Seek AI vs. Querio: An Honest Comparison for Data Teams

Seek AI vs Querio compared fairly: buying model, delivery surfaces, where context lives, auditability, and a bake-off protocol for evaluating both.

Seek AI and Querio solve the same headline problem — letting people ask questions of company data in plain English — but they are bought and operated differently. Seek AI is an enterprise natural-language data product sold through a sales process, with pricing and implementation scoped per customer. Querio is an analytics platform you can trial yourself, built around an agent that writes inspectable SQL and Python in a notebook, with a context layer that lives in your own GitHub repository and a free MCP tier for agent access.

If you want a vendor to model your data with you under an enterprise agreement, Seek AI is worth a conversation. If you want to evaluate the product before talking to anyone, keep your definitions in files you own, and give people answers in Slack, Teams and Claude as well as a BI app, Querio is the closer fit.

A note on sourcing. Seek AI does not publish a detailed public feature matrix or price list. Everything below about Seek AI reflects its public positioning at the time of writing and should be confirmed with the vendor; we have deliberately left cells blank rather than guess. Inventing a competitor's specifications helps nobody, least of all a reader trying to make a real decision.

Where Seek AI Is Strong

Three things stand out in Seek AI's public positioning, and they are genuine strengths for the buyers it targets.

Enterprise-first engagement. A scoped implementation with vendor involvement suits organisations whose schemas are messy, whose stakeholders are numerous, and whose procurement expects a named account team. Some data estates genuinely need humans in the onboarding loop.
Business-user focus. The product is aimed squarely at non-technical staff getting answers without filing a ticket — the same bottleneck every data team recognises. That framing shapes the interface toward asking rather than building.
Custom pricing. Bespoke commercial terms are a disadvantage when you are comparing options quickly, and an advantage when your usage pattern is unusual enough that a published tier would price you badly.

The corresponding cost is evaluation speed. When pricing and capability details are only available through sales, an eight-week procurement cycle is common before anyone has typed a real question against real data. For lean teams, that is the single biggest difference in this comparison.

Where Querio Is Strong

Querio is designed around a different assumption: that the tool should be verifiable by the people who will be blamed if a number is wrong.

Every answer is code you can read. The agent writes real SQL and Python in a reactive notebook — connected cells, charts that update when the query changes. Open any answer and inspect exactly what it did. There is no opaque generated query you cannot see.
Context you own. Joins, metrics, definitions and trusted queries are stored as plain SQL, Markdown and Python files, synced to GitHub in the same repo as your dbt project. The agent proposes what it learns; humans approve what sticks. If you leave, the repo is still yours and still works with other agents.
People are met where they work. Data teams get the notebook; everyone else asks in Slack, Microsoft Teams or inside Claude via MCP, with OAuth so every agent query inherits that user's permissions. Slack answers spin up a real notebook behind the scenes, so nothing is unauditable.
Published pricing and a free agent tier. Starter is $500/month for up to 10 users; Core is $1,999/month ($1,699 billed annually) with unlimited users, three connections and guided onboarding; Enterprise is custom. MCP and API access start free at 100 questions per month with no payment details required.
Live warehouse connections. Snowflake, BigQuery, Redshift, ClickHouse, MotherDuck, PostgreSQL, MySQL, MariaDB, SQL Server and MongoDB, through encrypted read-only credentials — no extracts, no duplicated data.

Side-by-Side

Dimension	Querio	Seek AI
How you start	Free trial; free MCP/API tier at 100 questions/month	Contact sales
Published pricing	Starter $500/mo (to 10 users); Core $1,999/mo, $1,699/mo annual, unlimited users	Not published — custom quote
Primary work surface	Reactive notebook plus dashboards built from notebooks	Natural-language question interface
Answer transparency	Real SQL and Python, inspectable per answer	Confirm with vendor
Where definitions live	Plain files in your GitHub repo, alongside dbt	Confirm with vendor
Agent access	MCP with OAuth permission inheritance, plus API and embedded	Confirm with vendor
Delivery surfaces	App, Slack, Teams, Claude and other MCP clients, scheduled reports, embedded	Confirm with vendor
AI metering	Included in the plan; at-cost overages with optional hard cap	Confirm with vendor
Security posture	SOC 2 Type II, annual third-party pen tests, HIPAA with BAAs, SSO, RBAC, sandboxed execution	Confirm with vendor
Deployment	Cloud; self-hosting and physical data separation on Enterprise	Confirm with vendor

The Questions That Actually Decide It

Feature grids rarely settle a purchase. In our experience these four questions do.

