Upsolve AI
Upsolve AI deploys context-verified analytics agents that answer business questions from governed definitions, not guesses.
Upsolve AI is the pick when a wrong number is expensive and you have a data team willing to encode definitions up front. Verification against golden sources plus full lineage is more governance than most text-to-SQL tools bother with, and Team's embeddable, multi-tenant agent with row-level security is what product teams need to go live — though at $2,000/mo month-to-month ($1,600/mo billed annually) it is a real commitment. If you want zero-setup text-to-SQL, look at Hex; if you mainly need a semantic layer, Cube is the cheaper conversation. Budget for context engineering before you budget for the license.
Verified 3d ago · liveness 75/100 · cite: rightaichoice.com/tools/upsolve-ai
- Mid-size and enterprise data teams with a repeat-question-heavy request queue
- Organizations that need every AI answer traceable to a governed definition
- SaaS companies embedding a multi-tenant analytics agent in their product
- Regulated teams requiring RBAC, row-level security, SAML SSO or on-prem deployment
- Buyers who want zero-setup text-to-SQL with no context work
- Companies with no data warehouse or modeled metrics to build on
- Small teams unwilling to fund up-front context engineering
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Skip Upsolve AI if nobody on your team owns metric definitions and you want a text-to-SQL tool you can point at a warehouse today with no context engineering work.
Every question burns credits — a standard analytical question costs 5-10, deep multi-step exploration caps at 20 — and overage is $0.05 per credit month-to-month ($0.04 billed annually), so a curious sales team moves
Free is a genuine test drive — 2,000 one-time credits with all Pro features unlocked and no credit card. Starter Pro at $500/mo ($400/mo billed annually) suits a single data team; Team at $2,000/mo ($1,600/mo billed annually) is where embedding, RBAC and multi-tenancy arrive. Against Hex or a plain text-to-SQL layer you are paying a governance premium; against a warehouse-native BI stack you are paying for the agent, not the dashboard. Cube is the cheaper conversation if you only need the
In short
Upsolve AI — Upsolve AI deploys context-verified analytics agents that answer business questions from governed definitions, not guesses. Best for Mid-size and enterprise data teams with a repeat-question-heavy request queue, Organizations that need every AI answer traceable to a governed definition, SaaS companies embedding a multi-tenant analytics agent in their product. Free to start; paid plans from $500/mo.
What's new in Upsolve AI
Checked 3 days agoAcross the latest 5 updates: 5 news mentions.
Upsolve AI: semantic layer lifts agent accuracy 17 points
Vendor post claims governed definitions in a semantic layer raise data-agent numeric accuracy by 17 points versus ungoverned context.
Upsolve AI on context rot across 18 frontier models
Post cites benchmark evidence that LLM accuracy degrades as input grows and outlines context engineering mitigations.
Upsolve AI lists context engineering tools by layer
Roundup of 2026 context engineering tooling grouped into memory, retrieval, MCP, observability and governed data context.
Upsolve AI frames RAG as subset of context engineering
Guide positions retrieval-augmented generation as one retrieval technique inside the broader context engineering stack.
Upsolve AI compares 2026 context window sizes
Reference page compares context window limits across GPT-5.6, Claude, Gemini and Grok, stated as verified September 2026.
What people actually say about Upsolve AI — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
29 mentions across 5 sources (Hacker News, YouTube, Product Hunt, Bluesky, Lemmy) · researched Jul 6, 2026.
Average across the 5 sources that answered — each source counts once, not each post.
- +Easy to connect a database and create charts in minutes.
- +No-code dashboard builder reduces dependency on engineering teams.
- +Context engineering approach addresses real text-to-SQL pitfalls.
- +Security features like Supabase RLS and RBAC for enterprise needs.
- +Can produce analytics quality comparable to dedicated tools like MixPanel.
- −Almost no third-party reviews beyond launch day supporters.
- −Confusion with non-profit bankruptcy tool of same name hurts discoverability.
- −Credit-based pricing may become costly for high-query teams.
- −Complex multi-source semantic model setup may require data team effort.
- −YouTube and Lemmy content is mostly about a different product.
