Multi-agent AI SaaS
EngageLayer
Build, ground, deploy, and operate AI agents.
A full-stack agent platform that connects visual orchestration, RAG knowledge, secure deployment, observability, and billing.
Overview
The product
EngageLayer gives teams one workspace for designing multi-agent workflows, grounding them in private knowledge, deploying them through widgets and APIs, and operating them with shared usage context and observability.
Challenge
What had to change
AI prototypes become difficult to ship when orchestration, document retrieval, provider failures, access control, analytics, limits, and billing are handled as separate concerns.
Response
The system built
The product turns those concerns into one connected lifecycle: model the graph visually, attach workflow-scoped knowledge, deploy through controlled surfaces, and measure every production run.
Product capabilities
What the product needed to do.
Visual workflow builder
Compose orchestrators, specialists, nested agents, and conditional routes on a graph.
Multi-provider runtime
Run workflows through provider adapters with retries and resilience controls.
RAG knowledge engine
Extract, chunk, embed, and retrieve documents with workflow-level scope.
Embeddable widgets
Configure branded chat surfaces and deploy them with API-key access.
Analytics and logs
Track queries, tokens, cost, latency, errors, and agent-level execution.
Billing and usage
Connect Stripe plans, limits, billing cycles, and preflight enforcement.
Technology
The stack behind the experience.
Product interface
Agent runtime
Knowledge and data
Operations
Visual case study
Six views of the product system.
The deck moves from product context through the problem, features, architecture, delivered system, and final product presentation.
Delivered system
EngageLayer moves beyond the chatbot demo by treating an AI agent platform as a connected operating system.
- Visual multi-agent workflows
- RAG knowledge pipeline
- Widgets and secure APIs
- Analytics, limits, and billing
More work