1. Who is accountable when a number is wrong?

If the answer is "the data team", they need to be able to open the query, not re-derive it. Ask each vendor to show you the exact SQL behind a non-trivial answer — a cohort retention question, not a row count — and then to change one definition and show what else moved.

2. Where does your context live in three years?

Semantic context is the expensive asset: the joins, the test-account filters, the churn definition your finance team argued about for a month. If it is stored inside a vendor platform, switching costs compound quietly. Querio's answer is files in your repo that keep working with any agent, even without Querio's MCP. Ask Seek AI where their equivalent lives and in what format you can export it.

3. How does the tool behave when it does not know?

The dangerous mode is not "no answer", it is a confident wrong one. Querio answers only from what is in the data and says so when something isn't there. Test this deliberately: ask for a metric that does not exist in your warehouse and see what comes back.

4. What does the bill look like at 5x usage?

Per-question metering changes user behaviour — people stop asking follow-ups, which is exactly the behaviour you were buying the tool to encourage. Querio includes AI usage in the plan with an optional hard cap so spend simply stops at the limit. Ask any vendor to model your bill at five times your expected volume.

How to Run a Fair Bake-Off

Vendor demos use vendor data. This protocol takes about a day per tool and produces evidence you can defend.

Write 30 questions before you see any product. Ten simple aggregates, ten that require a join your schema does not make obvious, five that depend on a contested definition (active customer, churn, qualified lead), five that should be impossible to answer from your data.
Have an analyst answer all 30 by hand first. This is your ground truth. It usually takes two hours and it is the only reason the exercise is worth anything.
Score each tool on four axes: correct number, correct logic (read the SQL — a right answer from wrong logic is a future incident), honest refusal on the impossible five, and time-to-answer including any setup.
Re-run the same 30 questions two weeks later after each tool has ingested feedback. The gap between run one and run two tells you whether the product actually learns your business or just re-guesses each session.
Test with a low-privilege account, not an admin one, and confirm restricted rows are excluded before aggregation rather than after.

Most buying regret comes from skipping step 2 and step 4. Related reading: the conversational BI buyer's guide, metrics for testing text-to-SQL accuracy, and what an AI data analyst can and cannot do.

Which One Fits You

Choose Seek AI if you want a vendor-led enterprise engagement, your procurement is comfortable with custom pricing, and hands-on onboarding of a difficult data estate is worth the longer evaluation cycle.
Choose Querio if you have a real warehouse and a small data team, you want to test before you talk to sales, you care that definitions live in your own repo next to dbt, and you want answers in Slack, Teams and Claude with permissions intact.
Choose neither yet if your warehouse has no trustworthy source-of-truth tables. No natural-language layer fixes an unmodelled schema; it just produces wrong answers faster.

FAQ

Does Seek AI publish pricing?

Not publicly at the time of writing — pricing is quoted per customer. Querio publishes tiers: Starter $500/month for up to 10 users, Core $1,999/month ($1,699/month billed annually) with unlimited users and three connections, and custom Enterprise pricing. See the pricing page.

Can I connect either tool to Claude or another AI assistant?

Querio ships an MCP server that uses OAuth, so each agent query inherits the asking user's data permissions, and it is free to start at 100 questions per month. For Seek AI, confirm agent and API access with the vendor.

Do I still need dbt if I use Querio?

Yes, and that is the point. Querio's context files sync to GitHub in the same repository as your dbt project, so transformations stay in dbt and the semantic context that agents need sits beside them instead of drifting in a separate tool.

What happens to my definitions if I stop using Querio?

They stay in your repository as plain SQL, Markdown and Python. They continue to work with Claude Code or any other agent, with or without Querio's MCP server.

How long should an evaluation take?

Budget one day per tool for the 30-question protocol above, plus a two-week gap before the second run. A trial that never gets past a demo dataset tells you almost nothing about accuracy on your schema.