Standard engagement
RAG-as-a-Service
Your docs, searchable by an agent that cites every answer.
A fully managed retrieval-augmented agent over your knowledge base. We index your docs, build the retrieval pipeline, evaluate citation accuracy, and operate it month over month. Your team asks questions in Slack, the customer-facing widget, or your app — the agent answers with citations or admits it does not know.
from $1,800
2–3 weeks setupPineconepgvectorOpenAIAnthropicLangChainRe-rankers
What ships during the engagement.
Indexed corpus (up to 50k pages) with chunking strategy tuned to content
Retrieval pipeline with re-ranker + hybrid search (vector + BM25)
Citation-formatted response template with source links
What you walk away with.
- Production RAG agent over your knowledge base
- Citation-grounded answers (every claim links back to source)
- Eval suite that catches retrieval drift
“They scoped, shipped, and operated our RAG pipeline in twelve days. Citation accuracy on our eval set landed at 92%, and ongoing tuning costs us less than a Slack seat.”
- How big can the corpus be?
- We index up to 50k pages in the base scope. Larger corpuses (100k+) add 1 week and are priced separately.
- What is the ongoing cost?
- Vector storage runs $20–$200/mo depending on volume. LLM inference depends on traffic — typical SMB: $40–$400/mo. You pay these directly (BYOK).
Want to scope RAG-as-a-Service?
A short call to confirm fit and timeline.