Embedded AI for SaaS analytics.
Yours to design, control, and deploy.
Give your customers AI that earns its place inside your product — and inside the analytics workflow they already use. Sidekick, built-in and custom agents, and the Qrvey MCP Server give your team the controls to design, govern, and grow AI-driven analytics specifically for SaaS — grounded on your data, governed by your multi-tenant security model.
AI in your product is easy to demo.
Delivering it to ten thousand tenants is the hard part.
Most teams start with a chatbot prototype. Six months in, they discover the prototype doesn't know their schema, doesn't respect their tenants, and doesn't fit their product — and the AI has opinions the product team never approved.
Models don't know your schema.
An LLM pointed at your database guesses joins and confidently invents metric definitions. By the time a customer asks why the number is different from yesterday's, your AI feature has become a support ticket.
Chatbots don't know your tenants.
Generic AI assistants don't inherit your multi-tenant security model. With the right prompt, Acme can see Initech's metrics — and your security review turns into a quarterly project, not a feature that earns its place in the product.
Bolt-ons don't fit your product.
A chat panel pasted into the corner of someone else's UI isn't the AI experience your customers will adopt. AI that lives outside the workflow gets ignored — and the data points that prove it stack up in your retention dashboard.
Three layers, designed to fit together.
All controlled by your product team.
Qrvey delivers embedded AI through three building blocks: Sidekick, the assistant your customers see; Agents, the capabilities Sidekick exposes; and the MCP Server, which connects everything to your analytics environment. Multi-tenant security applies at every layer. The LLM is yours to pick.
Four steps from existing analytics
to AI inside your product.
The MCP Server links Sidekick and its agents to your existing Qrvey datasets, dashboards, metadata, and tenant permissions — no parallel data layer to maintain.
Choose built-in agents and define custom ones with the data access, actions, and context that match your domain. Layer in product, customer, and use-case context.
Place Sidekick inside the analytics workflows your customers already use. JavaScript components and comprehensive APIs match your product's design and behavior.
Add new agents as your product grows. One deployment serves every tenant, with multi-tenant security applied to every interaction by default.
Everything you need to deliver AI analytics
that earns its place in your product.
An AI assistant embedded in the analytics experience.
Sidekick is the conversational interface users interact with — placement, behavior, and workflows defined by your product team.
- Lives where your customers already workSidekick operates directly inside dashboards, reports, and analytics workflows — not as a separate surface that asks customers to leave what they're doing.
- Guides exploration into insightThe assistant moves customers from a question to an answer to a visualization — keeping the path aligned with the analytics your team already curates.
- White-labeled and brand-consistentMatch your product's design, tone, and copy. Sidekick is JavaScript components and comprehensive APIs, not an iframe pasted into the corner.
Structured AI capabilities, not freeform improvisation.
Agents represent defined analytical functions with controlled data access, scoped actions, and layered context.
- Each agent has a jobAnalyst answers questions. Viz builds dashboards. Forecast projects metrics. Every agent has a scope, so AI behavior is predictable, not improvisational.
- Layered context, not a single promptAgents combine product, customer, and domain context with instructions that govern behavior — so the same question from Acme and Initech gets answers grounded in each tenant's reality.
- Built-in coverage, room to growStart with built-in agents for analysis and visualization. Add custom agents as your product surfaces new use cases. The library grows with you.
Designed around your product, not a generic template.
Custom agents let your team configure AI to match the workflows, terminology, and priorities your customers actually use.
- Specific data access, by designDefine exactly which datasets, dashboards, and assets each agent can touch — and which it can't. Inherits your existing multi-tenant security model.
- Actions you approveConfigure what the agent can do — answer, visualize, alert, automate. AI behavior is shaped by your team, not by whoever wrote the latest model prompt.
- Context that reflects your domainLayer in business logic, terminology, and priorities so the agent speaks your customer's language — and answers questions the way an expert from your industry would.
- Evolves as your product evolvesAdd new agents, refine existing ones, expand scope over time. AI maps to your roadmap instead of forcing your roadmap around AI.
revenue.* · subscriptions.* · churn_modelsThe connection between AI and the analytics it works on.
The Qrvey MCP Server is the access layer — it lets Sidekick and its agents interact with the same datasets, dashboards, metadata, and permissions your analytics already uses.
- One source of truthAI operates on the same datasets and definitions your dashboards do. No parallel pipeline, no drift between what AI says and what your dashboards show.
- Permission-aware by defaultEvery agent call runs through your existing tenant scopes, row policies, and asset permissions — so AI inherits your multi-tenant security, it doesn't escape it.
- Built on the open MCP standardStandardized model context protocol means agents can plug into your analytics environment without bespoke integrations every time you want to add a capability.
Built differently than whatever you're considering instead.
Wrapping an LLM around your database isn't a product.
An AI button on a BI tool is still a BI tool.
A standalone AI app isn't part of your product.
Designed for the people who own this in your product.
Decide what AI does, where it appears, and how it evolves. Deliver targeted agents tied to specific customer workflows — without standing up another AI infrastructure project.
Skip building an AI orchestration layer from scratch. Sidekick, agents, and MCP plug into your existing Qrvey stack — JavaScript components and comprehensive APIs match your product's behavior.
Get answers without filing a ticket. Ask a question, see a chart, set an alert — conversational analytics that meets them where they already work, not in a separate AI app.
Deploy embedded AI inside your existing infrastructure. Qrvey runs in your VPC, so AI inherits your deployment model, your security posture, and your release process — no new pipeline to maintain.
The same multi-tenant security model
your analytics already runs on.
AI inherits your security model.
Qrvey applies the same multi-tenant securityto AI interactions as it does to analytics. Sidekick, every agent, and every MCP request is scoped to the tenant making the call — so AI can't see, recommend, or hallucinate data that the user isn't already authorized to access.
That means you don't have to choose between giving customers AI capabilities and giving your security team something to worry about. The model that protects your dashboards protects your agents — automatically.
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