Our Approach
We build RAG systems with a clear set of engineering principles, a proven reference architecture, and technology choices that prioritize security, transparency, and maintainability.
Engineering principles
These principles guide every decision we make, from architecture design to technology selection.
Security by design
Security is built into every layer of the system, not added as an afterthought. Data never leaves your infrastructure, and access controls are enforced at every interaction point.
Transparency and explainability
Every response generated by the system can be traced back to its source documents. Users understand where information comes from and can verify accuracy.
Auditability
Complete logging of all system interactions, including queries, retrieved documents, and generated responses. Essential for compliance and continuous improvement.
Data sovereignty
Your data remains under your control at all times. No external API calls with sensitive information, no cloud dependencies for core functionality.
Modular architecture
Components can be replaced, upgraded, or customized independently. No vendor lock-in, no proprietary dependencies that limit your options.
Infrastructure agnostic
Deployable on your existing infrastructure, whether on-premise servers, private cloud, or hybrid environments. We adapt to your constraints.
Reference architecture
A layered architecture that separates concerns and allows independent scaling and customization of each component.
Data Ingestion
Document processing pipeline that handles various formats, extracts text, metadata, and structure. Includes quality validation and deduplication.
Embedding & Indexing
Converts processed text into vector representations and builds searchable indices. Supports multiple embedding models and indexing strategies.
Retrieval
Semantic search across your knowledge base with configurable relevance scoring. Supports filtering, reranking, and multi-stage retrieval.
Generation
LLM integration for response generation with retrieved context. Supports various models, including on-premise options.
Application
User-facing interfaces and API endpoints. Includes authentication, authorization, and integration capabilities.
Observability
Monitoring, logging, and analytics for system health and performance. Enables continuous improvement and compliance reporting.
Technology stack
We use proven, well-documented technologies that your team can understand, maintain, and extend.
Embedding Models
- -Sentence Transformers
- -E5 / BGE models
- -Instructor embeddings
- -Custom fine-tuned models
All models can run on-premise without external API calls.
Vector Databases
- -Qdrant
- -Milvus
- -Weaviate
- -pgvector
Selected based on scale requirements and existing infrastructure.
Language Models
- -Llama 3 / Mistral (on-premise)
- -Claude / GPT (where permitted)
- -Fine-tuned domain models
Model selection depends on regulatory constraints and performance requirements.
Infrastructure
- -Docker / Kubernetes
- -PostgreSQL
- -Redis
- -Prometheus / Grafana
Designed to integrate with your existing operations tooling.
Development
- -Python
- -LangChain / LlamaIndex
- -FastAPI
- -React / Next.js
Standard technologies that your team can maintain.
Want to discuss architecture for your use case?
Every organization has unique constraints and requirements. A workshop is the best way to explore how our approach adapts to your specific situation.