs and implements enterprise RAG architectures, connecting LLMs to private documentation while keeping data private and answers grounded in your sources.
Your data — AI's intelligence
Standard AI doesn't know your private data
Public AI models are smart, but they lack access to your internal documentation, standard operating procedures, and product guides.
Without RAG integration
LLMs hallucinate and invent wrong facts when they lack access to real documentation
Employees spend hours searching through folders, chats, and wikis for a single rule
Pasting corporate data into public chats leaks IP and violates data privacy
Pre-trained models are frozen in time and cannot know updates from today
With Expletech RAG systems
Strict citation keeps answers grounded in your files
Semantic search finds the exact paragraph or document in seconds
Enterprise-grade private vector databases keep data entirely in your workspace
Real-time ETL pipeline syncs vector databases whenever you upload or edit files
What we build for your RAG solution
We establish a robust data ingestion and retrieval infrastructure tailored to your exact document formats.
Semantic knowledge retrieval
Context-aware search engine that understands what users mean, going beyond simple keyword matching.
Data connection & ETL pipelines
Automated text extraction from PDF, DOCX, XLSX, and Notion, translating raw files into high-quality vectors.
Hallucination control (guardrails)
Custom prompt engineering and validation layers that force the LLM to output 'I don't know' if proof is missing.
Interactive citation engine
Every single answer comes with clickable links and references to specific source paragraphs and pages.
Slack, Teams & web integrations
Deploy your RAG assistant to corporate workspaces or integrate it directly into web portals and support lines.
On-premise open-source models
Deploy models (Llama, Mistral) on private, secure servers for fully offline security and IP protection.
How we build your RAG system
A structured data-engineering roadmap from raw files to intelligent retrieval.
Data audit & ingestion design
We inspect your data sources, design chunking strategy, and select vector database architectures.
Vector DB & ETL pipeline build
We spin up Pinecone/Qdrant databases and code automated ingestion scripts for real-time syncing.
LLM tuning & hybrid search
We configure hybrid keyword-semantic search, implement Cohere Re-ranking, and calibrate system prompts.
Security, testing & delivery
We run compliance checks, implement role-based access control (RBAC), and launch the final integrations.
Reliability metrics of our RAG systems
Engineering-first approach to information retrieval, ensuring speed, precision, and privacy.
Transparent RAG development pricing
Cost depends on data complexity, source formats, and cloud or on-premise infrastructure requirements.
RAG audit & architecture
Document sources audit · chunking and metadata strategy · RAG architecture design · LLM & Vector DB selection
RAG MVP
Connection to 1-2 data sources · Pinecone/Qdrant setup · standard prompt flow · Slack or Telegram frontend integration
Enterprise RAG
Integration with complex DBs/Confluence · hybrid search + re-ranking · citations · role-based access controls · web dashboard
On-premise RAG & tuning
Local LLM deployment (Llama-3/Mistral) on client servers · model fine-tuning on domain-specific data · fully offline privacy
What determines your price
- Diversity of document formats & data source complexity (Confluence, DBs, PDFs)
- Retrieval strategy requirements (hybrid search, semantic ranking, re-rankers)
- Role-based access control (RBAC) complexity for corporate security
- Infrastructure choice (vector cloud APIs vs. local/on-premise databases)
Enterprise-grade reliability and security
We keep your data confidential and the system's answers under control.
Confidentiality (NDA)
We sign a comprehensive NDA. Your data is never uploaded to public servers and is never used to train public models.
Strict hallucination control
Our systems only answer when they find corresponding sources. If information is missing, the AI says so rather than making up answers.
On-premise deployment
We can run the entire RAG pipeline offline on your company's physical hardware or private secure cloud VPC.
Technical support & SLA
We monitor search availability and retrieval precision, and update embedding models under the SLA terms.
Complementary AI solutions
Integrate RAG systems with other capabilities to build complete cognitive platforms.
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Common questions
Everything you need to know about implementing RAG.
Give your data a voice.
Let's check if your data is ready for a semantic search engine and discuss the implementation timeline.