AI & RAG data pipelines
Feed your LLMs fresh, governed context from every operational source, all self-hosted inside your VPC.
Why this belongs on your own infrastructure
Retrieval-augmented generation is only as good as its data freshness. A vector store built from last night's snapshot answers yesterday's question. Bridge pipes structured and unstructured sources into your embedding pipeline on a CDC cadence, masks sensitive fields in-flight, and keeps the entire stack — including your corpus — inside your VPC.
Common challenges
Stale context
A vector index built from a nightly batch goes stale within hours; users lose trust in the answers.
PII leakage to the model
Customer and employee data must be masked before it can ever reach an embedding step or model.
Data residency
Regulated corpora cannot leave the region or the VPC, ruling out most managed pipelines.
Structured + unstructured
Real answers need both operational rows and documents unified into one index.
Built for exactly these constraints
Capabilities drawn from production deployments — log-based capture, in-perimeter operation, and a spend you can predict.
Stream DB changes into embeddings
CDC feeds the embedding pipeline so the index tracks source changes continuously.
Data masking
Tokenize or redact sensitive fields before they reach the embedding step or model.
Self-hosted whole stack
Capture, transform, embed, and index all run inside your VPC — your corpus never leaves.
Parse → chunk → embed → index
Document pipelines handle parsing and chunking before embedding.
Schema drift handling
Source changes propagate to the index without manual rework.
Predictable cost
A 3× monthly cap covers both operational and AI workloads.
What the pipeline looks like
All stages run inside your network. No data egresses unless you choose to.
Ready to put ai & rag data pipelines into production?
Book a 30-minute demo scoped to your stack, or start a free evaluation license today.