- • Overages on credit packs can increase monthly spend unexpectedly
- • Enterprise custom pricing may have large minimum commitments
Viability Score
How well maintained and how widely used is Upsolve AI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Natural language querying of warehouse data with citations to approved sources
- Three-layer context encoding: warehouse tables, validated SQL patterns, semantic models
- Context management suite for definitions, metrics, dimensions and policies
- Context ingestion from Notion, Slack, email and 30+ other sources
- SQL pattern matching against validated, approved query patterns
- Golden source verification with KPI-verified, SQL-matched and definition-applied checks
- Full lineage from source table to model to metric to final answer
- Usage signals showing query frequency and which dashboards depend on a query
- Observability and evaluation suite with real-time credit tracking
- AI Cockpit for generating semantic layers and data models
- Embeddable agent frontend inside your own product
- Multi-tenant support with row-level security and RBAC
- AI dashboards with scheduled email delivery
- Model Context Protocol (MCP) app integration
- Bring-your-own-model routing on Enterprise (e.g. Azure OpenAI, AWS Bedrock)
About Upsolve AI
Upsolve AI is an analytics agent platform for data teams who don't want to ship a demo-grade chatbot to production. It encodes institutional context in three layers: your warehouse tables, validated SQL patterns, and semantic models with metrics, dimensions and definitions. Context is also pulled from where it lives — Notion, Slack, email and 30+ other sources — so the agent knows churn excludes reactivations within 30 days, or that MRR counts paid plans only. Answers are verified against golden sources with KPI-verified, SQL-matched and definition-applied checks, and every number carries lineage from source to answer, plus usage signals showing which dashboards depend on a query. The audience is mid-size and enterprise data teams buried in repeat requests. Upsolve's own framing puts 47% of the analyst queue at repeat questions and 2-4 weeks of wait time. Pricing is credit-based: a simple clarification is 1 credit, a basic lookup 3-5, a standard analytical question 5-10, and deep multi-step exploration caps at 20 credits. Free includes 2,000 one-time credits (~200 analytical questions); Starter Pro is $500/mo month-to-month or $400/mo billed annually for 2,000 credits/mo; Team is $2,000/mo month-to-month or $1,600/mo billed annually for 10,000 credits/mo. Free and Pro already include 50+ data connections and unlimited agents, so the gate is embedding and governance, not connectivity.
Behind the Verdict
The interesting thing about Upsolve AI is what it refuses to do. Most text-to-SQL products sell you a chat box bolted to your warehouse and let you discover in production that 'churn' means three different things to three teams. Upsolve instead forces the unglamorous work up front: encode warehouse tables, validated SQL patterns, and semantic models with metrics, dimensions and definitions. Only then does the agent answer, and every answer is checked three ways — KPI verified, SQL matched, definition applied — against golden sources you have approved. Lineage runs source to model to metric to answer, and usage signals tell you a query is used in 12 dashboards and was queried 340 times this week. That last part is unusual and genuinely useful: it turns dashboard sprawl into a signal you can act on. Strengths. Verification depth is the moat, and it is the thing competitors mostly skip. Context ingestion from Notion, Slack and email means business rules that live in someone's head or a stale Confluence page can be captured where they actually exist. The credit model is at least legible: 1 credit for a clarification, 3-5 for a lookup, 5-10 for a standard analytical question, capped at 20 for deep exploration, with overage at $0.05/credit month-to-month or $0.04/credit billed annually. Free includes 2,000 one-time credits and unlocks all Pro features, so you can genuinely test the workflow before paying. Team adds the things product teams actually need — embeddable agent frontend, multi-tenant support, row-level security and RBAC — and the AI Cockpit helps generate the semantic layer rather than demanding you hand-write all of it. Weaknesses. The same context work that produces trust is the adoption tax. Upsolve's site claims about 30 minutes to a working agent and roughly 7 days to a reliable one, and those are different numbers for a reason: the first agent is easy, the trustworthy one requires someone to define metrics and approve golden sources. If nobody owns your metric definitions, this tool will expose that fast. Enterprise pricing is custom — the model is a base platform fee plus credits, or BYOM plus an Upsolve surcharge — so on-prem, VPC, SAML SSO, HIPAA and model routing all require a conversation. Credit consumption also scales with question complexity, which is fair but means your analytics bill now moves with how curious your sales team is. Where it fits. Mid-size and enterprise data teams with an existing warehouse and at least a rough semantic layer, a repeat-question-heavy request queue, and governance pressure — regulated industries, finance definitions like MRR, anything that lands in a board deck. It fits SaaS companies embedding customer-facing analytics, because the multi-tenant and RBAC plumbing is already there. Where it doesn't. Companies with no warehouse or modeled metrics have nothing to encode. Buyers who want zero-setup text-to-SQL will find the context requirement annoying rather than valuable. Small teams
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Real-world workflow fit
Concrete scenarios for the personas Upsolve AI actually fits — and what changes day-one when you adopt it.
