Service · 26
Data Engineering & Knowledge Pipelines
From raw sources to AI-ready knowledge — ingestion, distillation and retrieval that scale.
How it's built
(1) Source audit — systems, formats, freshness requirements. (2) Pipeline design — schema, flow plan, distillation strategy. (3) Build — ingest and transform, embedding and indexing. (4) Quality — dedup, drift checks, retrieval benchmarks. (5) Serve — APIs and retrieval layer with scaling and monitoring.
Core fundamentals
- garbage in, garbage out — quality gates at ingestion
- freshness SLAs per source
- retrieval benchmarked, not assumed
- pipelines that self-heal
Build blueprint
Deliverables
- data pipelines
- knowledge API
- quality dashboard
- schema docs
Stack
FastAPIPostgreSQLWeaviateLangGraph
Tags
Data EngineerETLVector DatabaseKnowledge Graph
From $3,000