AI Solutions
RAG Architecture & LLM Implementation for Enterprise
We design and build retrieval-augmented generation systems that ground AI answers in your data — keeping documents in your infrastructure and giving you production-ready LLM applications.
What is RAG?
Why RAG is the right architecture for enterprise AI
Retrieval Augmented Generation (RAG) is the dominant architecture for enterprise LLM applications because it solves the core problem with off-the-shelf LLMs: they do not know your data, your policies, or your specific domain.
In a RAG system, your documents are converted to vector embeddings and stored in a vector database. When a user asks a question, the system retrieves the most relevant passages and provides them to the LLM as context. The model answers from that context — not from general training data.
This eliminates hallucinations on domain-specific questions, keeps all your data in your own infrastructure, and allows the system to cite the source documents it used — essential for compliance and audit requirements.
Use cases we build
Internal knowledge base assistant
Employees ask questions; AI answers from your HR policies, SOPs, and internal docs.
Document Q&A
Upload contracts, reports, or technical manuals. Ask questions in natural language.
Customer-facing chatbot
Answer product, policy, and support questions grounded in your documentation.
Compliance document search
Instantly surface relevant clauses from regulatory documents, legal agreements, or compliance policies.
Technical implementation
The stack we build on
Vector databases
- pgvector
- Pinecone
- Weaviate
Embedding models
- OpenAI Ada
- Cohere Embed
- Sentence Transformers
LLMs
- OpenAI GPT-4
- Google Gemini
- Anthropic Claude
- Llama (on-prem)
Orchestration
- LangChain
- LlamaIndex
- Custom pipeline
Data privacy by design
All data processing happens in your infrastructure. We do not train any model on your documents. The only external API call is the LLM inference — and even then, only the retrieved passage and the user query are sent, not your full document library.
For organisations with strict data residency requirements, we deploy fully on-premise using open-source LLMs (Llama 3, Mistral) and self-hosted vector databases — no data leaves your network.
Integration patterns
- REST API (integrate with any system)
- WhatsApp Business API
- Slack and Microsoft Teams
- Web widget (embed anywhere)
- Custom mobile app (React Native)
Technical FAQ
RAG implementation questions
Build a production RAG system
Tell us about your data and use case. We'll design the right architecture.