Connect Snowflake, ingest the existing churn and MRR definitions from Notion and a Slack thread, then approve four golden source assets before anyone asks a question.
Outcome: Sales and finance self-serve the 47% of requests that were repeat questions, and each answer shows KPI-verified and SQL-matched checks with lineage back to the source table.
Upgrade to Team to get the embeddable agent frontend, configure row-level security and multi-tenant support, and point each customer at their own data slice.
Outcome: Customers explore their own data without your team fielding tickets, and tenant boundaries are enforced by RBAC rather than application code.
Use the AI Cockpit to generate draft data models and semantic layer definitions, then review them against validated SQL patterns before promoting.
Outcome: Modeling that was a multi-week project gets a head start, and usage signals show which generated queries actually landed in dashboards.
Use Cases
- Cut your data team's ad-hoc request queue by delegating the 47% that are repeat questions to an agent.
- Let sales ask 'What is our pipeline this quarter?' and get a verified answer without waiting 2-4 weeks.
- Keep finance definitions like MRR applied consistently — paid plans only, refunds excluded after 30 days.
- Embed a context-aware analytics agent in your own product so customers explore their data independently.
- Trace any number back through source, model, metric and answer lineage for audit or review.
- See which queries actually feed dashboards before you deprecate or rebuild them.
Limitations
- Pricing is credit-based: 1 credit for a simple clarification, 3-5 for a basic data lookup, 5-10 for a standard analytical question, and deep multi-step explorations are capped at 20 credits, with overage at $0.05 per credit.
- The free tier is 2,000 one-time credits (~200 analytical questions), Starter Pro is $500/mo for 2,000 credits/mo, and Team is $2,000/mo for 10,000 credits/mo.
- Setup depends on encoding institutional context — warehouse tables, validated SQL patterns, semantic models and definitions — before agents return verified answers; the site claims 30 minutes to a working, reliable agent.
- Enterprise capabilities including on-prem/self-host/VPC deployment, SAML SSO, HIPAA and BYOM model routing are custom-priced as a base platform fee plus credits or BYOM plus surcharge.
as of 2026-10-07
Verification history
We have re-verified Upsolve AI 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Upsolve AI tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0
Ideal for
A data team wanting to test verification quality on real questions before committing budget — no credit card required.
What this tier adds
Free entry point: 2,000 one-time credits (~200 analytical questions) with all Pro features unlocked, 50+ data connections and unlimited agents.
Starter Pro
$500/mo (annual $400/mo)
Ideal for
A single data team moving from test drive to production use of the agent without embedding or governance requirements.
What this tier adds
Adds 2,000 recurring credits per month, the full observability and eval suite, the MCP App and priority email support — $500/mo month-to-month or $400/mo billed annually.
Team
$2,000/mo (annual $1,600/mo)
Ideal for
A SaaS company embedding a multi-tenant analytics agent in its own product, or a team that needs RBAC and row-level security.
What this tier adds
Adds row-level security/RBAC, the embeddable agent frontend, multi-tenant support, the AI Cockpit for semantic layer generation, AI dashboards with email scheduling and dedicated support — $2,000/mo month-to-month or $1,600/mo billed annually.
Enterprise
Custom
Ideal for
Regulated or large organizations needing on-prem/VPC deployment, SAML SSO, HIPAA and bring-your-own-model routing.
