Graph-Integrated Development Environment
Build AI solutions that work in production.
Where engineers and researchers can build pipelines, train models, deploy endpoints, and collaborate with their team in a single visual graph. EU-hosted. Runs on your hardware if you want it to.
No payment required · EU-hosted · Local compute on free tier · GDPR by design
You're not bad at AI.
You're managing five tools that were never designed to work together.
Tool fragmentation
Building a production AI system in 2026 means stitching together Jupyter for prototyping, VS Code for writing the actual code, Git for version control, MLflow for experiment tracking, and at least one LLM API layered on top. That's five tools, five context switches, and not a single shared data model between them.
None of these tools know about each other. Your training data doesn't flow into your deployment pipeline. Your experiment tracking lives in a different universe from your version control. Change one LLM provider and you're rewriting 200 lines of integration code.
The competency gap
AI talent is scarce, and the US and China are paying millions to lock it up. Someone on your team still has to understand and own the solution.
Research-to-production gap
Working models in notebooks that cannot be shared, reproduced, or deployed without DevOps skills your research team shouldn't need.
EU compliance overhead
No mainstream AI development platform is GDPR-by-design. You're either accepting the risk, building custom infrastructure, or putting the project on hold.
The Graph-IDE.
Lovable generates apps. Cursor writes code. Dify builds RAG prototypes. Mainly builds the system that runs in production.
The graph is the primary engineering artefact, not just a visualisation of your code. It's persistent, versioned, auditable, and shareable. Code lives inside nodes; the graph is the architecture. This is what makes it possible to train a model, deploy it as a live endpoint, collaborate with your team in real time, and prove every inference decision to a compliance auditor, all without leaving a single interface.
Grounded Vibing
Vibe coding moves fast and loses architecture. Grounded Vibing moves fast inside an enforced structure. The graph is the discipline.
Build
Structured AI Development
Python nodes in a directed acyclic graph. Every node versioned, every connection typed, every architecture auditable. A real engineering artefact.
Deploy
Models to Endpoints
Train domain-specific models, deploy them as endpoints, and route inference through a provider-agnostic LLM proxy, all in the same graph. Run on EU-hosted cloud, your own machine, or an on-prem k3s cluster with custom Docker per project.
Collaborate
One Graph, One Team
Work on the same graph at the same time. The graph becomes the shared language between researchers, engineers, and stakeholders. Every change is tracked, every decision is visible, and the architecture speaks for itself in review meetings.
What the others can't do.
Eight capabilities we spent four years building. Every one of them is an architecture decision you have to make on day one. You can't bolt them on later.
In-graph model training
Train domain-specific PyTorch models in the same environment where you deploy them. No visual AI competitor does this.
BYOC at free tier
Local compute via a single machine. No US cloud required. From your first day on the platform.
EU data sovereignty, proven
VINNOVA-funded and proven in clinical deployment. Per-model hosting origin visible on every inference call.
Graph-aware AI copilot
Ask AI reads your current graph state and suggests the next node in context. It understands the architecture you're actually building.
Real-time multi-user collaboration
Full team collaboration on a shared live graph. The feature that turns a developer tool into a team purchase.
Custom Docker per project
Every project runs in an isolated, configurable container. Complete environment reproducibility across your team.
Granular compute metering
Per-second billing with cost visibility per node. Know what you're spending before you scale it.
Cross-org workflow sharing
Share working graphs with external collaborators or deploy them as middleware to end customers without exposing your infrastructure.
Your data stays where you put it. We can prove it.
Mainly.AI is EU-first because of how the infrastructure was built. Every LLM inference call surfaces the hosting origin of the model being used, visible to your team in real time. Your compute runs on EU-hosted cloud, your own machine, or your on-premises cluster. No data routes through US-based infrastructure unless you explicitly choose it.
This is baked into the platform, not something you configure after the fact.
EU AI Act
The EU AI Act is live and enforced. Every competitor who routes inference through US-based APIs is your compliance team's problem. Mainly.AI is not.
Credentials
VINNOVA-funded
Swedish government innovation funding. EU-first is a funding requirement.
ASSET research partner
Privacy-preserving ML for autoimmune disease research. Live and PubMed-published.
GDPR-by-design
Per-model data residency metadata on every inference call. Auditable from the ground up.
We tested Lovable, Cursor, and n8n. Mainly.AI was the only platform where we actually shipped something a customer could use.
Civaro
Intelligent municipal permit system deployed across multiple Swedish municipalities.
ASSET consortium
Privacy-preserving AI in regulated clinical research. Live, peer-reviewed, and published.
A+ Intelligence
Two students, two months, one AI assistant for teachers. Acquired by Swedens fastest growing school platform.
Already using Langflow, Dify, or Lovable?
See where each tool stops, and what Mainly adds on top.
| If you're using… | Where it stops | What Mainly adds |
|---|---|---|
| Langflow / Flowise | Inference-only. No model training. No EU data residency. No real-time collaboration. | Train models in-graph. Deploy to your own infrastructure. EU-first by default. |
| Dify | Excellent for prototypes. Licence restricts commercial multi-tenant SaaS. No local compute. | Full commercial deployment. BYOC at free tier. Your data on your infrastructure. |
| Lovable / Cursor | Generates code fast. No architecture enforcement, no deployment, no EU hosting. | The backend for the system Lovable built the frontend of. When the prototype needs to become a product, this is what you switch to. |
| MLflow / W&B | Experiment tracking only. No visual workflow layer. No collaborative development. | Replace your tracking tool with a full development environment. Experiment and deploy in the same graph. |
| Custom Kubernetes | Maximum flexibility. Six months to first deployment. No collaboration, no visual layer. | Same infrastructure control, with a collaborative visual platform on top, from day one. |
You've built the prototype. Now build the system.
Most AI tools help you generate a prototype. Mainly.AI picks up where they leave off. When the prototype needs to run in production, hold up under audit, scale to a team, and stay on your infrastructure. Start with the Hello World. Build your first working graph in under 30 minutes. Free, no card required.
