RAG Systems Engineering
Productionize the prototype your team has been demoing for six months.
Most RAG systems are demos pretending to be products. This engagement makes them real: chunking strategy that respects the corpus, retrieval evals you can actually trust, citation accuracy you can defend, and observability so you know when it breaks. Fixed scope, fixed price, deployable.
Concrete artifacts you keep.
Every line below ships during the engagement. No “TBDs”, no slide-deck hand-waving — working code, written docs, and dashboards your team owns.
The outcome, not just the output.
- Chunking + embedding pipeline tuned to your corpus
- Retrieval evals running on every change
- Citation accuracy measured and reported
- Cost ceilings, rate limits, and fallback paths
- Deployed to production with monitoring
How the engagement runs.
Corpus + query analysis
Sample your corpus, profile query patterns, agree on success metrics. Pick vector DB + embedding model + reranker stack.
Pipeline + evals
Build ingestion pipeline, chunking strategy, embedding store. Stand up retrieval eval harness against a golden set.
Reranking + hybrid + cost
Add a reranker only where it pays for itself. Tune hybrid (BM25 + vector). Profile cost per query and add ceilings.
Production + observability
Deploy to your infra, wire observability, document failure modes. Hand off with runbook and 4 weeks of tuning.
See the artifact, not the marketing.
Real shape, redacted content. Pick a tab to preview what ships.
Twelve-page audit excerpt: scope, methodology, findings ranked by impact, and a prioritized fix list. Redacted.
Sample provided after intro call · ask sage@sageideas.dev
Money-back if you're not happy in week 1
Reset the engagement before momentum builds. No invoices to dispute, no awkward email.
Async-first, weekly demos, no surprises
You see exactly what shipped each week. No status meetings to attend, no reports to chase.
Code is yours from day 1 — no lock-in
Your repo, your infra, your accounts. We work in your stack. You can take the work in-house at any time.
“Cut our flake rate from 12% to 0.4% in three weeks. The eval suite caught two regressions on day one of running in CI.”
Common questions
- How big a corpus can you handle?
- We have shipped systems from 10k to 10M chunks. Above that, we scope a longer engagement with sharding strategy.
- Which vector DB do you use?
- We start neutral. After looking at your corpus + query patterns + cost ceiling, we recommend Pinecone, pgvector, Weaviate, or Turbopuffer with a written rationale.
- Do you handle sources beyond text?
- Text + structured data + tables yes. Images and audio require a longer engagement and are scoped separately.
- What does success look like?
- A measurable retrieval quality target (e.g., recall@10 ≥ 0.85), agreed before kickoff, and a CI gate that prevents regressions.
Ready to scope RAG Engineering?
A 30-minute call to confirm fit, scope, and timeline. No pressure, no slides.
Average reply: 3 hours, business days