Overview
White-Label Chatbot Platform for Agencies is a no-code, white-label conversational agent built on NIVA's persona, flow, and smart-form engines. Agencies want to offer AI chatbots but can't justify building per-client bots from scratch, and most platforms charge extra for white-label or lack client isolation. With NIVA, nIVA provides full white-label (name, colours, avatar, configurable "Powered by"), multi-tenant client isolation, and a 250+ persona library so an agency can launch a branded vertical bot per client in hours. BYOK controls AI costs at scale. The result is a measurable shift from manual, after-hours-limited handling to instant, structured, around-the-clock engagement that feeds directly into your systems.
The problem
Agencies want to offer AI chatbots but can't justify building per-client bots from scratch, and most platforms charge extra for white-label or lack client isolation. For saas & tech teams specifically, every hour spent on this manually is an hour not spent on higher-value work, and every unanswered query outside business hours is a lost opportunity that a competitor with instant response will capture.
How NIVA solves it
NIVA provides full white-label (name, colours, avatar, configurable "Powered by"), multi-tenant client isolation, and a 250+ persona library so an agency can launch a branded vertical bot per client in hours. BYOK controls AI costs at scale. Because the persona is pre-trained for saas & tech and the logic is assembled in a no-code flow, the team owning this does not need engineering support to launch or iterate. Every conversation is logged and attributed, so the same deployment doubles as an insight layer revealing the questions and friction points worth acting on.
Automation flow
- Pick a client's vertical from pre-configured domains
- Subscribe to relevant personas from the library
- Upload the client's knowledge and configure branding
- Build flows and forms; embed with one script tag
- Manage all clients from a multi-tenant admin dashboard
Before vs after
| Area | Before | With NIVA |
|---|---|---|
| Build cost per client | Weeks of work | Hours |
| White-label | Often a paid add-on | Included on all plans |
| Client isolation | Often missing | Built-in multi-tenant |
| Vertical coverage | Limited | 250+ personas, 25 verticals |
How it works under the hood
Under the hood this maps to NIVA's documented engines. The persona engine handles tone and routing for saas & tech, drawing on a library of pre-trained personas so the agent speaks the language of the domain from day one rather than being trained from scratch. The flow engine runs the conditional steps in the sequence shown above, branching on the visitor's answers so each person follows the path that fits their situation. The smart-form engine surfaces structured fields at the moment intent appears, capturing clean data inline instead of bouncing the user to a separate form. Cross-session memory preserves context so returning users are recognised and never asked to repeat themselves. Finally, webhooks push the completed interaction into your systems of record, and per-persona tool calls can read live data from your APIs mid-conversation wherever an endpoint exists. None of these steps requires writing code; they are assembled in the no-code admin and embedded with a single script tag.
Who this is for
This works in two directions. As a public-facing agent it engages prospects and customers directly on your website or app; as an internal tool it answers staff questions and runs intake against internal systems. The same engines power both, and which direction you deploy depends only on which knowledge sources and systems you connect. Many saas & tech operators start public-facing for the traffic and conversion upside, then reuse the same build internally.
See this use case on your business
See how a white-label chatbot platform for agencies performs for your saas & tech business. Book a NIVA demo to watch this exact flow run against your own content, or explore the live interactive bot to feel the experience your customers would.