Agent Studio for Data Teams
PLATFORM
Resources
PLATFORM
Resources
Agent Studio for Data Teams
Build analytics
agents that know
Build analytics
agents that know
your
your
So anyone can get relevant answers they can trust without waiting in line.
See how it works
See how it works
3 layers
3 layers
Of institutional
context encoded
Of institutional
context encoded
7 days
7 days
To a working,
reliable agent
To a working,
reliable agent
Compounds
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...
sales
finance
OPS
Marketing
Product
data team
1 analyst
47%
Repeat Questions
2-4
Weeks of Wait Time
01
95%
POCs fail in prod
Demo
Revenue Q4
Pipeline
Churn Rate
Production
Wrong KPI
Hallucinated
No Guardrail
02
-
All Connected
None of it structured
Notion
-
sales
finance
OPS
Marketing
Product
data team
1 analyst
47%
Repeat Questions
2-4
Weeks of Wait Time
01
-
95%
POCs fail in prod
Demo
Revenue Q4
Pipeline
Churn Rate
Production
Wrong KPI
Hallucinated
No Guardrail
02
-
01 / Problem
Every data team hits the same three walls.
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.Looks right in the demo. Fails in production.
AI hallucinations kill trust. It's not a model problem, it's a context problem.
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.
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.Looks right in the demo. Fails in production.
AI hallucinations kill trust. It's not a model problem, it's a context problem.
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
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.
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
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
Operations Analyst Agent
Completed
Completed
Total: 4.2s
Total: 4.2s
0s
0s
1s
1s
2s
2s
3s
3s
4s
4s
AGENT
AGENT
OpsAnalystAgent
OpsAnalystAgent
4.2s
4.2s
TOOL
TOOL
query\_warehouse
query\_warehouse
0.8s
0.8s
CHAIN
CHAIN
analyze\_metrics
analyze\_metrics
1.5s
1.5s
LLM
LLM
gpt-4o — anomaly\_…
gpt-4o — anomaly\_…
0.6s
0.6s
LLM
LLM
gpt-4o — summari…
gpt-4o — summari…
0.7s
0.7s
TOOL
TOOL
validate\_context
validate\_context
0.5s
0.5s
LLM
LLM
gpt-4o — recommend
gpt-4o — recommend
0.8s
0.8s
TOOL
TOOL
push\_to\_dashboard
push\_to\_dashboard
0.2s
0.2s
Tokens
Tokens
2,847 in · 1,204 out
2,847 in · 1,204 out
Context
Context
6 layers verified
6 layers verified
Gaps
Gaps
0 detected
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
03 / End User Studio
AI Data Analyst for Everyone.
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
Analytics Agent
Context-Verified
Context-Verified
What's our Q4 pipeline coverage?
What's our Q4 pipeline coverage?
Pipeline is 3.2x quota at $4.2M across 38 open deals.
Pipeline is 3.2x quota at $4.2M across 38 open deals.
Business rule applied
Business rule applied
Guardrail respected
Guardrail respected
Which reps are under-covered?
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
Alert threshold met
Ask anything about your data...
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.
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
04 / Why Upsolve
How Upsolve Agent Studio Stacks Up
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
05 / Who it's for
Built for data teams that deliver clarity and conviction.
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.
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
08 / FAQ
FAQs
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.
The Platform for Accurate, Reliable, and Trustworthy AI Analytics.
Agent Studio for Data Teams. Encode context. Deploy agents. Deliver clarity.
© 2026 Upsolve AI, Inc.
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