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Answers with citations, not hallucinations

RAG Knowledge Assistants

Retrieval-augmented assistants over your knowledge base that answer with sources and admit when they don’t know.

  • Published pricing — from $1,000, never quote-on-request
  • Senior strategists — no juniors learning on your dime
  • Live dashboard tied to revenue, not vanity rankings
Senior-led, every accountReviews published with real names
RAG Knowledge Assistants hero illustration
5.0
from 60 published client reviews
Senior pod, no juniors
on every account

Brands across the industries we serve

PfizerOracleBlackstoneMedtronicWeWorkWalgreensKeller WilliamsITVNovo NordiskSwiss LifeGE AerospaceEmaarPfizerOracleBlackstoneMedtronicWeWorkWalgreensKeller WilliamsITVNovo NordiskSwiss LifeGE AerospaceEmaar

What is RAG Knowledge Assistants?

Retrieval-augmented assistants over your knowledge base that answer with sources and admit when they don’t know.

  • Discipline: AI Agents
  • Typical outcome: Senior specialists named on the account
  • Engagement: monthly retainer or one-time audit
  • Seniority: lead strategist on every weekly call

About This Service

How We Deliver
RAG Knowledge Assistants

We build the retrieval pipeline properly: chunking and embeddings tuned to your content, hybrid search, re-ranking, and answer synthesis that cites its sources. Evaluated on a golden question set so you can prove accuracy before it faces a customer or an employee.

Talk to a Specialist

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Tuned chunking + embeddings

Hybrid search + re-ranking

Cited answer synthesis

Golden-set accuracy evals

Freshness / re-index pipeline

Pain Points

Why most teams stall before they see results

The patterns we see across new audits — and the fixes baked into every engagement.

Knowledge nobody can find

Information buried across docs and systems is effectively lost. RAG assistants surface it on demand.

Generic LLMs that don't know your data

Off-the-shelf models don't know your business. We ground assistants in your actual knowledge.

Hallucinations without grounding

Ungrounded AI invents answers. Retrieval grounding keeps responses factual and sourced.

Security and access concerns

Internal knowledge needs access control. We build secure, permission-aware assistants.

RAG Knowledge Assistants impact illustration

Measurable Results

Engineered for outcomes, not vanity metrics

8

Specialisations in AI Agents

1,000

US markets with their own measured page one

Since 2019

Building search programmes

Features

What RAG Knowledge Assistants includes

Core Offerings:

Tuned chunking + embeddings

Hybrid search + re-ranking

Cited answer synthesis

RAG Knowledge Assistants feature visualization

Additional Value:

Golden-set accuracy evals

Freshness / re-index pipeline

Why RAG Knowledge Assistants works

What makes this work hold up

No retainers without a measurable outcome attached. Every sprint is anchored to a KPI we agreed on in week one — with weekly Loom updates, a shared dashboard, and a senior strategist on every call.

  • Senior strategist on every account — no juniors learning on your dime
  • Weekly Loom updates plus a shared Looker dashboard
  • Your stack, your CMS, your CMS workflow — we adapt to you
  • Outcome-tied pricing — KPI ranges baked into the contract

Our Process

How we ship RAG Knowledge Assistants

A proven 4-step framework that compresses time to results without cutting corners.

01

Audit & Diagnose

Benchmark where RAG Knowledge Assistants stands today, surface the highest-leverage opportunities, agree on the KPIs we'll be measured on.

02

Strategy & Roadmap

A senior strategist sequences the 90-day roadmap with quick wins front-loaded and moat plays scoped behind them.

03

Execute & Ship

The pod ships work weekly — instrumentation, deliverables and dashboards updated against the KPIs you signed off on.

04

Measure & Compound

Monthly reviews recalibrate the roadmap. Wins compound, the program keeps moving, every dollar stays accountable.

01Answers from your data, with sources
Ground AI in your knowledge

Answers from your data, with sources

Generic LLMs don't know your business, and ungrounded AI hallucinates — RAG (retrieval-augmented generation) fixes both by grounding responses in your actual documents and data. We build knowledge assistants that retrieve from your content and answer from it with citations, so responses are factual, current and traceable to a source. Retrieval grounding is what makes an AI assistant trustworthy on your specific knowledge.

