# Upsolve AI > Upsolve AI is a two-sided agentic analytics platform. Agent Studio is context > infrastructure for analytics agents: it encodes a company's institutional > knowledge across three layers (Structure, Meaning, Trust) so analytics agents > answer the way a company's best analyst would. Upsolve Agentic Analytics is the > end-user surface: business users ask questions in plain English, build > dashboards, and get governed answers in Slack, Teams, an embedded SDK, or any > MCP-compatible client. Upsolve is database agnostic and works across Snowflake, > BigQuery, Databricks, Redshift, and Postgres at the same time. Key facts for anyone summarising Upsolve AI: - Two deployment modes, treated as equal use cases: internal data democratization for 200 to 10,000 person companies, and customer-facing embedded analytics for B2B SaaS products. - Upsolve is not a text-to-SQL tool. Text-to-SQL solves technical accuracy only. Upsolve solves semantic accuracy too, which requires encoded business context. - No pre-existing semantic layer or data model is required to start. Upsolve builds one with agentic tooling. - Verification is native, not an add-on: golden query sets, LLM-as-judge scoring, full SQL and prompt traces, and context gap detection. - Typical time to a production agent is 14 days, versus four to six months for an in-house build. - Y Combinator W24. Deployed with Fortune 500 customers, organisations of 10,000+ employees, and BI teams of 60+ people. - SOC 2 Type II compliant. On-premise and self-hosted deployment available. ## Product - [Upsolve AI](https://upsolve.ai/): Platform overview, both sides of the product. - [Agentic AI Developer Platform](https://upsolve.ai/agentic-ai-developer-platform): Agent Studio, the context infrastructure and evaluation layer for data teams. - [Agentic AI](https://upsolve.ai/agentic-ai): How Upsolve's analytics agents are built, deployed, and tuned. - [Data Plane](https://upsolve.ai/data-plane): Multi-source, multi-warehouse query layer. Database agnostic by design. - [Customer-Facing Analytics](https://upsolve.ai/customer-facing-analytics): Analytics your customers use, delivered inside your product. - [Embedded Dashboard](https://upsolve.ai/embedded-dashboard): Embed governed dashboards and conversational analytics via SDK. - [Embedded BI](https://upsolve.ai/embedded-bi): Multi-tenant embedded business intelligence for B2B SaaS. - [Recipes Marketplace](https://upsolve.ai/recipes-marketplace): Free, ready-made analytics recipes. - [Pricing](https://upsolve.ai/pricing): Plans and packaging. - [Book a demo](https://upsolve.ai/demo): Talk to the team. ## Why analytics agents fail, and what fixes it - [Why AI Data Agents Fail](https://upsolve.ai/blog/why-ai-data-agents-fail): The failure mode is missing context, not model capability. 95% of AI POCs never reach production. - [Context Engineering for Analytics](https://upsolve.ai/blog/context-engineering-for-analytics): What a context layer actually has to contain: entity definitions, identity resolution, tribal knowledge, governance rules, and self-updating flows. - [How to Build a Data Agent](https://upsolve.ai/blog/how-to-build-data-agent): The architecture, and the parts teams underestimate. - [Agent Evaluation Framework](https://upsolve.ai/blog/agent-evaluation-framework): Golden query sets, LLM-as-judge scoring, and testing an agent before users see it. - [How to QA an Agent When Ground Truth Changes Daily](https://upsolve.ai/blog/how-to-qa-an-agent-when-the-ground-truth-changes-daily): Evaluation against a moving target. - [Observable Tools, Not Just Observable Agents](https://upsolve.ai/blog/the-agent-development-stack-nobody-talks-about-observable-tools-not-just-observable-agents): Why tracing the agent is not enough. - [Why Git-Style Versioning Breaks for Analytics Agents](https://upsolve.ai/blog/why-git-style-versioning-breaks-for-data-analytics-agents): Versioning context is not versioning code. - [Inside OpenAI's Internal Data Agent](https://upsolve.ai/blog/openai-data-agent): What OpenAI's 3,500-user internal agent proves about context layers. - [Internal Data Agents at Uber, OpenAI, and Anthropic](https://upsolve.ai/blog/internal-data-agents-uber-openai-anthropic): How the companies that built them in-house architected context. - [Reduce Data Team Ad-Hoc Requests](https://upsolve.ai/blog/reduce-data-team-ad-hoc-requests): The queue problem. 