For business leaders, not engineers

AI that answers from your company's own documents, explained in plain English.

You've been told your company needs to use AI. These short guides help you decide where it pays off, what it really costs, what can go wrong, and how it works, written by people who have built one.

Start with the business questions See how it works, step by step

The short answer

What is this, in one sentence?

It's an AI assistant that looks things up in your company's own documents before it answers, and shows you where it found the answer.

The everyday picture: an open-book exam. Instead of answering from memory, the AI answers from your books, and shows the page. Engineers call this RAG, short for retrieval-augmented generation: "retrieval" is the looking up, "generation" is the writing of the answer.

Start here

The questions leaders actually ask

Each one gets a short guide: a page you can read in a few minutes, and a video you can watch in less.

Where would this actually pay off?

The jobs it does well, the ones it doesn't, and how to pick a first project.

2 guides ready →

What will it really cost?

The bill nobody quotes you, a business case your finance team will accept, and why pilots stall.

1 guide ready, 2 coming →

What's our legal exposure?

Where your data goes, who's responsible when it's wrong, and which rules apply. The questions to ask your lawyer.

Worried it could leak between departments, or show people files they shouldn't see? See SecureLLM.ai.

3 guides coming

Is our information ready?

Messy shared drives, old versions and documents nobody owns, and what to fix first.

Guide ready →

Can we trust the answers?

How often it will be wrong, how you'll know, and when a person must check.

Guide ready →

Build it, buy it, or get help?

The real trade-offs, the questions to ask in a vendor pitch, and how to avoid getting locked in.

3 guides coming

Will people actually use it?

Who should own it, what training it takes, and why good tools still go unused.

1 guide coming

How do we get started?

What a sensible first project looks like: the team, the timeline, the budget, and what success means.

Guide ready →

Why this matters

Most AI projects stall before they start. Rarely because of the AI.

87%

of IT and security leaders have delayed a generative AI rollout over data security and how information is managed. The delays were about readiness, not budget.

Source: AvePoint, The State of AI 2026, a survey of 750 IT, security and AI leaders. The survey's sponsor sells data-security software.

Messy information is the most-named hurdle, ahead of fragmented systems and security

Data quality and consistency59%
Fragmented data across systems50%
Security and privacy50%

Source: K2view, GenAI adoption survey 2026, 300 organizations, top concerns in using company data for AI. The shares add up to more than 100%, so organizations named several. The survey's sponsor sells data-management software.

How it works

From your documents to a trustworthy answer, in six steps

Most systems of this kind follow much the same path. Click a step to read about it, or start at the beginning.

Read how it works, step by step Watch the 5-minute video

Who we are

Written by people who have built one

We built Arro, an AI platform that answers from a company's own documents. That means we've done every step on this page ourselves: reading messy files, building the catalogs, getting the right passages to the top, and keeping the answers honest.

Plain English first. Technical terms only when you'll hear them in a meeting, and always explained.
Every fact has a source you can check.
We never rank or score our own products. Where we describe how we built something in Arro, it's labeled.

When you're ready

Planning an AI project? Talk to the people who wrote this.

Moves and Measures helps companies choose the right first project, check their information is ready, build the business case, and run a pilot that proves it.

Email Moves and Measures