Rivac LabsRAG Pipeline Engineer
Most RAG systems that underperform aren't failing because of the model — they're failing because of chunking that splits context badly, retrieval that surfaces the wrong documents, or no re-ranking step to fix it before generation. We build production retrieval-augmented-generation pipelines end to end: chunking strategy, hybrid search, re-ranking, and eval-driven tuning against your actual document set and query patterns. This is for teams whose RAG feature gives plausible-sounding but wrong answers, or who are building one from scratch and want retrieval quality treated as the hard problem it is, not an afterthought. You get a pipeline measured against a real eval set, with retrieval quality you can quantify rather than guess at.
How We’d Approach This
A clear, staged plan — not a black box
- 1
Analyze your document corpus and real query patterns to diagnose where retrieval is likely to fail.
- 2
Build a pilot pipeline — chunking, embedding, retrieval, re-ranking — against a representative query set.
- 3
Review retrieval quality metrics with your team and tune chunking and ranking based on measured gaps.
- 4
Deploy the tuned pipeline with the eval set in place so future document or query changes stay measured.
What You Get
Deliverables from this engagement
- Production RAG pipeline (chunking, retrieval, re-ranking)
- Evaluation set and retrieval-quality metrics report
- Hybrid search configuration tuned to your document set
- Documentation for maintaining and extending the pipeline
Six Ways We Could Architect This
Different engagement, different build — pick the shape that fits
There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.
Ready to get started?
Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start — scoped small enough to prove itself before you commit to anything bigger.
Talk to us about RAG Pipeline Engineer