Technology
How Chervik works
An educational overview of the AI and infrastructure technologies that power the Chervik platform.
AI Models & Multi-Model Routing
Chervik uses a model-agnostic routing layer that selects the optimal LLM for each task — balancing cost, latency, and capability. Supports OpenAI, Anthropic, Mistral, Llama, and custom fine-tuned models. Models run locally or via private API endpoints.
Retrieval-Augmented Generation (RAG)
RAG grounds AI responses in your actual enterprise data, eliminating hallucinations. Documents are chunked, embedded, and stored in a vector database. At query time, the most relevant chunks are retrieved and injected into the LLM context window alongside the user's question.
Hybrid Search
Pure semantic search misses exact matches. Pure keyword search misses intent. Chervik combines both using a reciprocal rank fusion algorithm — delivering the precision of BM25 with the semantic understanding of vector search.
Security Architecture
Security is not an afterthought. All data is encrypted at rest and in transit. Role-based access control ensures users only see content they're authorised to access. Every AI query is logged for audit purposes. Supports deployment in air-gapped environments.
Cloud-Native Architecture
Built on Kubernetes for portability across AWS, Azure, GCP, and on-premise. Infrastructure is defined as code using Terraform. Horizontal scaling, health checks, and zero-downtime deployments are built in.
Data Sovereignty
Your data never leaves your infrastructure. Chervik is designed to run entirely within your cloud account or on-premise data centre. No telemetry, no data sharing, no vendor lock-in on your knowledge assets.
Ready to deploy AI that stays in your control?
Talk to our team about your enterprise AI requirements. No commitment required.