Design reliable LLM workflows.
Visual workflows, routers, API actions, prompt contracts, and output contracts give teams structure around model behavior.
AI operating layer
The AI layer for your software.
LLMLab gives teams one managed platform for adding production-ready LLM capabilities to the products and tools they already run, including workflows, knowledge-grounded support, model routing, API actions, observability, and deployment surfaces.
By the ten-minute mark, we've connected our live GitHub repo and website, parsed our API into actionable endpoints, and embedded a support agent that can guide users through the product and perform real tasks, all for less than $0.10. The assistant in the corner is that same agent, grounded in our live codebase and automatically updating if our API changes.
Operating layer
One operating layer for the full LLM application stack.
Production LLM software usually needs workflow orchestration, vector search, prompt and version control, API integrations, model routing, auth, logs, billing, and deployment surfaces. LLMLab brings those pieces into one operating layer.
Ground runs in real systems.
Attach knowledge bases, public APIs, secrets, and auth bindings without rebuilding every connector.
Ship AI features where users work.
Publish workflows through web integrations, app integrations, API-accessible workflows, and support-facing surfaces.
Keep production runs observable.
Use runtime logs, node executions, monitoring, review loops, access control, and cost controls to improve safely.
The LLMLab stack
A layered architecture for production AI systems.
Founders, engineers, and operators can reason about the same system: where it deploys, how it runs, what it knows, which models it uses, and how it is governed.
Deployment surfaces
Website integrations, app experiences, support tools, API endpoints, Slack, Discord, and hosted assistants.
Workflow runtime
Orchestration for routing, retrieval, clarification, API actions, validation, and controlled outputs.
Knowledge + API layer
Connected docs, websites, files, API definitions, secrets, auth bindings, and tool schemas.
Model layer
Model routing, escalation paths, provider flexibility, prompt library, and output contracts.
Security, permissions, billing, observability
The control plane underneath every run: access, usage, cost visibility, run history, and review workflows.
Use cases
Where teams put the operating layer to work.
Start with one workflow, then extend the same runtime into new surfaces, integrations, and product experiences.
Support agents that understand your app
Let users ask questions about your product and get answers grounded in live codebase, docs, and API context, with approved actions routed through your APIs.
Internal copilots connected to company knowledge
Give teams a governed way to retrieve information, use tools, and execute repeatable workflows.
API automation workflows
Turn public or internal APIs into structured actions that can be chained, reviewed, and monitored.
LLM-powered product features
Embed workflow-backed AI features into applications without owning every infrastructure component.
Human-reviewable AI operations
Keep high-impact runs inspectable with logs, node-level execution details, and review loops.
Custom model routing and escalation
Route tasks by policy, cost, quality needs, or fallback behavior across the model layer.
Get started
Build the layer your AI product runs on.
Try the full platform without a credit card. Every new workspace includes $5 in credit, which goes further than you might expect for testing workflows, knowledge, APIs, model routing, monitoring, and deployment.