Talk to your data

Simplify interaction between your teams and your data

Every time someone needs a policy detail, a client history, or a project spec, they search, scroll, and check with whoever might remember. This turns that into one question and one answer with its source. It runs on retrieval-augmented generation (RAG), so every answer comes from your own documents and names the page it came from. Your team stops hunting and gets back to the work.

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Bridge the gap between your teams and your data.

Getting to the data is slow, so people answer from memory instead. General AI answers with words that were never in your documents. Mistakes follow, hours go, and decisions get made on the wrong answer.

The old way

You search folders and hope someone remembers.

Open the folder.
Search for the right file.
Ask the one person who might remember.
Wait for a reply.

The way with this

You ask a question and get the answer.

Ask.
Read the answer.
Check the page it came from.

What published research shows

This happens every week

81% of employees could not find information they needed at a critical moment. For 27% of them it happens every week.

Your people hit this on the questions that could not wait.

Coveo, 2022

General AI makes it worse

Asked verifiable questions about real court cases, general models fabricated answers between 58% and 88% of the time.

Ask a general tool for an exact detail and it will invent one.

Journal of Legal Analysis, 2024

Most people never check

66% of people rely on AI output without checking whether it is accurate. 56% say they are making mistakes at work because of AI.

Your team acts on the answer, then pays for it in their own work.

KPMG and University of Melbourne, 2025

Trust drops where stakes rise

9% of workers trust AI for business-critical decisions. Among executives, 61% do.

The people closest to the work trust it least, where a wrong answer costs most.

WalkMe, 2026

These numbers aren't about the industry. They're about your last meeting, your last report, your last decision made without a check.

Organizations that already work this way

These companies use retrieval-augmented generation inside their own systems. Here is what each one uses it for.

RAG (retrieval-augmented generation) now dominates at 51% adoption, a dramatic rise from 31% last year.

Menlo Ventures, 2024

Morgan Stanley

Advisors ask an internal document library and get the answer back.

JPMorgan Chase

A client assistant answers questions about payments reporting.

Citi

Developers get answers drawn from the bank's own code repository.

Allianz

Call-center staff answer from the company's own knowledge library.

Thomson Reuters

Legal drafting grounded in its own Practical Law content.

Bloomberg Law

Legal research answered from its own editorial content.

Toyota

Departments search their own engineering and regulatory documents.

Blue Origin

Engineering knowledge bases answer questions across the company.

Uber

An on-call assistant answers security and privacy policy questions.

Dropbox

Search and knowledge management across a business's own content.

Customer service answers drawn from past support tickets.

Atlassian

Research reports built from a company's own project and page content.

What a grounded answer looks like

A good answer doesn't just sound right. It ends one of three ways, every time, and you can tell which.

The document had the answer, and it's shown to you alongside the page it came from.

Two documents disagree. Both are shown side by side, so a person decides which one applies.

The documents don't cover it. That gets said plainly, instead of a guess dressed up as an answer.

Ask one of these three questions. You get a clear answer, two sources that disagree, or a plain no answer at all.

Choose a question to begin.

Supercharge your teams today

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