Lyzr

Lyzr is an agent-as-a-service platform that lets enterprises deploy private, low-hallucination AI agents on their own data in days rather than months.

Overview

Lyzr packages agent building into enterprise-friendly components: retrieval with built-in hallucination checks, structured workflows, and a studio for assembling agents from prebuilt skills. Agents run inside the customer’s virtual private cloud, which satisfies data-residency requirements while still offering a managed experience. The platform covers common business agents — sales development, HR helpdesk, data analysis — and its orchestration layer coordinates multiple agents on shared tasks. An evaluation framework scores agent answers for groundedness and completeness before they reach users, and analytics surface the conversations that need review. For teams without deep AI engineering staff, Lyzr’s blend of templates, guardrails, and private deployment shortens the distance between pilot and production. Its studio ships with industry templates that encode common business roles, and the SDK lets engineering teams embed agents directly into their own products while Lyzr handles retrieval, evaluation, and operational monitoring underneath. Deployment guides cover common cloud setups, and support engineers assist with the first production rollout.

Key Features

• Private VPC deployment with managed agent operations
• Groundedness checks that score and filter low-confidence answers
• Prebuilt business agents for sales, HR, and analytics workflows
• Multi-agent orchestration on shared tasks and data
• Evaluation analytics highlighting conversations needing review
• Embeddable SDK for putting agents inside your own product surfaces

Pricing

Free evaluation tiers let teams test agents on sample data. Subscription pricing scales by agent, message volume, and deployment footprint, with enterprise plans adding custom models and dedicated support.

Who Is It For

Mid-market and enterprise teams that need agents running on proprietary data under compliance constraints, without assembling an AI platform from open-source parts. It suits enterprises that want agent capabilities without hiring a large platform team, and product companies that would rather focus engineering effort on their domain than on agent plumbing.