Make Enterprise AI Analytics Your Own.
White-label QuaerisAI and deliver a fully branded analytics experience to your customers.
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Contract Type by Complaints Received
- Semiannual75
- Project72
- Annual53
- Monthly43
Average Service Quality by Service Name
| Corporate Training | 94.08 |
| Digital Transformation | 91.71 |
| Financial Analysis | 91.30 |
| ERP Implementation | 90.78 |
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- Snowflake
- BigQuery
- Databricks
- Redshift
- Azure Synapse
- OpenAI
- Anthropic
- Meta
Bring AI analytics to market faster.
Offer customers a branded AI analytics experience powered by QuaerisAI, with certified metrics, source traceability, and access controls built in.
- Warehouse-native, zero data copy
- Query-time row-level security
- Bring your own LLM
Ask Atlas: 3 of 12 lanes fell below the 95% target. Midwest outbound drove 61% of late loads.
Built for the partnersalready selling to these buyers.
Analytics inside your product
Adding governed analytics to an existing software product, without building a semantic layer from the ground up.
- Embed PinBoards and Stories in your app
- Tenant-scoped access enforced at query time
- Offer a premium analytics tier with real ROI
Branded client portals
Offering a branded analytics product to clients, backed by infrastructure they didn't have to build.
- A portal per client, under your agency's brand
- Clients ask their own questions in plain English
- Scheduled briefs replace manual monthly decks
Industry-specific analytics
Needing analytics specific to one industry, running on infrastructure that stays industry-agnostic underneath.
- Your brand, your pricing, your contract
- Deploy as SaaS, in a VPC, or on-prem
- Partnerships team behind every rollout
What building this yourselfactually costs you.
Set the assumptions to match your team and your budget. Every figure is yours, the math is shown, and it is meant to survive your CFO.
Build = engineers × cost × months ÷ 12, plus upkeep (engineers × cost × maintenance share) for the rest of year 3. QuaerisAI = platform fee × 3, plus your integration months × cost ÷ 12. You keep = the difference.
- Upkeep, every year
- $576K
- Engineer-months back
- 96
- Months not spent building
- 12
What it costs you each year
| Year | Build it yourself | With QuaerisAI |
|---|---|---|
| Year 1 | $1.4M | $210K |
| Year 2 | $576K | $120K |
| Year 3 | $576K | $120K |
What a first release usually still lacks
- Natural-language questions with governed answers
- Agents for alerts, forecasts, and root cause
- Query-time row-level security on every surface
- A semantic layer every client shares
- Per-client branding, domains, and emails
Let's talk about what fits
We don't publish a white label price list, because a generic one would fit almost no partner. Here is what actually sets the number, and what the call covers.
- What sets the price. Deployment scale, customization scope, and how the partnership is structured.
- Where it runs. Multi-tenant SaaS, in your own VPC, or on-prem with Kubernetes-native containers.
- No cost for viewer seats. Client users who read dashboards and ask questions do not add seat fees.
- What your security team will ask. SOC 2 Type II audit in progress, GDPR supported, encrypted in transit and at rest.
15 minutes with our partnerships team, on your data model and your clients' scenarios.
Every figure here comes from your own inputs, including the platform fee, which we quote per partner rather than publish. What a partner actually pays moves with deployment scale, customization scope and client count, so a complex rollout sits higher in this range than a simple one. Figures are rounded for display, so a column may not add to its total exactly. Illustrative, not a quote.
Run it in our cloud,or run it in yours.
The same white-labeled product either way. The choice is who operates the application tier, and it is the one thing your client's security review will ask about.
We operate it. You brand it and sell it.
QuaerisAI cloud
Application, assistant and agents. Semantic layer, row-level security and audit.
Your warehouse
Queries run in Snowflake, BigQuery, Databricks, Redshift or Synapse. No extracts, no copies, whichever option you pick.
What you get
- Nothing to provision. QuaerisAI runs, patches and upgrades the platform.
- Start without a cloud account, a Kubernetes cluster or a platform team.
- New releases reach your clients without a coordination window on your side.
- One environment to support, however many clients you onboard.
What you take on
- The application tier runs in our environment, so your security review covers a third-party processor.
- Region and network topology follow our deployment, not your cloud account.
- A client whose contract requires software inside their own tenancy needs the other option.
Data stays in the warehouse
Queries run in Snowflake, BigQuery, Databricks, Redshift, or Synapse. No extracts, no copies.
Query-time row-level security
Row-level security and column masking are enforced on every query, from every surface.
Your identity provider
SSO with Okta or Azure AD. Roles load from your warehouse, so permissions live in one place.
Every question logged
A full audit trail of who asked what, what was returned, and why access was allowed or denied.
Bring your own model
Use OpenAI, Anthropic, Google, or Meta models. Your data is never used to train foundation models.
Your brand end to end
Logo, colors, fonts, your own domain and your product name, whichever cloud it runs in.
From agreement to branded product,in four steps.
- 01
Connect your data
The same read-only, no-migration connection QuaerisAI uses everywhere else.
- 02
Configure your branding
Logo, colors, fonts, domain, and notification templates.
- 03
Launch
Your branded product goes live on the same certified semantic layer.
- 04
Manage your tenants
Add and oversee customer instances from one place.
Everything your customers seecarries your identity.
What they don't see doesn't change at all.
- Certified metrics
- Audit trail
- Access controls
15 minutes with our partnerships team to scope your rollout.
- Your logo
Logo
Replaced with the partner's logo across the product.
Colors
UI theme set to the partner's brand colors.
Fonts
Typography set to the partner's brand font.
- analytics.yourbrand.com
Custom domain
Product served under the partner's own domain, not quaeris.ai.
- FromYour Brand
Notification templates
Emails and in-app alerts sent under the partner's name.
- Your product name
App name
Product name shown to end users set by the partner.
Two terms, used interchangeably,that are not the same thing.
Embedded analytics puts your product's data in front of the people already using it. White label analytics gives your clients an analytics product with your name on it. QuaerisAI ships both, and plenty of partners run both.
An analytics product your clients use as if you built it.
- Your logo, colors, typeface, domain and product name on every surface.
- Each client asks their own questions against their own data.
- Sold as a product or a service in its own right.
| Attribute | Embedded analytics | White label analytics |
|---|---|---|
| What it is | A capability inside your product | A product that carries your name |
| Where it runs | Inside your application | In your app, or as a standalone branded platform |
| Whose brand shows | Yours, across the views you embed | Yours everywhere, down to the domain and the notification emails |
| Who uses it | The people already using your product | Your clients and their own teams |
| What they do with it | Explore, pin and collaborate on the views you ship | Ask their own questions in plain English against their own data |
| Who defines the metrics | You, for your product | You, once, shared by every client |
| How clients stay apart | Your application's own permissions | Query-time row-level security, enforced per client in the warehouse |
| Typical buyer | SaaS product teams | SaaS, consulting and marketing agencies, vertical solution providers |
| Commercial model | A feature of your product | A line item your client pays for |
| What viewers cost | Part of your product's pricing | Nothing. Viewer seats add no fee |
Most partners end up doing both
Embed QuaerisAI where your product already lives, and sell a branded platform to the clients who want one of their own. The engine, the semantic layer and the governance underneath are the same either way.
Explore embedded analytics