Supper
PRODUCT DETAILS
No. 03  ·  May 2026
Product · Agent interactions

A data analyst on call  24 x 7, in plain English.

Ask Supper a business question the way you'd ask a colleague. The agent navigates your database, applies your business logic, writes SQL or python code, executes it, and shows its work — all in under a minute.

What it is Conversational data agent
Average reply ~10–25 seconds
Where it lives Web · Slack · MCP
Shows its work Full audit trail on every response
§ 00

Less dashboard.
More conversation.

Just like talking to an (expert) data analyst

The Supper agent is a conversational interface to your entire data stack. Type a question — about the sales pipeline, churn, product usage, anything you've connected — and you get back a verified answer, the working behind it, and the next questions worth asking.

No dashboard to navigate. No SQL to write. No analyst queue to wait in. Just a the answer you need to get back to work.
§ 01

The anatomy of a Supper answer.

Walk-through · A real question, end to end
A.
Ask

Type the question. Skip the schema spelunking.

A user asks for monthly shipment counts for a specific customer. The agent matches the customer name in the database, finds the entities involved, and decides what to pull. No table names, no joins, no SQL required.

  • Plain-English questions, full sentences welcome
  • Entity matching across every connected source
  • Results materialize into a named, shareable artifact
app.supper.co · ask · monthly shipments
User asks for total shipments per month for Alaskan Salmon Company; the agent confirms it found the entities, thought for 23 seconds, and produced a 'Monthly shipments' artifact.
Fig. A · Question in. Entities found. Artifact named.
B.
Reason

Watch it think. Inspect every choice.

Expand the reasoning trace and you see exactly which sources were pulled, which business-logic rules were applied, and the SQL the agent wrote. Every step is open for inspection — by the asker, by the data team, by anyone reviewing the answer later.

  • Pulled data sources listed by name (HubSpot, MSSQL, multi-schema)
  • Applied business logic shown as the rules that fired
  • Generated SQL or Python visible inline — copy, audit, override
app.supper.co · reasoning trace
Expanded reasoning trace showing pulled sources, applied business logic about preferred reporting tables and shipped-date use, the determined execution plan, and the generated SQL.
Fig. B · Sources, logic, and SQL — all visible inline.
C.
Answer

Get an agentic summary, full answer data, and thoughtful next steps

The final answer leads with the insight in human language, surfaces the data quality notes that matter (a null bucket, a missing date), and proposes the next questions worth asking. 

  • Headline insight — written, not just charted
  • Data-quality flags raised inline, not buried
  • Suggested follow-ups — one click to keep going
app.supper.co · answer · results
Final answer panel: insight summary, monthly rollup data, data-quality note about a null month bucket, and three suggested next-step questions.
Fig. C · Insight, caveats, and follow-ups.
§ 02

Admin review keeps the data team in control and makes future answers better.

Review · Feedback · Memory

Every conversation lands in the Question Review queue. Your data team can endorse a question, correct a definition, or flag a source — and the agent applies the lesson to every future question that touches the same logic.

  • Centralized review queue across every team and channel
  • Endorsed answers become canonical for that metric
  • Corrections propagate into the business-logic layer
  • Every question shows owner, source, and review status
app.supper.co · question review
Question Review page listing recent agent questions, their owners, and the data sources used — searchable and filterable for the data team to review and endorse.
Fig. 02 · Every question, reviewable by the data team.
§ 03

The same agent, wherever you work.

Use Supper wherever you do your best work
Web app
The full agent experience.
Reasoning traces, artifacts, follow-up threads, and one-click sharing of any answer to teammates.
→ app.supper.co
Slack
Where the question gets asked.
@supper in any channel. Same agent, same business logic, threaded back into your team's existing conversation.
→ supper.co/slack
MCP
From Claude. From any AI.
Connect Supper as a tool to Claude, Claude Code, or any MCP-compatible client. Verified data, anywhere.
→ See MCP one-pager
Also reachable via
API Webhooks Workflows Dashboards + scheduled reports
§ 04

Common questions.

FAQs from other Supper customers
Do I need to know SQL or Python to use Supper?
No. The agent matches entities, picks tables, and writes the code for you. If your question is ambiguous, it asks back before guessing.
How long does a typical answer take?
About 10–25 seconds for most questions. Complex multi-source questions can take a minute; the agent streams its progress so you see what's happening.
Can I trust the answer without checking it?
Yes — and you can also verify in one click. Every answer ships with the raw code, the sources, and the business logic applied. Endorsed answers are flagged for the team.
What about questions about people or PII?
Permissions are enforced at the query level. The agent can only return data the asker is already authorized to see. PII columns can be masked or excluded entirely.