On September 9, 2026, Munich-based AI startup Atira raised a €13M seed round led by Accel to automate complex industrial quoting processes. This event highlights a massive enterprise push to replace manual, cross-departmental RFQ (Request for Quotation) evaluations with multi-agent architectures that sit between CRM and ERP systems to autonomously assess technical specifications and commercial risks.
Master Plan: Multi-Agent Orchestration Pipeline for Complex RFQ Parsing and Automated Technical Proposal Generation in 2026
Automate complex engineering RFQ parsing, supply chain data retrieval, and technical proposal synthesis using a durable multi-agent architecture with mandatory human-in-the-loop validation.
The Problem
In industrial manufacturing, responding to complex Requests for Quotation (RFQs) is a highly manual, error-prone, and time-consuming process. Sales engineers must parse dense, unstructured PDFs and CAD annotations, cross-reference required specifications against internal Product Lifecycle Management (PLM) systems, query Enterprise Resource Planning (ERP) databases for real-time component pricing and lead times, and finally synthesize this data into a legally binding technical proposal. This bottleneck limits the volume of bids a manufacturer can submit and introduces the risk of unprofitable quotes due to outdated supply chain data. A multi-agent AI orchestration pipeline solves this by delegating discrete tasks—document extraction, data retrieval, and technical writing—to specialized LLMs. However, because these proposals carry strict financial and compliance implications, the architecture cannot rely on autonomous dispatch. It requires a deterministic validation layer and a mandatory Human-in-the-Loop (HITL) QA gate, ensuring engineers only spend time reviewing and approving high-confidence drafts rather than assembling them from scratch.
Who this is for: Senior AI Engineer / Enterprise Architect at a mid-to-large manufacturing or heavy engineering firm.
Head-to-Head: Why This Model Won
The primary orchestrator requires exceptional zero-shot reasoning, strict adherence to complex engineering constraints, and high-quality technical writing. We evaluate flagship models based on their ability to synthesize unstructured RFQ requirements with structured ERP data without hallucinating specifications.
Primary workload evaluated: Complex RFQ reasoning and technical proposal synthesis — costs below are for 10,000 tasks of this workload.
| Model | Cost / 10k tasks | Best feature | Biggest drawback | Verdict |
|---|---|---|---|---|
| claude-opus-5 Anthropic | $3750 | Unmatched nuance in technical writing and strict adherence to complex, multi-step engineering constraints. | Premium pricing makes it cost-prohibitive for high-frequency, low-margin tasks. | Winner (Primary Role) |
| gpt-5-5 OpenAI | $4000 | Exceptional native tool-calling reliability for orchestrating sub-agents. | Slightly higher output token cost and occasionally produces overly verbose technical drafts compared to Claude. | Runner Up |
| deepseek-v4-pro DeepSeek | $261 | Incredible reasoning-to-cost ratio, making it highly viable for massive RFQ volumes. | Lacks native vision capabilities, requiring a separate model for any embedded diagrams not caught by the OCR layer. | Budget Pick |
| grok-4-6 xAI | $1300 | Fast agentic execution and strong real-time data processing capabilities. | Technical writing tone tends to require more prompt engineering to match strict enterprise engineering standards. | Rejected for Primary Role |
Recommended AI Stack
Primary Orchestrator & Proposal Writer → claude-opus-5 (Anthropic)
Why: Claude Opus 5 serves as the central brain, taking the extracted RFQ data and retrieved ERP context to draft the final technical proposal. Its superior adaptive thinking and technical writing capabilities ensure the output matches professional engineering standards while strictly adhering to the provided constraints.
~$0.375 / request
Math: Assume 50,000 input tokens (RFQ context, engineering guidelines, ERP data) and 5,000 output tokens (technical proposal). Input: 50,000 * ($5.00 / 1,000,000) = $0.25. Output: 5,000 * ($25.00 / 1,000,000) = $0.125. Total: $0.375.
