AI agents for support and sales, deployed in your own environment

Use AI where standard SaaS would be blocked: leezy.ai can run deployed in your own environment, with your own LLM, private cloud, or full on-premise setup. Customer data, model traffic, and integrations stay under your control while your team automates support, lead qualification, and scheduling.

Discuss enterprise setup

Tell us about your environment, data rules, and target workflows. We will map the deployment and demo the setup around your requirements.

Already building with leezy

On-prem: Deployment; Run in your cloud, on your servers, or dedicated
Custom: Integrations; Connect CRM, helpdesk, ERP, and internal APIs
Own LLM: Models; Use private models, hosted models, or both
99.9%: Uptime SLA; Available on dedicated infrastructure

Private deployment paths

Choose the operating model that fits your governance.

Standard: Managed multi-tenant; Start quickly on managed infrastructure when the use case does not require isolation or private model routing.
On request: Dedicated single-tenant; Run on isolated storage with custom retention, a private domain, dedicated capacity, and stronger operational separation.
Full control: Your cloud or on-premise; Deploy into your AWS, Azure, or GCP account, or onto your own servers, with model routing and integrations under your control.

Your model, your data, your approvals

Bring leezy.ai into the systems your teams already use, then decide exactly where the agent runs, which model it uses, how far usage can scale, and when a human needs to approve or take over.

Use your own LLM

Connect OpenAI, Anthropic, Llama, a self-hosted model, or your existing model gateway. Keep control over model choice, data flow, and cost.

Connect internal workflows

Wire the agent into CRM, helpdesk, ERP, booking tools, identity providers, or internal APIs so automation follows your real processes.

Capacity based on your setup

With dedicated or on-premise deployment, agent executions, tokens, members, and knowledge sources can scale with the infrastructure you choose. The limit is your agreed model, capacity, and SLA, not an artificial SaaS limit.

Use AI without handing over customer data

Keep sensitive conversations, knowledge sources, and model calls inside the environment your IT and legal teams already govern. Every deployment can include encryption, audit logs, role-based access, and retention rules matched to your policies.

Protected data flows

Encrypt data in transit and at rest, define retention windows, and keep model traffic within the approved deployment boundary.

Clear ownership and roles

Control who can edit agents, approve knowledge changes, review conversations, and manage deployment settings.

Audit-ready operations

Track actions, configuration changes, and sources with actor, timestamp, and target for IT, legal, and compliance reviews.

Define data retention

Set how long conversations, leads, and support context are stored, with purge rules aligned to your internal policies.

From first call to controlled production

A focused team scopes the use case, maps the technical boundary, builds the integrations, and stays involved after launch so the rollout stays useful and governable.

01
Map the use case
We review support volume, sales workflows, data classes, systems, and approval needs, then define the first automation target.
02
Build the private setup
We connect tools and internal APIs, configure roles, model routing, and retention rules, then validate the behavior against your requirements.
03
Launch with ownership
Go live on managed, dedicated, customer-cloud, or on-premise infrastructure with named contacts for ongoing changes.

For teams where generic SaaS is not enough

Enterprise teams use leezy.ai when customer conversations must connect to internal systems, follow strict approval paths, or stay inside a controlled infrastructure boundary.