Productized Engagement

Retrieval grounded in your data — production-grade, not demo-grade.

A productized RAG engagement — ingestion, a current vector store, retrieval, governance, and evals — so the model answers from your real, governed data instead of a curated demo set. The Data & Retrieval pillar of AI Enablement, our data engineering evolved for LLMs.

Scope Your RAG Engagement
RAG retrieval on AWS illustration

The same retrieval that hit the right document on demo data misses on real customer data — stale indexes, no governance, no way to tell whether an answer is grounded. RAG-in-a-box builds the retrieval layer properly: ingestion that stays fresh, a vector store on AWS, access scoped by IAM, and evals that catch regressions before users do.

Deliverables

What You'll Receive

Ingestion & vector store

Pipelines that bring your sources into a vector store — Amazon OpenSearch or Bedrock Knowledge Bases — with freshness, lineage, and the governance to keep retrieval trustworthy.

Retrieval grounded in your data

RAG against your indexed data with access scoped by IAM, so the model grounds on what the user is allowed to see — and cites where the answer came from.

Evals & quality gates

An evaluation harness that scores retrieval quality on your data — not "looked good in the demo" — wired into CI so a regression is caught before it ships.

How It Works

1
Scope

We map your sources, access rules, and quality bar to a retrieval design — what gets indexed, how it stays fresh, and how grounding is measured.

2
We build

Ingestion, vector store, retrieval, and governance on AWS, with an evaluation harness that proves retrieval holds up on your real data.

3
Handover

The retrieval layer, the evals, and the runbooks — yours to keep and extend, ready for the Application Delivery pillar to build the feature on top.

Is This For You?

This engagement is a good fit if you:

  • Have an LLM feature that needs to answer from your data
  • Found that retrieval works in the demo but misses in production
  • Need access scoped so users only see what they're allowed to
  • Want evals instead of eyeballing answers
  • Are based in the United States
Advanced Tier

AWS Partner

AWS-Certified

Engineering team

Months→Hours

Governed data at NYBC

Yours

You keep the retrieval layer

Ground your AI in real data.

Tell us what your AI needs to answer from. We'll scope the retrieval layer and come back with a plan — with us or not.

Scope Your RAG Engagement

Fixed scope. Senior engineers. A plan before you commit.