llms.txt
# 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.