Alternatives considered: gpt-5-5 was considered for its tool-calling, but Opus 5's technical writing quality is superior for client-facing proposals. deepseek-v4-pro was rejected for the primary role due to the need for absolute zero-shot reliability in high-stakes enterprise quotes.
OCR & Vision Extractor → gemini-3-1-flash-lite (Google)
Why: This model handles the initial ingestion of unstructured RFQ PDFs, including complex tables and embedded diagrams. Its native multimodal capabilities and extremely low cost make it ideal for brute-force extraction of dense engineering documents.
~$0.008 / request
Math: Assume 20,000 image/text input tokens per RFQ document and 2,000 output tokens (structured JSON extraction). Input: 20,000 * ($0.25 / 1,000,000) = $0.005. Output: 2,000 * ($1.50 / 1,000,000) = $0.003. Total: $0.008.
Alternatives considered: mistral-ocr-3 was considered but rejected due to its opaque flat-pricing structure which complicates per-request cost modeling. gpt-5-4-mini was rejected because Gemini 3.1 Flash Lite is significantly cheaper for high-volume multimodal input.
ERP/PLM Data Retrieval Agent → gpt-5-4-mini (OpenAI)
Why: This agent receives specific queries from the orchestrator (e.g., 'Check inventory and lead time for Part #12345') and translates them into strict JSON API calls to the internal ERP/PLM systems. GPT-5.4 mini is highly reliable at nested JSON tool-calling at a fraction of the cost of flagship models.
~$0.012 / request
Math: Assume 10,000 input tokens (API schemas, context) and 1,000 output tokens (API payloads). Input: 10,000 * ($0.75 / 1,000,000) = $0.0075. Output: 1,000 * ($4.50 / 1,000,000) = $0.0045. Total: $0.012.
Alternatives considered: claude-haiku-4-6 was considered, but OpenAI's models historically exhibit slightly better adherence to complex, deeply nested JSON schemas required by legacy ERP systems.
Output Validator & HITL Router → claude-haiku-4-6 (Anthropic)
Why: Before a proposal reaches a human engineer, it must pass an automated validation gate to ensure no mandatory constraints (e.g., ISO compliance clauses, minimum margin thresholds) were missed. Haiku 4.6 is exceptionally fast and cheap for this binary pass/fail schema checking.
~$0.003125 / request
Math: Assume 10,000 input tokens (draft proposal + ruleset) and 500 output tokens (validation report). Input: 10,000 * ($0.25 / 1,000,000) = $0.0025. Output: 500 * ($1.25 / 1,000,000) = $0.000625. Total: $0.003125.
Alternatives considered: gpt-5-4-mini was rejected because Haiku 4.6 is 3x cheaper for input tokens, which dominate the cost of this validation step where the entire draft must be read.
Compare migration costs
Run a live cost comparison before you commit:
System Architecture
Cost Breakdown
| Scenario | Cost |
|---|---|
| Per request (typical workload) | $0.3981 |
| Daily @ 100 req/day | $39.81 |
| Daily @ 1,000 req/day | $398.13 |
| Daily @ 10,000 req/day | $3981.25 |
| Monthly @ 1,000 req/day | $11943.90 |
| Monthly @ 10,000 req/day (at scale) | $119437.50 |
💰 Cost Optimization Strategies
Provider-specific tactics to cut the monthly bill above. Apply these AFTER you have a working baseline — premature optimization wastes engineering time.
claude-opus-5
Anthropic offers a 90% discount on cached read tokens via Prompt Caching. Cache the massive 20,000+ token system prompt containing your company's engineering guidelines, standard terms, and formatting rules. Since every RFQ uses this same base context, this will save ~$0.09 per request.
Anthropic offers a 50% discount via the Message Batch API. Move low-priority, non-urgent RFQs (e.g., standard parts re-orders with a 48-hour SLA) to the Batch API to cut the orchestrator cost in half.
gemini-3-1-flash-lite
Google offers a 90% discount on cached tokens. Cache standard boilerplate layouts or common supplier templates if processing multiple RFQs from the same client in a short window.
