Give your customers AI that does real work, without building an agent platform yourself.
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You own the experience, the pricing, and the relationship; we run the machinery underneath.
Your product
The only surface your customers ever see.
Your agents
Authored once, versioned centrally, deployed to every customer.
Your tenants
Each customer isolated, with their own data, credentials, and limits.
Your economics
Usage and cost metered per customer, so you know what the feature costs to run.
You shouldn't have to spend a year building the infrastructure that makes an agent reliable in production.
The hard parts already run: isolated execution, versioning, credentials, approvals, metering, webhooks. Your team integrates an API, provisions a workspace per customer, and renders the results in your existing product.
Your customers never need a Levain account: no separate login, no Levain UI, no emails from us. The agent lives inside your product, under your brand, with the pricing, permissions, and workflows you choose.
Author the agent once, deploy it across your customer base, and improve the definition centrally. Fleet learning compounds across deployments while each customer's data stays inside its own workspace.
Building the platform yourself
Shipping on Levain
Your company is the org; each customer gets an isolated workspace inside it. Their data, documents, credentials, runs, and limits stay behind their own boundary, while you steer the whole fleet from the org level: versions, rollouts, entitlements, budgets, analytics.
Test the next version on your staging workspace first. Promote it to the fleet when it holds; roll it back if it doesn't. Your customers stay pinned until you decide.
The whole surface is an API, and the integration follows the flows you already have: signup, OAuth, offboarding.
Build it at the org level: instructions, tools, integrations, guardrails. Version it like software, because that's what it is.
One API call in your signup flow gives each customer their own workspace. Suspend or purge it just as easily when a customer leaves.
Pass the credentials and integrations your customer has authorized. Levain keeps them scoped to that workspace and watches their health.
Trigger runs from your backend. Results, approvals, credential alerts, and spend events come back as signed webhooks you render in your own UI.
Three jobs, one platform: ship the feature, keep every tenant inside the lines, and let the economics improve as the fleet grows.
Get the feature into your customers' hands.
Authoring & versioning
Define an agent once; stage, promote, and roll back versions across the fleet.
Tenant provisioning
A workspace per customer, created and retired from your signup and offboarding flows.
API + webhooks
Trigger runs from your backend; results and events come back signed.
Keep every tenant inside the lines you set.
Customer isolation
Data, credentials, and history separated by design; every run in an isolated sandbox.
Spend caps & entitlements
Cap spend per customer and gate capabilities by the plan they bought.
Approvals
Human decisions surface in your product; nothing proceeds without them.
Audit trail
Every run logged, metered, and attributed to its workspace.
What gets easier as the fleet grows.
Per-customer metering
Usage, cost, and outcomes per tenant, so you know your margins.
Fleet analytics
See which agents, versions, and customers drive outcomes.
Fleet learning
Improvements compound across deployments; customer data stays put.
Warm execution
Runs start in seconds; progress streams into your UI.
The rule is simple: customer data stays with the customer, and agent improvements compound across the fleet.
Each workspace keeps its own data, knowledge, credentials, runs, and controls. An agent running for one customer can't reach another customer's workspace, and the isolation model gives your security team a clear boundary to review.
What crosses workspaces is agent behavior: outcomes, corrections, and failure modes, distilled into improvements to the shared definition and delivered as versions you roll out. One customer shouldn't have to teach the system twice, and one customer's data never becomes another customer's.
Every run is metered, logged, and attributed to its workspace. When a customer asks what happened, you have the answer: what ran, what it cost, what it did, and where it stopped.
Tell us what your product does, who uses it, and where an agent could do the work. We'll show you what that agent looks like shipped inside your product.