Agent Studio for Data Teams

Build analytics agents that know your business so anyone can get relevant answers they can trust without waiting in line.

3 layers Of institutional context encoded

  • 14 days To a working, reliable agent
  • Compounds: Accuracy improves with every conversation

Structure

Tables

Warehouse Tables

  • users
  • subscriptions
  • invoices
  • events

Postgres • 4 tables

SQL Patterns

  • SELECT churn_rate...
  • SELECT revenue...
  • SELECT active_users...

3 validated queries

Semantic Models

  • Metrics
  • Dimensions
  • Definitions

3 aligned models

Meaning Notion

Churn Rate Definition
Churn = lost customers / total customers

Context applied

Slack

Do we exclude reactivations?
Yes, within 30 days.

Context applied

Email

Subject
Revenue calculation update
We should exclude refunds after 30 days. Also, only count paid plans in MRR.

Context applied

30+ other sources

Trust

Verified Answers

VERIFICATION
✓ KPI verified
✓ SQL matched
✓ Definition applied

GOLDEN SOURCES

4 approved assets

Usage Signals

USAGE SIGNALS
Used in 12 dashboards
Queried 340× this week

Lineage

LINEAGE
Source → Model → Metric → Answer
Full data trace available

Analytics Agent

Context-Verified
What was our churn rate last quarter?
Churn was 2.1% in Q3 — down 0.4pp vs Q2.
Calculated using your adjusted revenue KPI definition.

KPI-verified

SQL pattern matched

Break it down by product line

Churn by product line, Q3

Product Line Churn Rate
Core 1.8%
Growth 2.4%
Enterprise 0.9%

Based on:
churn.sql
KPI definition

01 / Problem

Every data team hits the same three walls.

  • Your team can't keep up with insight requests.
    47% of the queue is repeat questions: sales wants pipeline. Finance wants burn. Ops wants utilization. You need to scale now, not after a 12-month data modelling project.

  • 95% of POCs fail in production.

02 / Solution

Build Analytics Agents Your Users Trust

A two-sided platform. Builders get a fully observable agent-building studio. End users get visual insights on demand.

01 Bring all your data.

30+ SQL database connectors out of the box. Bring your existing dbt project if you have one, but getting started with Upsolve does not require a pre-built semantic layer.

Supported connections

  • Snowflake
  • BigQuery
  • Redshift
  • Postgres
  • Databricks
  • MySQL
    + 24 more SQL connectors

dbt project import supported

02 Encode context.

Encode everything that generic AI is missing into Upsolve AI's context infrastructure to deliver accurate, relevant, and trustworthy insights that make users stick around.

CONTEXT ARCHITECTURE

  1. Structure the skeleton
  2. Meaning the vocabulary
  3. Trust the judgment

03 Test and Deploy.

Every conversation is traced and captured end-to-end from user question, through to tool calls, SQL queries generated, and agent output. We've opened the blackbox so agent behavior is 100% transparent.

04 Evaluate and Tune.

Built-in evaluation agent grades performance based on a range of performance criteria. Context monitoring automatically surfaces gaps to harden output relevance and correctness; your metrics definition changed, the context layer adapts to it.

03 / End User Studio

AI Data Analyst for Everyone.

Let users chat to their data anywhere and anytime decisions call for evidence.

Conversational analytics
Get answers in real-time.

Every response is grounded in the business logic your data team encoded. Business rules, guardrails, and semantic context applied automatically.

Personal dashboards

Create dashboards with a prompt. Fully interactive and shareable dashboards built for you.

04 / Why Upsolve

How Upsolve Agent Studio Stacks Up.

Competitors encode 1-2 layers. Upsolve covers all six, and is the only platform built around the builder/end-user feedback loop that makes agents improve over time.

Capability Upsolve Competitor 1 Competitor 2
Text-to-SQL tools ✓ — —
Purpose-built AI Analytics platform ✓ — —
3-layered Context Architecture ✓ — —
Out-of-the-box Data Modelling Tool ✓ ◐ ◐
Validated SQL Pattern Encoding ✓ — —
Behavioral Guardrails & Definition ✓ ◐ —
End-user Chat Surfaced to Builder ✓ — —
Comprehensive Agent Tracing & Observability ✓ — —
Deploy anywhere ✓ ◐ —
AI-powered Context Layer Healing ↗ soon — —

05 / Who it's for

Built for data teams that deliver clarity and conviction.

  • Mid-market + Enterprise
    Internal data teams
    Head of Data, Analytics Engineer, BI Lead
    Ad-hoc requests from every department
  • AI & Innovation
    AI & Innovation teams
    Chief AI officer, CDO, innovation lead

06 / Team

By a team that's been there, done that.

Recognized on G2

4.8/5
High Performer
Easiest to Use
Best Support

08 / FAQ

01 What do I need to get started with Upsolve AI?
02 Why can't I just use ChatGPT or Claude with my data?
03 Is my data secure with Upsolve AI?
04 Which AI models does Upsolve use? Are we locked in?
05 Can I self-host?

Can’t find your answer here? Get in touch.