What is a credit in Upsolve AI?

A credit represents the simplest AI response. Simple chitchat costs 1 credit, basic data lookups cost 3-5, and deep analytical explorations can cost up to 20 credits.

Which data sources does Upsolve AI support?

It supports over 50 data connections, including major warehouses like Snowflake, BigQuery, Redshift, and Databricks, as well as SQL databases like Postgres and MySQL.

How long does it take to deploy a reliable data agent?

Upsolve AI is designed for speed, typically allowing teams to go from data connection to a working, reliable agent in approximately 7 days.

Does Upsolve AI support multi-tenancy?

Yes, multi-tenant support and row-level security are available on the Team and Enterprise plans, ensuring users only see the data they are authorized to access.

Can I self-host Upsolve AI?

Self-hosting, on-premises, and VPC (Virtual Private Cloud) deployments are available for Enterprise customers with specific security or regulatory needs.

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Customer analysis

Freemium

Upsolve AI

Embed AI-powered analytics and data agents into your SaaS product. Let users query data in natural language and access personalized dashboards with governed, trustworthy insights.

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Rating

Reviews

Upvotes

3 months ago

Listed

Customer analysis

Embedded Analytics

Business Intelligence

Generative BI

Data Visualization

Natural Language Query

AI Data Agent

SaaS Analytics

Semantic Layer

Tool information

Provider

Upsolve Labs, Inc.

Platforms

Web

Slack

Microsoft Teams

Languages

English

API Available

Yes

Added to directory

3 months ago

Last updated

about 2 months ago

Rating

Not available

Pricing

Freemium

About Upsolve AI

What the tool does and who it's for

Upsolve AI is a purpose-built analytics platform designed to embed governed, AI-powered data agents and dashboards into SaaS applications. It allows product teams to provide end-users with hyper-personalized analytics using a natural language interface. The platform utilizes a unique three-layered context architecture to reduce AI hallucinations and ensure that insights are grounded in specific business logic. Key features include an 'Agent Studio' for data modeling, an 'AI Cockpit' for semantic layer generation, and multi-surface deployment options such as React components, iframes, and Slack/Teams integrations. By automating the translation of natural language to SQL and Python, it enables non-technical users to perform complex data exploration and 'slice-and-dice' analysis without manual querying.

Key capabilities

AI Agent Studio: Build and tune data agents that understand specific business KPIs and institutional context.

Natural Language Exploration: Enables end-users to query data, apply filters, and 'drill down' using plain English.

Hyper-personalized Dashboards: Dynamically generates interfaces and surfacing KPIs based on the user's role and persona.

Three-Layered Context Architecture: Bridges the gap between raw data and AI to ensure high accuracy and trust.

Multi-surface Deployment: Embed via React components, iframes, or deploy to Slack, Teams, and MCP apps.

AI-Guided Insights: Automatically flags important data signals, trends, and anomalies for proactive user alerts.

Governance & Security: Includes row-level security (RLS), RBAC, and SOC 2 compliance for enterprise-grade data handling.

Semantic Layer Modeling: Built-in tools to unify and curate context from SQL files, dbt repos, and documentation.

Pricing

Freemium — plans below

Free

2,000 one-time credits
Full test drive of Pro features
50+ data connections
Unlimited agents
Community support

Starter

500

2,000 credits per month
Charts & visualization
Context management suite
Full observability & eval suite
Priority email support

Team

Enterprise

Contact sales

On-prem / VPC deployment
SAML SSO & HIPAA compliance
Model routing (BYOM)
Forward Deployed Engineering
24/7 dedicated support & shared Slack

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