Google offers a 50% discount on batch processing. If you run nightly bulk ingestion of updated supplier catalogs or historical RFQs for RAG, route them through the Batch API. Not applicable for real-time client RFQ parsing.
gpt-5-4-mini
OpenAI automatically applies a 90% discount on cached input tokens above 1024 tokens. Ensure your ERP API schema definitions and tool descriptions are placed at the very beginning of the system prompt so they are consistently cached across all retrieval agent calls.
Not applicable — the ERP retrieval agent operates in the critical path of the orchestrator's reasoning loop and requires real-time latency to fetch pricing before drafting the proposal.
claude-haiku-4-6
Anthropic offers a 90% discount on cached read tokens. Cache the strict validation ruleset (e.g., ISO compliance checklists, margin calculation rules) to minimize the input cost of the validation gate.
Not applicable — validation must happen immediately before the draft is pushed to the Human-in-the-Loop review queue.
30-Day Implementation Plan
Week 1: Foundation
- Provision secure API access to internal ERP and PLM systems.
- Deploy the OCR & Vision Extractor pipeline using Gemini 3.1 Flash Lite to parse historical RFQs.
- Establish the durable execution framework (e.g., Temporal or LangGraph) to manage agent state.
Week 2: Core Build
- Develop and test the ERP Retrieval Agent (GPT-5.4 mini) with strict JSON schemas for inventory and pricing lookups.
- Draft the master system prompt for the Orchestrator Agent (Claude Opus 5), embedding engineering guidelines.
- Wire the Orchestrator to the ERP Agent, enabling multi-turn data retrieval.
Week 3: Production Hardening
- Implement the Validation Agent (Claude Haiku 4.6) to check drafts against mandatory business constraints.
- Build the Human-in-the-Loop (HITL) UI where sales engineers can review, edit, and approve the AI-generated drafts.
- Implement dead-letter queues and retry logic for failed ERP API calls.
Week 4: Launch & Optimization
- Enable prompt caching across all models to reduce per-request costs.
- Conduct shadow testing: run the pipeline alongside human engineers on live RFQs and compare outputs.
- Refine system prompts based on HITL rejection reasons and deploy to production.
Pros / Cons / Risks
✓ Pros
- Dramatically reduces RFQ turnaround time from days to minutes, increasing bid volume.
- Standardizes technical proposal quality and ensures compliance with internal engineering guidelines.
- Integrates real-time supply chain data, preventing quotes based on outdated pricing or lead times.
− Cons
- High token cost per complex RFQ due to the need for flagship reasoning models.
- Requires strict, ongoing maintenance of ERP API schemas and tool descriptions.
- OCR layer may still struggle with heavily handwritten markups on legacy CAD drawings.
⚠ Risks
- Hallucinated specifications or pricing could lead to legally binding, unprofitable quotes if the HITL gate is bypassed.
- High volume of automated queries could trigger rate limits on legacy on-premise ERP systems.
Recommended Infrastructure
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Get my instant AI audit — $39 →Common Questions
Why not use a single flagship model for the entire process?
Using a single model like Claude Opus 5 for every step—including basic OCR, simple API routing, and validation—would be prohibitively expensive and slow. By routing discrete tasks to specialized, cheaper models (like Gemini for OCR and Haiku for validation), you reduce the per-request cost by over 60% while maintaining flagship quality where it matters most: the final technical writing.
How do we prevent the AI from quoting incorrect prices?
This architecture relies on two critical safeguards. First, the Orchestrator is not allowed to invent pricing; it must use the ERP Retrieval Agent to fetch real-time data via strict API calls. Second, the mandatory Human-in-the-Loop (HITL) review queue ensures that a qualified engineer signs off on the final numbers before the proposal is ever sent to a client.
Can this system handle scanned PDFs or CAD files with handwritten notes?
The Gemini 3.1 Flash Lite OCR layer is highly capable of extracting text from complex, unstructured images, including standard CAD title blocks and typed annotations. However, heavily stylized handwritten notes remain a challenge for all current vision models. In cases where the OCR confidence is low, the system should flag the document for manual human extraction before proceeding.