Ship AI features without turning every one into an infrastructure project.
Launch AI chat, copilots, agents, and workflows across web, Slack, API, email, and more — on a production runtime your team doesn’t have to build from scratch.
The AI feature isn’t the bottleneck. The infrastructure around it is.
Your team can prove the concept quickly. The delay comes from everything after: deploying to a second channel, handling credentials safely, making the behavior reliable, and coordinating across frontend, backend, and AI orchestration. That coordination cost is what kills roadmap velocity.
Surface fragmentation
The feature works in one place. Product wants it in Slack, in the app, behind an API, and triggered by email. Each channel becomes a separate engineering project.
Security overhead
Real AI features need real credentials. Every integration needs token management, credential isolation, and audit trails. That’s weeks of platform work per feature.
Behavior inconsistency
The demo impressed everyone. In production, the same input produces different results. Without structured execution, reliability becomes a full-time job.
Less coordination. More shipping.
One feature, many channels
Build the capability once. Deploy it to web chat, Slack, API, email, MCP, and more. Each surface has independent configuration — adding a channel is a product decision, not an engineering sprint.
Security handled at the platform level
Protected parameters hide credentials from the AI model. Your team doesn’t need to build credential management per feature. It’s built in.
Reliable, repeatable execution
Agents run with cost budgets and iteration controls. Flows execute with structured logic, branching, and error handling. The feature behaves the same way every time it runs.
Fewer handoffs
The runtime handles orchestration, tool execution, deployment, and security. Your team focuses on the product logic — what the AI does, not how it runs.
The prototype proves the idea. The runtime proves the product.
Twelve percent of AI agent projects make it from pilot to production. The failure isn’t the idea — it’s the infrastructure between a working demo and a reliable product feature. Runtype is the runtime that closes that gap for your team.
Start with one. Expand when it lands.
Embedded product assistant
Help users onboard, troubleshoot, and complete tasks inside your product. Starts as web chat, extends to email and API when support needs grow.
Internal operations copilot
Give your team faster access to workflows, triage, and repetitive tasks. Deploy in Slack where the team already works. Add a dashboard surface later.
Multi-channel support agent
One agent handles customer questions across web chat, email, and Slack. Same logic, same quality, same audit trail — three fewer projects for engineering.
The roadmap impact
Common questions
Do we need to rebuild everything to use Runtype?
No. Start with a single feature on a single surface. The architecture is designed to expand — add capabilities and surfaces incrementally.
Can engineering still control implementation details?
Yes. Runtype handles the runtime platform — orchestration, security, deployment, execution. Your engineers control the product logic: what the AI does, which tools it uses, how it responds.
How does this affect our existing infrastructure?
Runtype is additive. It doesn’t replace your backend or frontend. It’s the AI product layer — your team calls it via API, embeds it via widget, or connects it via MCP. It integrates, it doesn’t take over.
Can we launch in one place and expand later?
That’s the core model. One capability, one surface. When the feature lands, add more surfaces without rebuilding the logic.
Ship the feature, not the platform.
Free to start, upgrade when you ship.