What this tier adds
Adds on-prem/self-host/VPC deployment, SAML SSO and HIPAA, BYOM model routing, and Forward Deployed Engineering for semantic layer, data modeling and context management with 24/7 support.
Where the pricing makes sense
The company stage and team size where Upsolve AI's pricing actually pencils out — and where peers do it cheaper.
Free is a genuine test drive — 2,000 one-time credits with all Pro features unlocked and no credit card. Starter Pro at $500/mo ($400/mo billed annually) suits a single data team; Team at $2,000/mo ($1,600/mo billed annually) is where embedding, RBAC and multi-tenancy arrive. Against Hex or a plain text-to-SQL layer you are paying a governance premium; against a warehouse-native BI stack you are paying for the agent, not the dashboard. Cube is the cheaper conversation if you only need the
Setup time & first value
How long it actually takes to get something useful out of Upsolve AI — broken out by persona, not the marketing-page minute.
Free signup is immediate and needs no credit card. Upsolve claims about 30 minutes to a first working agent once a warehouse connection exists. A reliable, production-grade agent is closer to 7 days, because someone has to encode tables, SQL patterns, semantic models and metric definitions and approve the golden sources. Enterprise onboarding adds Forward Deployed Engineering for the semantic
Switching to or from Upsolve AI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Hex: Export your existing saved queries and reuse them as validated SQL patterns rather than starting the pattern library from scratch.
- →From Cube: Map your existing metrics and dimensions into Upsolve's semantic models, then approve golden sources to get verification coverage.
- →From ad-hoc text-to-SQL pilots: Keep the warehouse connection, add the missing context layer of definitions and validated queries.
- →From manual analyst queue: Ingest definitions already documented in Notion, Slack and email so repeat questions route to the agent.
- ↗To Cube: Export metrics and dimensions if you decide you only need the semantic layer, not the agent and verification layer.
- ↗To Hex: Take your validated SQL patterns back to a notebook-centric workflow if your team prefers writing queries directly.
- ↗To a warehouse-native BI tool: Point dashboards at the warehouse directly if scheduled reporting outweighs conversational analysis for your team.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Upsolve AI”, and we withheld 6: 6 could not be judged, because “Upsolve AI” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Upsolve AI.
Official links
Tools that pair well with Upsolve AI
Common stack mates teams adopt alongside Upsolve AI, with the specific reason each pairing earns its keep.
Pigment
Pigment is an enterprise business planning platform with AI agents that build and run your planning model on governed data.
Domo
Domo prepares governed data for AI agents, then layers BI dashboards, workflows, and embedded analytics on top of it.
Sigma Computing
Sigma Computing is the warehouse-native AI analytics runtime for building governed AI apps, agents, and reports on live cloud data.
Featured Head-to-Head Comparisons
Upsolve Ai vs Nectar Energy
Nectar Energy and Upsolve AI serve entirely different domains — building energy management vs. data analytics. Your choice depends on whether you need to cut electricity costs in commercial real estate or empower non-technical teams with trusted data answers. Nectar is ideal for facility managers with BMS infrastructure; Upsolve is for data teams wanting to offload ad-hoc queries. There is no overlap.
Upsolve Ai vs Geologicai
These tools serve entirely different domains — no direct competition. Choose GeologicAI if you're in critical minerals mining and need a complete multi-sensor core analysis platform with AI logging and resource modeling. Choose Upsolve AI if you're a data team wanting to build trusted, context-aware analytics agents that reduce ad-hoc SQL requests and empower non-technical users.
Upsolve Ai vs Screenplayiq
These tools serve completely different domains. Upsolve AI is for data teams that need to embed business context into analytics agents to reduce ad-hoc requests and ensure trust. ScreenplayIQ is for film industry professionals who want data-driven feedback on screenplays with box office predictions. Choose based on your field: data analytics vs. screenwriting.
Alternatives to Upsolve AI
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Pigment
Pigment is an enterprise business planning platform with AI agents that build and run your planning model on governed data.
Domo
Domo prepares governed data for AI agents, then layers BI dashboards, workflows, and embedded analytics on top of it.
Sigma Computing
Sigma Computing is the warehouse-native AI analytics runtime for building governed AI apps, agents, and reports on live cloud data.
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