Why Lamatic?
Lamatic is a managed platform that abstracts GenAI infrastructure so you can build and run Flows (agents, RAG, multi-step AI) without building the plumbing yourself.
The problem it solves
Shipping a production GenAI feature usually requires:
- Connecting and managing models — Multiple providers (OpenAI, Anthropic, etc.), API keys, fallbacks, and rate limiting.
- Data and context — Vector stores, chunking, embeddings, and retrieval so the model can use your data.
- Orchestration — Prompt middleware, async request/response handling, and an API to call from your app.
- Deployment and ops — Hosting, scaling, and observability (logs, traces, feedback).
Doing this from scratch is research- and time-intensive. Lamatic provides a no-code/low-code Flow builder, managed VectorDB and model connectors, GraphQL API, and edge deployment so you can focus on prompts and product logic instead of infrastructure.
When to use Lamatic vs building yourself
| Use Lamatic when… | Build yourself when… |
|---|---|
| You want to ship GenAI features in days, not months | You need full control over every layer (custom infra, proprietary models only) |
| Your team is small or cross-functional (product + eng) | You have a large platform team dedicated to GenAI infra |
| You need RAG, agents, or multi-step flows with minimal boilerplate | Your use case is a single, static prompt with no context or tooling |
| You want one place to build, deploy, and observe Flows | You already have a mature, in-house GenAI stack you want to keep |
What Lamatic gives you
- Flow builder — No-code/low-code editor to chain Nodes (LLM, RAG, data, logic) into deployable Flows.
- GraphQL API — One endpoint per project to run Flows from your app, CLI, or backend.
- Managed data and models — VectorDB, embeddings, and model connectors (OpenAI, Anthropic, etc.) without running your own services.
- Deploy on the edge — Flows run on Lamatic’s edge so you don’t host or scale the runtime.
- Observe — Logs, reports, and optional integrations (e.g. Langfuse) for debugging and improvement.
Lamatic grew out of building Dinnerfy (opens in a new tab) — a simple app that still required vectorizing data, connecting multiple LLMs, prompt middleware, and an API layer. We built Lamatic so other teams don’t have to rebuild that foundation. The full story and product vision are on our blog (opens in a new tab).