Upsolve AI - Embed GenBI in Your Product

Product

Solutions

How it works

Pricing

Docs

Get early access →

Product

Solutions

How it works

Pricing

Docs

Get early access →

Get early access →

Agent Studio for Data Teams

Build analytics

agents that know

Build analytics

agents that know

your

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

Get early access →

See how it works →

6 layers

Of institutional

context encoded

Of institutional

context encoded

14 days

To a working,

reliable agent

To a working,

reliable agent

Compounds

Accuracy improves

with every

conversation

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

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...

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

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.

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.

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.

Bring all your data.

30+ connectors out of the box. Bring your existing dbt project.

Semantic layer optional.

30+ connectors out of the box. Bring your existing dbt project. Semantic layer optional.

Supported connections

Snowflake

BigQuery

Redshift

Postgres

Databricks

MySQL

+ 24 more SQL connectors

dbt project import supported

Encode context.

Encode everything that generic AI is missing: KPIs, SQL patterns, business

rules, guardrails. Deliver relevant, accurate insights that make users stick

around.

CONTEXT ARCHITECTURE

Structure

the skeleton

Meaning

the vocabulary

Trust

the judgment

Test and Deploy.

—

Stress-test agents before users see them

—

Full chat history and prompt traces. No black box, full observability.

—

AI diagnostics and context gap detection

—

Deploy to wherever users already are. In your product, your intranet, or

piped directly into the AI tools they use every day.

Operations Analyst Agent

Completed

Total: 4.2s

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

Tune and Improve.

Full chat history captured from end users

User feedback surfaced to the builder

AI-generated context improvement suggestions

Gaps identified before they destroy trust

FEEDBACK LOOP

USERS

CHAT

Conversations captured

GAPS SURFACED

AI finds what's missing

CONTEXT FIXED

Gaps encoded → better

accuracy grows

Get early access →

03 / End User Studio

AI Business Analyst for every user.

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.

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.

Explore additional features →

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

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

Book 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

Full 6-layer structured context architecture

✓

—

Works without a semantic layer

✓

◐

Validated SQL pattern encoding

✓

—

Behavioral guardrails & scope rules

✓

◐

—

End-user chat surfaced to builder

✓

—

Agent evaluation / golden query testing

✓

—

Deploy anywhere (intranet, SaaS, MCP)

✓

◐

—

AI-suggested context improvements

↗ soon

—

Purpose-built AI agent platform

✓

—

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

05 / Who it's for

Built for data teams that deliver clarity.

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

High Performer

Easiest to Use

Best Support

Fall 2025

Trusted by Fortune 500 teams

Enterprise Ready

Scales Globally

Security First

Mission-Critical

What people say

We had a semantic layer. The AI was still giving

wrong numbers. Upsolve was the first tool that let

us encode the actual business logic.

We had a semantic layer. The AI was still giving wrong numbers. Upsolve was the first tool that let us encode the actual business logic.

Head of Analytics · Series C SaaS

For the first time, our sales team can answer their

own data questions. The ticket queue dropped

70%. My team actually has time to build now.

For the first time, our sales team can answer their own data questions. The ticket queue dropped 70%. My team actually has time to build now.

BI Lead · Enterprise Fintech

We shipped an AI analytics feature to our

customers in six weeks. Our data team spent a

week in the Studio. Everything else was Upsolve.

VP Product · B2B Analytics Platform

We shipped an AI analytics feature to our customers in six weeks. Our data team spent a week in the Studio. Everything else was Upsolve.

VP Product · B2B Analytics Platform

08 / FAQ

FAQs

What do I need to get started?

Why can't I just use ChatGPT or Claude with my data?

Is my data secure?

Which AI models does Upsolve use? Are we locked in?

Can I self-host?

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

Stop answering the same 10 questions today.

Get early access →

Agent Studio for Data Teams.

Encode context. Deploy agents.

Deliver clarity.

Agent Studio for Data Teams. Encode context. Deploy agents. Deliver clarity.

Get early access →

Privacy

Terms