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RROEI/TECH
PROJECT / 02 · AI SYSTEM

A production-grade retrieval agent teams drop into their existing chatbot.

An embeddable RAG service with ingestion, retrieval, grounding and evaluation — designed to plug into any chatbot or product surface.

CLIENT
RAG Agent
YEAR
2025
INDUSTRY
AI Infrastructure
STATUS
In production
p95 answer latency
< 900ms
grounded answer rate
94%
time to integrate
1 day
PROBLEM

Teams shipping chatbots kept rebuilding the same retrieval stack — chunking, embeddings, re-ranking, grounding, evals — and kept getting the same production issues: hallucinated answers, stale sources, and no observability into why an answer went wrong.

SOLUTION

A hosted RAG agent with a typed API and a drop-in JavaScript client. Ingest documents from URLs, files or connectors; ask questions from any chatbot with a single call; get grounded answers with cited sources, streamed to the UI.

ARCHITECTURE
  • Ingestion workers with content-aware chunking and metadata extraction
  • Vector store (pgvector) with hybrid retrieval and re-ranking
  • Prompted answer synthesis with strict grounding and citation extraction
  • Evaluation harness for regression tests on real question sets
  • Per-tenant isolation, PII redaction and audit trail on every query
TypeScriptNode.jsPostgreSQLpgvectorAnthropicOpenAI
OPERATING FLOW
01
Ingest
02
Chunk
03
Embed
04
Retrieve
05
Re-rank
06
Ground
07
Cite
08
Answer
MODULES

The system, module by module.

01

Ingestion

URL, file and connector ingestion with content-aware chunking and metadata.

02

Retrieval

Hybrid search (BM25 + vector) with a re-ranker to surface the best evidence.

03

Answer synthesis

Grounded generation with inline citations and streaming to the chatbot UI.

04

Evaluation

Golden sets, LLM-judge scoring and regression alerts on every deploy.

OUTCOME

Customers ship a grounded assistant in an afternoon instead of a quarter, with evaluation in place from day one.

ArchitectureAI EngineeringPlatform