Pricing

Docs

Request demo

Agent Studio for Data Teams

Build analytics

agents that know

your

edge cases. 

So anyone can get relevant answers they can trust without waiting in line.

Request demo

See how it works

3 layers

Of institutional

context encoded

7 days

To a working,

reliable agent

Compounds

Accuracy improves

with every

conversation

Structure

Tables

Warehouse Tables

users
subscriptions
invoices
events

Postgres • 4 tables

SQL

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 →

churn_rate

by product_line

Q3 2025

Core

1.8%

Growth

2.4%

Enterprise

0.9%

Based on:

churn.sql

KPI definition

Ask anything about your data...

01 / Problem

Every data team hits the same three walls.

  • 95%

    POCs fail in prod

    Demo

    Revenue Q4

    Pipeline

    Churn Rate

    Production

    Wrong KPI

    Hallucinated

    No Guardrail

    02

    Looks right in the demo. Fails in production.

    AI hallucinations kill trust. It's not a model problem, it's a context problem.

  • All Connected

    None of it structured

    Notion

    Linear

    SQL File

    Fireflies

    sheets

    dbt repo

    03

    Context makes data meaningful. Engineering it is no simple task.

    Orchestrating institutional knowledge is messy business. It's scattered across tools, docs, repos, and people's heads.

  • sales

    finance

    OPS

    Marketing

    Product

    data team

    1 analyst

    47%

    Repeat Questions

    2-4

    Weeks of Wait Time

    01

    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%

    POCs fail in prod

    Demo

    Revenue Q4

    Pipeline

    Churn Rate

    Production

    Wrong KPI

    Hallucinated

    No Guardrail

    02

    Looks right in the demo. Fails in production.

    AI hallucinations kill trust. It's not a model problem, it's a context problem.

  • All Connected

    None of it structured

    Notion

    Linear

    SQL File

    Fireflies

    sheets

    dbt repo

    03

    Context makes data meaningful. Engineering it is no simple task.

    Orchestrating institutional knowledge is messy business. It's scattered across tools, docs, repos, and people's heads.

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

01

Structure

the skeleton

02

Meaning

the vocabulary

03

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.

Operations Analyst Agent

Completed

Total: 4.2s

0s

1s

2s

3s

4s

AGENT

OpsAnalystAgent

4.2s

TOOL

query_warehouse

0.8s

CHAIN

analyze_metrics

1.5s

LLM

gpt-4o — anomaly_…

0.6s

LLM

gpt-4o — summari…

0.7s

TOOL

validate_context

0.5s

LLM

gpt-4o — recommend

0.8s

TOOL

push_to_dashboard

0.2s

Tokens

2,847 in · 1,204 out

Context

6 layers verified

Gaps

0 detected

SHIP & VERIFY

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.

FEEDBACK LOOP

01

USERS

CHAT

Conversations captured

02

GAPS SURFACED

AI finds what's missing

03

CONTEXT FIXED

Gaps encoded → better

accuracy grows

request demo

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 — no

prompt engineering from end users.

Analytics Agent

Context-Verified

What's our Q4 pipeline coverage?

Pipeline is 3.2x quota at $4.2M across 38 open deals.

Business rule applied

Guardrail respected

Which reps are under-covered?

3 reps below 2x threshold:

Chen (1.4x), Park (1.7x), Williams (0.9x).

Williams flagged — pipeline dropped 40% this week.

Alert threshold met

Ask anything about your data...

Teams

Claude

ChatGPT

Cursor

MCP

Slack

Your app — embed via SDK

Multi-surface deployment

Meet users where they already are.

Deploy your analytics agent to Slack, Teams, Claude, ChatGPT, Cursor, or

embed it directly in your product via MCP. Same context, same guardrails,

every surface.

Personal dashboards

Create dashboards with a prompt.

Fully interactive and shareable dashboards built for you.

How this works →

Personal Dashboard

+

Add chart

Key Metrics

Revenue by region

Q4 pipeline

3.2x

$4.2M · 38 deals

Churn rate

2.1%

↓ 0.3pp MoM

Drop chart here

request demo

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

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 (intranet, SaaS, MCP)

✓

◐

—

—

AI-powered Context Layer Healing

↗ soon

—

—

—

✓ native · ◐ partial / in progress · — not present

request demo

05 / Who it's for

Built for data teams that deliver clarity and conviction.

Most tools stop at visualization. We go further — turning data into decisions your team can trust and act on.

Mid-market + Enterprise

Internal data teams

Head of Data, Analytics Engineer, BI Lead

Ad-hoc requests from every department

Tried AI tools. Got hallucinations.

Can't grow the team. Need to scale output.

No time for a 12-month build project

AI & Innovation

AI & Innovation teams

Chief ai officer, cdo, innovation lead

Every team has a different view of the truth.

Drowning in dashboards. Still no clear answers.

Can't connect data initiatives to business outcomes.

Org doesn’t trust AI outputs to act.

06 / Team

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

Built on trust you don’t have to question

Compliance

Backed by

Batch w24

investor

built by a team from

Proof that goes beyond promises

Recognized on G2

4.8

/5

High Performer

Easiest to Use

Best Support

Fall 2025

Trusted by Fortune 500 teams

Enterprise Ready

Scales Globally

Security First

Mission-Critical

request demo

08 / FAQ

FAQs

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.

Stop answering the same 10 questions today.

talk to our team

The Platform for Accurate, Reliable, and Trustworthy AI Analytics.

© 2026 Upsolve AI, Inc.  

Privacy

| 

Terms