Generative AIRetrieval-Augmented Generation

RAG that makes your AI cite its sources — and stop making things up.

An LLM that confidently invents answers is a liability, not a feature. We build production RAG that connects models to your documents, databases, and knowledge — so every answer is accurate, current, and citation-backed at enterprise scale.

Senior engineers only — no juniors on your dimeYou own 100% of the codeWe reply within 24 hours

Key performance indicators

Hallucination rate reduction (%)

Retrieval precision & recall scores

End-to-end query latency (ms)

Knowledge base freshness (update lag)

Measured on every engagement

Delivery plan

Plan delivery, hit milestones, measure outcomes

RAG architecture engagements are scoped with clear data ingestion, retrieval design, and evaluation milestones.

Milestone-based delivery

Progress you can verify, sprint by sprint

  • A working demo every week — not a status deck
  • A direct line to the engineers building it
  • Scope locked per milestone — no surprise invoices
  1. 1

    Phase 1

    Data audit & chunking strategy

  2. 2

    Phase 2

    Embedding model selection & indexing

  3. 3

    Phase 3

    Retrieval pipeline & prompt design

  4. 4

    Phase 4

    Evaluation, scaling & monitoring

Deliverables

What we hand over

Concrete, verifiable artifacts produced during delivery — quality you can audit, not promises.

01

Production RAG pipeline

02

Vector database & embedding store

03

Retrieval quality evaluation report

04

Ingestion & update automation

What we measure

Expected outcomes

Every engagement is tracked against results you can put in front of your board — not effort, outcomes.

01

Factually grounded AI responses

02

Reduced hallucination and compliance risk

03

Live knowledge base synchronized with source

How we integrate

Engagement blueprint

How our teams plug into yours — from day one.

Core team

  • RAG architect
  • Embedding & vector DB engineer
  • LLM integration engineer

Prerequisites

  • Knowledge sources identified & accessible
  • Chunking & metadata strategy defined
  • Latency & accuracy targets agreed

Engagement models

  • Fixed-scope RAG build
  • Iterative retrieval optimization
  • Ongoing knowledge sync retainer

Let's build something extraordinary

RAG systems that make your LLMs trustworthy — grounded in your data, not hallucinated guesses.

2000+ vetted engineers · 3 global hubs · 98% client retention

FAQs

RAG Architecture questions

Questions about our process, pricing, or technology? Clear answers to the most common ones.

Still have questions?

We reply within one business day.

Talk to an expert

Contact Us

for project discussion

Once you fill out this form, our sales representatives will contact you within 24 hours.

2000+
Talents Vetted
3+
International Offices
100+
Project Delivered
50%-70%
Average Cost Saving

Got a project in mind?

We guarantee to get back to you within a business day.