Free tool
AI architecture playground
Drag components onto a canvas, wire them together, and get a live score against real architecture rules, or run the guided wizard for an AI-drafted proposal tailored to your industry.
In short
A free, hands-on AI architecture builder. Drag LLMs, vector databases, retrieval and infrastructure components onto a canvas, connect them, and see a live score against real scoring rules for 16 common AI use cases. Export a branded PNG or PDF, or share a link that reconstructs the exact architecture. A guided wizard can also draft a tailored enterprise proposal after a few questions about your industry and goal.
Build an architecture
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How the scoring works
Each of the 16 use cases has required and recommended components, and connection rules that reward the right data flow and flag the wrong one. The score is not a vibe; it is worked out from exactly which components you have placed and how they are wired, the same way an architecture review actually works.
What the wizard adds
The guided wizard asks about your industry, goal, data sensitivity and existing stack, then asks Claude to draft a full delivery proposal: phases, team, a cost range and the components to start with. It's a starting point for a conversation, not a fixed quote, because pricing depends on specifics no four-question form can know.
What it does and does not do
- The canvas, drag and drop, connections, scoring and PNG export run entirely in your browser: nothing is sent anywhere to use them.
- The guided wizard's AI-drafted proposal and free-text use-case matching do call a backend, because generating that text genuinely requires it.
- Entering the guided wizard or skipping straight to the free-form canvas both ask for a name and work email once, so the team can follow up, but the canvas itself never gates on payment or a hard signup wall.
- It does not replace an architecture review. It tells you where to start one.
Not ready to build one? Get a quick recommendation
A lighter companion tool: four questions about volume, data sensitivity, load and team, and the deployment pattern that usually fits, with no canvas, no email, nothing sent anywhere.
Answer all four and the pattern that usually fits will appear here, with the reasoning behind it.
Go deeper
- The RAG study guideA complete technical guide to retrieval-augmented generation, for the team building it.
The detail behind it
- Private LLM or OpenAI API in 2026: How We Run the MathWhen does private LLM actually win in 2026? The cost thresholds, the engineering tax most teams forget, and 10 lessons we have learned shipping private LLMs
- How to Deploy a Private LLM on Your Own Infrastructure: Enterprise GuideLearn how to deploy private large language models on your own infrastructure. Covers data sovereignty, GPU requirements, model selection
- Why 87% of Enterprise AI Projects Fail, And How to Be in the 13%Discover the top 5 reasons enterprise AI projects fail and a proven 90-day PoC framework to ensure your AI initiative succeeds. Data-driven analysis.
- Building an AI Center of Excellence: The Organizational PlaybookHow to build an AI Center of Excellence that actually works. Covers org structure, hiring, governance, vendor evaluation, and a 6-month launch timeline.
Common questions
- Is the playground free
- Yes. There is no payment and no hard signup wall on the canvas itself. Starting the guided wizard or skipping straight to free-form building does ask for a name and work email once, so the team can follow up on what you build.
- Does it tell me what it will cost
- The free-form canvas does not price anything: it scores your architecture against real rules instead. The guided wizard's AI-drafted proposal does include a cost range, but it is a starting estimate for a conversation, not a quote, because a credible number depends on specifics a wizard can't know.
- Is my architecture sent anywhere
- Building, scoring, connecting and exporting a PNG all run entirely in your browser. The guided wizard's AI-drafted proposal and the free-text use-case matcher do call a backend, because generating that text genuinely needs to.
- Can I share what I build
- Yes. A share link encodes the architecture itself, so opening it reconstructs the exact canvas, including your layout, not just a summary.
- What actually decides private against hosted
- Usually three things in this order. Whether the data can legally or contractually leave your perimeter, whether your volume is high and steady enough to keep owned capacity busy, and whether anyone is available to operate it. The quick-recommendation tool below the playground walks through exactly this.
Check the answer against a real architecture
The tool gives you the pattern. A conversation with someone who has deployed it tells you what it costs you specifically.
Book a consultationThe AI practicePrivate LLM deployment