  • Retrieval-augmented generation over your data
  • Answers grounded in your documents
  • Source citations and traceability
  • Factual, current, business-specific responses
02Make scattered information findable
Surface buried knowledge

Make scattered information findable

Most organisations have knowledge scattered across docs, wikis, tickets and systems that nobody can find — so it's effectively lost. We build assistants that ingest and index that knowledge and make it findable through natural-language questions, so employees or customers get the right answer instantly instead of hunting. Surfacing buried knowledge on demand is where a RAG assistant delivers immediate, everyday value.

  • Ingestion and indexing across sources
  • Natural-language knowledge retrieval
  • Instant answers over scattered content
  • Knowledge findable, not buried
03Right answers to the right people only
Secure & permission-aware

Right answers to the right people only

Internal knowledge assistants touch sensitive information, so access control and security aren't optional. We build permission-aware retrieval (users only get answers from content they're allowed to see), secure data handling, and the guardrails an internal knowledge system needs. Security and permission-awareness are what make a knowledge assistant safe to deploy on real internal data.

  • Permission-aware retrieval and access control
  • Secure data handling
  • Guardrails for sensitive information
  • Safe deployment on internal knowledge

How we work

RAG Knowledge Assistants — what you are actually buying

Commitments rather than results: how the engagement is staffed, reported and priced. Every one of them is true on the first day, before anything has been measured.

  • Named senior specialists on every engagement
  • Reported via live Looker Studio dashboards
  • Tied to revenue KPIs, not vanity metrics
Senior
Specialists named on the account
60
Client reviews published with real names
1,000
US markets with their own measured page one
Since 2019
Building search programmes

FAQs

Questions buyers ask before signing

Quick answers to the things buyers always check first.

What is a RAG knowledge assistant?
It's an AI assistant grounded in your actual documents and data via retrieval-augmented generation — so it answers questions from your knowledge with citations, rather than a generic model guessing. It makes scattered organisational knowledge findable through natural-language questions.
How does it avoid hallucinating?
By grounding every answer in retrieved content from your documents and citing the source, so responses are factual and traceable rather than invented. Retrieval grounding is exactly what makes AI trustworthy on your specific knowledge.
Can it respect our access permissions?
Yes — we build permission-aware retrieval so users only get answers from content they're allowed to see, with secure data handling and guardrails. That's essential for an assistant touching sensitive internal knowledge.
What can we use it for?
Internal knowledge (employees finding answers across docs, wikis, tickets), customer self-service grounded in your help content, or expert assistants over technical documentation — anywhere buried knowledge needs to be findable and answered accurately.

Still have questions? Talk to a specialist

The team

The senior specialists behind your work.

No account-manager buffer and no juniors learning on your budget — you work directly with the people who do the work.

Rao Usama — Co-Founder & Product Officer, Head of AI / DataFounder

Rao Usama

Co-Founder & Product Officer, Head of AI / Data

14+ yrs engineering · AI agents, MCP & automation

Co-Founder and Head of AI & Data. 14+ years in software engineering; leads our AI-agent, MCP-integration and workflow-automation work — turning models into systems that actually run in production.

MSc, Data Science

Abdur Rahman Shah — Web & SaaS Engineer · DevOps

Abdur Rahman Shah

Web & SaaS Engineer · DevOps

WordPress · custom web apps · SaaS · DevOps · technical SEO

Builds and ships the web layer — WordPress, custom web apps and SaaS architecture — with the DevOps to run it and the technical-SEO knowledge to make sure what he builds is fast, crawlable and built to rank.

Free site audit · results in 60 seconds

Find out whether this should be an agent.

Describe the queue and we will tell you honestly whether an agent can take it safely, what it would need access to, and where a human still has to sit in the loop.

From our clients

What clients say about our RAG Knowledge Assistants

Reviews from our Upwork, Fiverr and direct client engagements. Each card shows where the review was left.

5 avg. client review · 60+ engagements

Contact

Ready to talk about AI Agents?

Send the details and a senior specialist replies within one business day — including an honest read on whether starting with AI Agents is right, or whether something else is capping you first.

Map of the United States — REO Rank works with clients in every US metro
Rao AnasRao UsamaRao HuzaifaRao HasnainAbdur Rahman Shah Meet your strategists

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hello@reorank.com

Email us directly

Send us the details

A senior strategist replies within one business day. No spam, no junior account managers.

Goes straight to hello@reorank.com