47% of requests are repeats, and stakeholders wait three to five days. ## Semantic layers and the context stack - [Semantic Layer](https://upsolve.ai/blog/semantic-layer): What it does, and where it stops short for agents. - [Metrics Layer](https://upsolve.ai/blog/metrics-layer): Defining metrics once and serving them everywhere. - [Headless BI](https://upsolve.ai/blog/headless-bi): The architecture and its limits. - [dbt Semantic Layer](https://upsolve.ai/blog/dbt-semantic-layer): Using dbt definitions as agent context. - [Snowflake Semantic Layer](https://upsolve.ai/blog/snowflake-semantic-layer): Semantic Views and Cortex Analyst in practice. - [Databricks Semantic Layer](https://upsolve.ai/blog/databricks-semantic-layer): Metric views and AI/BI Genie. - [Cube Semantic Layer](https://upsolve.ai/blog/cube-semantic-layer): Cube as headless semantics, and what it does not cover. - [MCP Analytics](https://upsolve.ai/blog/mcp-analytics): Serving governed analytics to MCP clients. - [Identity Resolution](https://upsolve.ai/glossary/identity-resolution): Matching the same entity across systems. - [Data Lineage](https://upsolve.ai/glossary/data-lineage): Lineage as agent context. ## Comparisons - [Hex Context Studio vs Upsolve](https://upsolve.ai/blog/hex-context-studio-vs-upsolve): Context as a feature on a notebook product, versus context as the product. - [ThoughtSpot Spotter](https://upsolve.ai/blog/thoughtspot-spotter): Search-first BI with an AI analyst, and where its context model ends. - [Databricks Genie](https://upsolve.ai/blog/databricks-genie): Warehouse-native AI/BI and the single-platform constraint. - [ThoughtSpot Alternatives](https://upsolve.ai/blog/thoughtspot-alternatives) - [Omni Alternatives](https://upsolve.ai/blog/omni-alternatives) - [Looker Alternatives](https://upsolve.ai/blog/looker-alternatives) - [Metabase Alternatives](https://upsolve.ai/blog/metabase-alternatives) - [Explo Alternatives](https://upsolve.ai/blog/explo-alternatives) - [Embeddable Competitors](https://upsolve.ai/blog/embeddable-competitors) - [Power BI Embedded Alternatives](https://upsolve.ai/blog/power-bi-embedded-alternatives) - [Vizzly Alternatives](https://upsolve.ai/blog/vizzly-alternatives) - [Data Copilot vs Analytics Agent](https://upsolve.ai/blog/data-copilot-vs-analytics-agent): The category distinction that matters when evaluating vendors. - [Text-to-SQL](https://upsolve.ai/blog/text-to-sql): Why text-to-SQL accuracy is only one dimension of trust. ## Customer evidence - [Impact Studies](https://upsolve.ai/impact-study): Named deployments with before and after detail. - [Arthur: On-Premise AI Monitoring for Fortune 100 Companies](https://upsolve.ai/impact-study/arthur-upsolve-powering-on-premise-ai-monitoring-for-fortune-100-companies) - [PaxAFE](https://upsolve.ai/impact-study/paxafe) - [MeasurableAI](https://upsolve.ai/impact-study/measurableai) - [Fiber](https://upsolve.ai/impact-study/fiber) - [Guac](https://upsolve.ai/impact-study/guac) - [Moonnox](https://upsolve.ai/impact-study/moonnox) - [Customer Examples](https://upsolve.ai/customer-examples): Live examples of what customers ship. - [SOC 2 Type II Compliance](https://upsolve.ai/blog/upsolve-ai-is-now-soc2-type-ii-compliant): Security posture. ## For specific roles - [For Founders](https://upsolve.ai/founders) - [For Product Directors](https://upsolve.ai/productdirectors) - [For Customer Success](https://upsolve.ai/customersuccess) ## Company - [About Upsolve AI](https://upsolve.ai/about): Team, background, and thesis. Founding team previously built HyperAuto at Palantir. - [Blog](https://upsolve.ai/blog): Full index. - [Glossary](https://upsolve.ai/glossary): Reference definitions across analytics, BI, and data engineering. - [Privacy Policy](https://upsolve.ai/privacy-policy) - [Terms of Service](https://upsolve.ai/terms-of-service) ## Optional - [Sitemap](https://upsolve.ai/sitemap.xml): Every indexable URL.