RenderDraw lets every block in your workflow use a different AI provider, model, system prompt, and context. Configure each independently. Pass outputs between them. Each block is context-isolated — it only sees what you explicitly give it.
The most effective multi-model workflow pattern for RFP automation uses three blocks, each optimised for its specific task. This pattern is also used for RFI responses and complex takeoff workflows.
Reads the submitted PDF or drawing. Extracts dimensions, quantities, symbols, and tabular data. Returns structured JSON.
Receives Claude Sonnet's extraction + full RFP document + knowledgebase context. Reasons across all three to draft a compliance matrix and response sections.
Receives Claude Sonnet's draft. Reformats it into the required proposal template structure. Fast and cheap — no heavy reasoning needed here.
Structured proposal sections written to workbook + Word template populated for human review
Blocks are context-isolated by default. Block 2 does not automatically see everything Block 1 saw. You explicitly wire the output of one block into the input context of the next. This is intentional — it prevents context window bloat and keeps each block's reasoning focused.
You control three things for each context pass:
Why isolation matters: If Block 2 received Block 1's entire raw context (system prompt, retrieved knowledgebase chunks, full document), you would double or triple the token cost with no benefit. Block 2 only needs Block 1's output — the extracted data — not how it was extracted.
Cost optimization: A typical RFP workflow can use a vision-capable model for extraction, a long-context model for reasoning, and a fast model for formatting. Using one premium model for every step costs materially more than matching model class to task.
In the Workflow Editor, drag an AI Block onto the canvas. In the block's settings panel, select a provider and a vision-capable model. Set the system prompt to describe the vision extraction task. Enable Vision input and wire the workflow's input document to this block.
Add another AI block. Select the provider and long-context model you want for reasoning. In the Context section, add a Prior Block Output context source and select Block 1. Also add a Knowledgebase context source and select your RFP library. Set topK to 5 and threshold to 0.72.
For the Block 1 → Block 2 context pass, choose Format: JSON so the reasoning block receives structured extraction data. Optionally select only specific output fields (e.g., extracted_items, drawing_dimensions) rather than the full output to reduce token cost.
Add a third AI block. Select a fast, low-cost model. Wire Block 2's output as context. Set the system prompt to describe the formatting template. Set Temperature: 0.0 — you want deterministic formatting, not creative variation. Lower temperature on formatting blocks reduces hallucinated structure.
Connect Claude Sonnet vision, Claude Sonnet reasoning, and a fast formatting model in a single workflow. No infrastructure required.