Qrvey

What Qrvey is

Qrvey is an embedded analytics platform for software companies that want to put dashboards, reports and AI-driven analysis inside their own product. The home page describes the offer as embedded analytics (JS widgets, APIs and SDKs), AI and agents, and workflows and alerts, and labels it "AI-Native", "Deployed in Your Cloud" and "Multi-Tenant". The reader it fits is a SaaS product or engineering team, not a business user building a one-off internal dashboard. The pages held do not say where the company is based.

What you build and how

The home page lists four product areas. Data Visualization gives you interactive dashboards, an AI-powered chart builder, drill-downs and pixel-perfect reporting, all white-labeled and self-service. No-Code Automation is a visual workflow builder for alerts, actions and API integrations, and the page says it can build agentic workflows with no engineering required. Embedded AI covers an MCP server and an AI client interface, with natural language prompts, an AI assistant and custom agents for autonomous analytics. The fourth area is data, where the page mentions building and sharing custom datasets.

The dashboard side is described in plain no-code terms. A tour section on the home page invites you to build a dashboard under the line "Drag, drop, customize, no code, no sprint." The self-service pitch lists interactive reports, a drag-and-drop chart builder, drill-ins, custom formulas and responsive layouts. The point of all of that is that the end users of your SaaS product, your customers, can build and change their own views without opening a ticket to your engineers.

The AI layer is built around a semantic model. The page says Qrvey's natural language querying, anomaly detection and AI-generated dashboards are grounded on your semantic model, not guessing at your schema. It is LLM-agnostic: the FAQ says you can bring OpenAI, Anthropic, Bedrock or a fine-tuned open-source model running in your VPC, and that the semantic layer is the grounding mechanism, not the model. For a team that cannot send customer data to a third-party model, that choice matters.

White-labeling is covered by a section titled "Will it actually look like our product?" The answer on the home page is yes, and says every component takes your design tokens and typography. A tour step invites you to apply your tokens, your brand and your dark mode.

The workflow tour step says you can turn dashboards into alerts, actions and triggers, with delivery by messaging, email, apps and APIs.

For development teams, the FAQ says schemas, dashboards and semantic models are declarative and version-controlled, so you can promote between environments with the same CI/CD pipeline you already use. Qrvey's comparison page against Luzmo adds that Qrvey includes ETL, data transformation and a data engine, plus a built-in analytics database, as part of the platform. It lists ten criteria it compares on, including dashboard builder capabilities, dataset customization, semantic layer capabilities, data transformation, multi-cloud deployment support, managing environments and content migration, front-end embedding technologies and back-end API capabilities.

Deployment is where Qrvey differs most from hosted BI tools. The FAQ says Qrvey is deployed as Kubernetes containers in your own AWS, Azure or GCP account, single-tenant, in your VPC, with zero data egress by default, and that compliance teams typically sign off in days, not quarters. Qrvey's blog FAQ adds that you can deploy as many instances across as many environments and regions as needed at no extra cost, and the Luzmo comparison contrasts this with a SaaS-only vendor, saying control over data ownership, release cycles and timelines sits with the vendor. That comparison is Qrvey's own marketing, written by Qrvey.

What the pages say

Qrvey says it prices by flat annual platform fee, "not per seat, not per query, not per tenant." The pricing block reads "Flat-rate platform fee, no per-seat charges, no per-tenant pricing," and promises a number within one business day or less after you share your objectives. An FAQ answer on Qrvey's blog says flat-rate pricing comes with unlimited tenants, users, datasets, dashboards and so on.

Two named editions are described. Qrvey Pro is for SaaS teams with an analytics-ready database that want to move quickly and keep infrastructure light. Qrvey Ultra is for teams seeking maximum flexibility, scalability and control, with full-stack analytics delivery. The pages held give no price for either edition.

The home page includes an in-house build versus buy calculator marked as illustrative. Its assumptions are a $200K fully loaded engineer and an $85K flat annual platform fee, and a note says actual pricing depends on scope. Treat the $85K as a modeling input in a sales tool, not a quote. The calculator also lists build tasks it assigns engineering months to, such as a tenant landing with 40M rows, a SOC 2 request for row-level audit trails, pixel-perfect PDF exports and natural-language querying.

On security, the FAQ says Qrvey supports SOC 2, HIPAA and GDPR implementations, with multi-tenant isolation enforced at all levels of the solution and your data never leaving your cloud or VPC. The wording is "supports implementations," which is not the same as a certification held by Qrvey itself.

The Luzmo comparison page quotes customers. EvenFlow.ai's CEO says Qrvey lets the company satisfy any use case its customers ask for, and the page shows 115% NRR growth and 70% support tickets reduced beside it. INGENIOUS.BUILD's COO credits Qrvey with tenant flexibility and dashboard management. A JobNimbus senior PM calls Qrvey 9 the biggest release the company had seen, praising the revamped dashboard creation experience, native dashboard sharing, dataset creation and subscription improvements and a snappier, cleaner UI. A 70% enterprise adoption figure sits next to that quote. The page also carries a disclaimer that its comparison ratings are directional and based on public information.

Who it suits and who it does not

Qrvey suits a SaaS company that wants analytics as a feature of its own product, with tenant isolation, white-labeling and a deployment inside its own cloud account. A team with Kubernetes experience, a compliance reviewer who insists that data stays in a VPC, and customers who expect their own dashboards will find the model fits. A flat fee also suits a product whose tenant and user counts will grow, because the pages say the price does not scale with them.

It is a poor match for a solo founder or operations team that wants to connect a spreadsheet and publish a dashboard this afternoon. The pages describe an annual platform fee quoted after a conversation, an install into your own cloud, and an engineering audience. A team without that infrastructure expertise would carry the operating burden of the containers, though the pages themselves do not discuss staffing.

A second caveat is that most of the comparative claims on the pages come from Qrvey about competitors. Check them against your own trial before relying on them.