This free visual tool lets you design and prototype AI automation workflows without writing code. Pick one of five engineering-specific templates — Spec Writer, Calculation Checker, RFI Drafter, Report Generator, or Custom — to instantly load a realistic starting workflow. Then customize it: click any node to edit its properties, add new nodes from the palette, and click Run Workflow to simulate the output. Use this to plan and communicate your AI automation design before building it in a production platform like n8n, LangChain, or Make.com.
Spec Writer: Takes a drawing or calculation as input, parses the document, passes it to an LLM model with a spec-writing prompt, and outputs a formatted technical specification section. Ideal for automating Division 16/26 electrical specs, mechanical equipment specs, or structural material specs.
Calculation Checker: Feeds a calc sheet through an LLM that checks for code compliance errors (NEC 220.82, ASCE 7, AISC 360), unit inconsistencies, and arithmetic mistakes, then routes findings through a condition node that flags errors vs. passes.
RFI Drafter: Takes an RFI description and project document context, passes both to an LLM, and outputs a draft RFI response with appropriate clarifications and references.
Report Generator: Summarizes field inspection data (notes, photos descriptions, punch list items) into a structured inspection report with executive summary, findings, and recommended actions.
Custom Workflow: A blank canvas. Add any combination of the 5 node types to design a workflow specific to your engineering process.
📥 Input Source — The entry point of your workflow. Represents data coming in: a PDF upload, a form submission, a spreadsheet, an API call, or typed text. Configure the source type and format in the node properties panel.
📄 Document — A document processing step. Can represent a PDF parser, an image-to-text OCR step, a template loader, or a knowledge base chunk retriever. Typically sits between an input and an LLM node.
🤖 LLM Model — The AI processing step. Choose the model (GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro) and configure the system prompt and output format (JSON, Markdown, plain text). This is where the intelligence happens.
🔀 Condition — A branching logic node. Routes the workflow down different paths based on the LLM output — for example, routing to a 'Pass' output if no errors are found, or an 'Error Report' output if issues are detected.
📤 Output — The final delivery node. Represents where the result goes: a downloadable document, an email, a database write, a webhook, or displayed text.
1. Select a template from the left sidebar or start with Custom Workflow. 2. Click any node on the canvas to select it — the right panel shows its editable properties. 3. Rename the node, change its type, or configure its settings in the properties panel. 4. Add new nodes using the '+ Add Node' button in the toolbar and selecting a node type. 5. Click '▶ Run Workflow' to simulate the workflow and see mock output appropriate to your template. 6. Use '💾 Save' to save the workflow name. Use '🗑️ Clear' to reset the canvas. 7. When your workflow design is finalized, implement it in a production platform: n8n or Make.com for no-code, LangChain/LangGraph for Python-based agents, or the OpenAI Assistants API for simpler single-LLM flows.
No-code platforms: n8n (self-hostable, 400+ integrations, built-in AI agent nodes), Make.com (visual automation, strong PDF/HTTP modules), Zapier (widest third-party integration library). Best for engineers who want to automate without coding.
Python frameworks: LangChain (mature ecosystem, 100+ document loaders, VectorStore integrations), LangGraph (stateful multi-step agents with explicit state machines), CrewAI (role-based multi-agent systems). Best for custom logic or high-volume production use.
Key implementation steps: (1) Choose a document loader matching your input format (PyMuPDF for PDFs, openpyxl for Excel, Pillow+pytesseract for drawings). (2) Chunk long documents with RecursiveCharacterTextSplitter. (3) Store embeddings in a VectorStore (Chroma, Pinecone, pgvector) if doing RAG. (4) Write a system prompt that specifies the engineering output format. (5) Parse LLM output into structured data. (6) Deliver results to the output destination.
An AI workflow builder is a visual tool for designing automation pipelines that connect data inputs, LLM models (like GPT-4o or Claude), logic conditions, and outputs — without writing code. It lets engineers prototype AI-assisted processes like spec writing, calculation review, or report generation before building them in a production platform.
Common engineering AI workflows include: spec writing (drawing/calc → LLM drafts spec section), calculation checking (calc sheet → LLM flags code compliance errors), RFI drafting (RFI description + project documents → LLM drafts response), inspection report generation (field notes → formatted report), and submittal review (submittal → checked against spec requirements).
No. This tool is a visual workflow prototyper that runs entirely in your browser and simulates workflow execution with realistic mock outputs. When ready to build a production workflow, use a platform like n8n, Make.com, LangChain, or LangGraph with your own API key.
GPT-4o (OpenAI) and Claude 3.5 Sonnet (Anthropic) are the strongest for structured engineering outputs. Claude has a 200K context window, making it ideal for reviewing full calc packages. For cost-sensitive high-volume tasks, GPT-4o mini or Claude 3.5 Haiku are more economical.
Connect input nodes to your data sources: PDFs extracted with PyMuPDF, Excel/CSV loaded with pandas, drawings processed with a vision model, or project data from Procore/Autodesk APIs. The LLM node sends prompts to your chosen API. Output nodes deliver results — writing to a document, emailing stakeholders, or updating a database.