Design reliable LLM pipelines.
Visual pipelines, routers, API actions, prompt contracts, and output contracts give teams structure around model behavior.
AI operating layer
The AI layer for your software.
LLMLab connects your codebase, APIs, and live documents so AI can do more than chat. It can take authenticated actions, route tasks to the right model, and optimize cost across every request.
Operating layer
Real AI features need more than a chatbot.
LLMLab connects your knowledge, APIs, permissions, model choices, logs, and cost controls, so LLMs can safely answer questions and take useful actions inside the tools you already run.
Ground runs in real systems.
Attach live knowledge bases, public APIs, secrets, and auth bindings without rebuilding every connector.
Ship AI features where users work.
Deploy pipelines through web integrations, app integrations, webhooks, 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
Build a production AI stack without hiring a dedicated team
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.
Pipeline execution
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 pipelines.
Use cases
Where teams put the operating layer to work.
Start with one pipeline, then extend the same runtime into new surfaces, integrations, and product experiences.
Support agents that understand your software
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 pipelines.
API automation
Turn public or internal APIs into structured actions that can be chained, reviewed, and monitored.
LLM-powered product features
Embed pipeline-backed AI features into applications without owning every infrastructure component.
Human-reviewable AI operations
LLMs can automatically flag uncertain, sensitive, or high-impact runs for human review, with inspectable logs and node-level execution details for every step.
Custom model routing and escalation
Generate synthetic data and fine-tune low-cost models specific to your tasks. Automatically escalate to higher cost models when needed.
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. Test pipelines, knowledge, APIs, model routing, monitoring, and deployment without restrictions.