Upsolve AI, Inc.
Upsolve AI builds Agent Studio, a platform that lets data teams encode institutional context, govern and test it, and deploy reliable analytics agents to business users. Its stated aim is to make analytics accurate, trustworthy, and available without a data-team queue.
At a Glance
- Data and business-intelligence teams
- B2B SaaS and software companies needing customer-facing analytics
- Growth-stage companies
- Enterprise and Fortune 500 organizations
- +1 more
AI Tools by Upsolve AI, Inc.
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Upsolve AI
Analytics Agent Studio for Data
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Products & Services
A workspace for data teams to curate data models, semantic meaning, business rules, golden assets, tests, governance, and context; deploy and monitor an analytics agent.
A governed analytics agent that answers natural-language questions, generates and runs SQL, asks clarifying questions, creates charts and reports, and explains results in business context.
Embeddable, branded AI-powered BI that lets SaaS customers or customer-success teams create dashboards and analytics inside their product.
A product for turning CSV data into dashboards and shareable analysis.
Market Position
Upsolve positions itself as a context- and governance-first alternative to generic text-to-SQL and traditional embedded-BI tools: it combines semantic/data modeling, institutional knowledge, permissions, evals, observability, and agent distribution in one platform. Relevant alternatives and competitors include Hex, Metabase, Looker, Power BI, ThoughtSpot, Cube, dbt's semantic layer, Databricks Genie, and Snowflake Cortex/semantic views. Its differentiation is a tested, versioned context layer plus customer-facing and multi-tenant agent deployment.
Leadership
Founders
Ka Ling Wu
Co-founder and CEO. Former Product Lead at Palantir, where she helped create HyperAuto from inception to market launch and opened the Korea market; before Upsolve she led product-solution development at nPlan, a Series A AI/construction technology company. She studied civil and mechanical engineering at Brown University.
Serguei Balanovich
Co-founder and CTO. Spent seven years at Palantir, pioneering supply-chain and manufacturing commercial work, productizing it into HyperAuto, and serving as a development lead on Palantir Apollo. He studied computer science and applied mathematics at Harvard University.
Executive Team
Ka Ling Wu
Co-Founder & CEO
Former Product Lead at Palantir and former product-solution lead at nPlan; helped build Palantir HyperAuto. Brown University civil and mechanical engineering.
Serguei Balanovich
Co-Founder & CTO
Former Engineering Lead at Palantir; worked on HyperAuto and Apollo. Harvard computer science and applied mathematics.
Founding Story
After collaborating with more than 100 customers on digital-transformation work and building Palantir HyperAuto from zero to a product serving 50+ enterprise customers, the founders concluded that analytics should be augmented and automated. They started Upsolve to encode business context and institutional knowledge so analytics agents could provide answers people trust instead of merely plausible SQL.
Business Model
Revenue Model
Subscription SaaS priced by plan plus usage-based AI credits; paid plans include monthly credits and charge overage per credit. Enterprise can use a base platform fee plus credits or bring its own model with an Upsolve surcharge.
Pricing Tiers
2,000 one-time credits; all Pro features unlocked; approximately 200 analytical questions.
2,000 credits/month; $0.05 per credit overage; 50+ data connections, unlimited agents, charts, context management, observability/evals, and MCP App.
10,000 credits/month; $0.05 per credit overage; adds embedding, RBAC, multi-tenancy, AI Cockpit, dashboards, email scheduling, and dedicated support.
Base platform fee plus credits or BYOM plus Upsolve surcharge; on-prem/VPC, SAML SSO, HIPAA, model routing, forward-deployed engineering, and 24/7 support.
Target Markets
- Data and business-intelligence teams
- B2B SaaS and software companies needing customer-facing analytics
- Growth-stage companies
- Enterprise and Fortune 500 organizations
- Industries requiring governed, multi-tenant, or regulated analytics
- Trusted self-service analytics for business and data teams
- Customer-facing embedded analytics for B2B SaaS products
- Reducing repetitive ad-hoc BI and reporting requests
- Semantic-layer and institutional-context management for AI agents
- Governed multi-tenant analytics with row-level permissions
- Cross-source analysis across warehouses and operational systems
- Arthur
- Every.io
- Measurable
- Masraff