Your first chat

This page walks through a first chat with the agent: what you see when you open it, what happens when you ask a question, how to read the answer, and what it looks like when the agent doesn’t have enough data.

Before you start

The agent works from the data in your workspace, so it needs something to read: one file of your own or a sample dataset. If you haven’t loaded either, follow the Quickstart.

There’s nothing else to set up. You don’t need Dimensions, plans or instructions for a first chat. You may soon want them, and the agent will tell you when they’d help.

If you came in through sign-up, your first chat already has the question you wrote as its first message, and the agent is at work on it. Every chat you start after that opens empty.

1. What you see when you open the agent

Four things are worth knowing before you ask anything.

Part	What it does
The chat input	The Message Agent… field at the bottom. Type a question in plain English and press Enter.
Chat history	The left rail lists your past chats. In a new workspace it holds only your first chat.
File Manager	Opens from the message box. It shows your project’s files, including finished reports in Final Reports, and has the button that publishes a file to Artifacts.
Settings and voice	The other icons under the input open the agent’s settings and turn on voice input. Prompt Designer is one of the agent’s settings. You can skip them in your first chat.

The Help button in the app’s left navigation opens these docs.

2. Ask a real business question

Type a question about your business and send it. For example:

What is driving customer dissatisfaction?

The agent works through the request in four stages, and you can watch each one:

1. It makes a plan. Before answering, it lays out the steps it intends to take as a short task list: what it will inspect, what it will query, and what it will pull together at the end.
2. It inspects your data. It checks which tables, prompts and Dimensions are populated in your workspace, and what dates they cover. This is where it works out whether your question can be answered yet.
3. It runs queries and pulls evidence. It writes and runs the queries it needs: counts, filters, aggregations, transcript searches.
4. It writes back with findings. A headline answer, the method it used, evidence with numbers, quotes from the underlying conversations, and a recommended next step.

Example response

Here is an excerpt of an answer to that question, from a sample workspace of customer support chats. The figures are the example’s, not a benchmark (example from the Dimension Labs docs, May 2026).

Plan
  ✓ Inspect available tables and Dimensions for sentiment
  ✓ Quantify how common dissatisfaction is and break it out by theme
  ✓ Pull representative quotes for each top theme
  ✓ Pull together root causes and recommendations

Across 4,388 conversations (January 1 to February 1, 2026), the biggest drivers of dissatisfaction are billing, promo and credit problems, followed by account access friction. Using a transcript-based proxy, 30.9% of conversations (1,356) show clear dissatisfaction language.

Important caveat: your usual sentiment Dimensions aren’t populated, so this is based on patterns in the transcript text, not your configured Dimensions.

Theme	Mentions	Dissatisfied	Dissatisfaction rate
Billing, promo and credits	1,437	291	20.3%
Account access, login and PIN unlock	1,605	289	18.0%
Setup and activation	520	76	14.6%

Representative quotes:

• “BOGO promo isn’t applying… being charged the full EIP…”
• “I just want to pay my bill, not troubleshoot an app.”
• “I canceled… but I’m still being charged $55.”

A full response goes on to weekly trend charts, more themes and recommended actions.

Project: Lumen Coffee · reviews

You

Which stores get the most one-star reviews, and why?

Agent

1. Find the reviews and their Dimensions
2. Count one-star reviews by store
3. Read the reasons behind them
4. Rank the reasons by effect, not volume

Mission St64

Harbor41

Oak Ave33

Elm Park19

Union Sq12

one-star reviews

FindingMission St gets the most one-star reviews. Wait time is the reason in most of them, and it is the one that moves the rating.

How an agent session runs: question, plan, queries, finding.

3. Read the answer

Answers follow roughly the same shape. Once you’ve read one, the rest are quicker to scan.

Part	What to look for
Headline first	One or two sentences with the main finding and the top-line numbers.
Method disclosed	If the agent used a proxy or worked around missing data, it says so, with wording like “based on transcript patterns, not configured Dimensions.”
Evidence with numbers	Counts, percentages and time windows. Every claim comes with the number behind it.
Quotes	Direct quotes from the underlying conversations. They are the rawest signal, so read them.
Recommended next step	Usually either a deeper question to ask, or a setup step that would make the next answer more reliable.

If something looks off, ask “Show me the SQL you ran” or “How did you calculate that?”. You can ask for the conversations behind any number.

4. When the agent doesn’t have enough data

The agent would rather tell you it can’t answer than guess. When it can’t, it says exactly what’s missing and what to do about it.

Example refusal

I can’t yet answer “what’s driving dissatisfaction” from your labeled Dimensions, because the tables that normally hold dissatisfaction and driver Dimensions are empty in your workspace.

What I checked

• system_session_prompt (expected sentiment, rating and reason Dimensions): 0 conversations
• support_reasons (your custom prompt; expected issue category and predicted CSAT): 0 conversations
• transcripts (raw transcript text): 4,388 conversations

What I can do next

Run a lighter theme extraction directly from the transcript text, which is less reliable but available now, or wait until a backfill has run the Dimensions on the full dataset.

Table names depend on your workspace: support_reasons here stands for a table named after one of your own prompts. See What the agent can see.

From here you have two options:

1. Tell the agent to use the available data. It takes the interim path. That’s how the example answer above was produced, and why it carries a caveat.
2. Fill the gap, then ask again. Run a backfill from the Backfill tab. An uploaded file and any new prompt need one, and the standard Dimensions (System Prompts) run every day only when Dimension Labs has turned on daily enrichment for your workspace. A backfill typically shows up within the hour, depending on how much data is being enriched. See When enrichment runs.

Trust the refusals

Many analytics tools fail quietly and give you a number anyway. The agent fails out loud: it tells you what’s missing, then offers a way forward.

5. Optional: explore before you analyze

If you’d rather see what the agent can work with before asking an analytical question, start with discovery questions:

• What’s in my workspace?
• List my prompts.
• List my Dimensions.
• What metadata do I have?
• What Dimensions do I have for sentiment?
• Do we track competitor mentions anywhere?

The agent lists what’s available and tells you which kinds of questions it can answer well. More discovery questions are in What the agent can see.

It’s a conversation

You can refine, follow up, change direction or start over. Treat the agent like a colleague: give it context, push back when an answer feels off, and ask why as often as you need to. See Ask good questions.

Next

• Quick answers and analysis plans: when to explore and when to build a repeatable deliverable.
• Ask good questions
• Reports, decks and sharing