🏆 TL;DR: Upsolve AI is context infrastructure for analytics agents. We help data teams ship analytics agents that non-technical users actually trust. Agents that pull the right numbers and explain them in your business's context, not just generate plausible SQL. If you've ever tried to put an analytics agent in front of your team and quietly killed it after it gave two different answers to the same question, this is for you.

https://youtu.be/2bXZb6D-cNA

❌ The Problem

"Let's just build an analytics agent." Every data team drowning in requests has this thought. The beta feels like magic until a non-technical user asks the same question three times and gets two different answers. Trust dies. The project quietly joins the 95% of AI POCs that never reach production.

You already know it's not the model, GPT and Claude write fine SQL. The model just has no idea how your business works:

• Governance and permission can't live in a prompt. You can't trust an LLM to implement row- and column-level access or enforce who sees what. Without governance that an agent can't override, you can't put it in front of anyone outside the data team.
• It pulls the wrong data and joins the wrong tables. “gross_revenue” vs “net_revenue”, “customers” vs “users”. A semantic layer problem dressed up as a text-to-SQL one.
• Technically correct, practically wrong. Your fiscal year ends in March, not December. The number is "correct" and still useless.
• Your semantic layer and context goes stale. "User" got redefined twice since you scoped the project. Your semantic layer and context didn't keep up.
• No observability, no improvement. If you can't trace and root-cause a bad answer, you can't improve it.

These aren't LLM model problems. They're context problems, and context lives in docs, Slack threads, validated SQL from 2021, and your analysts' heads. No amount of SQL generation skill recovers it.

⚒️ What Agent Studio gives you

A builder surface to encode context across three layers: Structure (schema, lineage, semantic model), Meaning (metrics, KPIs, business rules), and Trust (verified answers, usage signals, golden sets), then deploy and tune against it:

• Software-defined, granular permissioning baked into the data model, enforced beneath the agent.
• A self-updating context layer that captures institutional knowledge across your files and systems, so metric redefinitions and new launches don't silently break answers.
• End-to-end tracing, an eval harness, and a staging sandbox. Golden query sets and LLM-as-judge scoring before a single user sees an answer, plus prompt and SQL provenance for every response in production.
• A compounding accuracy loop. Wrong answers and poor behavior get flagged from real conversations and fed back, so the agent gets more trustworthy over time.

End result: No existing semantic model required to start. Your team deploys analytics agents they don't have to babysit, and your non-technical users get answers they trust, wherever they already work (Slack, your product, internal portal, or any MCP surface).

Teams hit production-grade eval accuracy, clear 60–80% of their ad-hoc backlog in 7 days, and move stakeholders from a 3–5 day queue to answers in seconds.

🤝 Why trust us

We’re already in production with Fortune 500 users, 100+ person BI teams, and growth-stage companies. We are the team that built HyperAuto at Palantir, and encoding messy institutional context into systems is the problem we've spent years on.

🙏 Our ask

This is for you if you own a data or BI function and you're either:

• Drowning in ad-hoc requests, where repeat questions eat your analysts' week and a generic GPT-on-the-warehouse attempt already fizzled, or
• A B2B SaaS team whose customers keep asking for deeper analytics and insights and you don't want to spend two quarters of engineering effort building it.

If you're a Head of Data, BI lead, analytics engineer, or VP Product who lives this, book a demo here or reach me directly at kaling.wu@upsolve.ai. We'll stand up a POC against your real data warehouse, not a sandbox.