AI automation for RFQ triage, sales ops, and internal knowledge
B2B manufacturing AI — RFQ intake, quote-draft automation, specs search, sales operations. $3,000/mo retainer. Safety-critical workflows stay manual.
Who this is for
Ops director or owner at a $10M to $100M manufacturer where quote requests pile up, RFQ triage is manual, and sales ops has dozens of repeatable email flows.
The pain today
- RFQ intake is manual and backlog grows weekly
- Quote drafting takes senior estimator time on every bid
- Internal knowledge (specs, SOPs, past jobs) is scattered and slow to find
- Sales ops has dozens of repeatable email flows eating time
- No one on the team to set up AI safely
The outcome you get
- AI automations for manufacturing ops on $3,000/mo retainer
- RFQ intake with structured extraction and routing
- Quote-draft generation from past jobs and pricing logic
- Internal knowledge search across specs and SOPs (RAG)
- Sales email automation with human approval
Highest-ROI AI automations for B2B manufacturing
Three areas deliver clear ROI. RFQ intake and triage — LLM reads incoming RFQs (email, PDF, forms), extracts structured data, routes to the right estimator with priority flag. Cuts triage time 50 to 70 percent. Quote-draft generation — LLM pulls from past similar jobs and structured pricing logic to draft quotes for estimator review and finalisation. Internal knowledge search — RAG system over specs, SOPs, and past job data so sales and ops can answer customer questions in seconds instead of digging through PDFs. Each respects human judgement while eliminating lookup and typing work.
RFQ intake and quote-draft automation
RFQs arrive in many formats — email attachments, spec-sheet PDFs, web-form submissions, EDI feeds. LLM extracts structured data (materials, dimensions, quantities, tolerances, target delivery) and routes to estimators based on product line. For standard products with clear pricing logic, LLM drafts initial quotes by matching against past jobs. Estimator reviews and finalises. For complex custom work, LLM triages and provides context — estimator builds the quote. Saves 30 to 60 minutes per RFQ on average. Clear ROI at volume.
Internal knowledge search (specs, SOPs)
Mid-size manufacturers accumulate thousands of specs, SOPs, test reports, and past job data. Finding the right document wastes staff hours weekly. RAG system (retrieval-augmented generation) indexes all internal documents and lets staff ask natural-language questions. 'What torque spec for 316 stainless fasteners?' returns relevant passages from specs in seconds. For compliance (ISO, AS9100), RAG preserves source-document citations in every answer. Typical build: 4 to 6 weeks for initial RAG setup; quality improves over 2 to 3 months with feedback.
Pricing and engagement model
$3,000/mo retainer. Covers AI integration, RAG setup, prompt engineering, monitoring, iteration. 14-day money-back guarantee. Cancel anytime. 100 percent code ownership under Work Made for Hire. LLM costs pass through. Vector DB hosting (for RAG) pass through. For manufacturers with sensitive IP (proprietary processes, customer-specific work), self-hosted open-source models are an option — adds infrastructure cost but keeps data fully in your control.
Case: LAK Embalagens — B2B manufacturing digital playbook
LAK Embalagens: Brazilian B2B packaging manufacturer rebuild with catalog-first site, 45 percent bounce reduction, 3x impressions, top three rankings. Stack: React, Next.js, TypeScript, Tailwind CSS. The digital-showroom pattern applies to AI work — structured product data plus AI drafting creates faster, better customer-facing content and internal tools. Manufacturers with clean catalog data benefit most from AI because the source data is structured. For manufacturers without catalog discipline, the first step is often data cleanup before AI can deliver.
When a process is too safety-critical for AI
Some manufacturing processes stay fully manual regardless of AI advances. Safety-critical calculations (load ratings, dosage, tolerances that could cause injury if wrong). Regulatory submission content (medical device, aerospace, pharma). Anything where an AI mistake creates physical harm or regulatory violation. I help you draw that boundary in week one. Within the boundary, AI delivers real ROI on throughput and research. Outside the boundary, human experts do the work as they always have.
Recent proof
A comparable engagement, delivered and documented.
Turned a B2B manufacturer into a digital showroom
Designed and developed a high-performance institutional website to showcase packaging solutions and generate qualified leads.
Frequently asked questions
The questions prospects ask before they book.
- Can AI read spec sheets and drawings?
- Yes, with caveats. Text from spec sheets extracts well via LLM with vision (GPT-4 Vision, Claude Vision). Engineering drawings (dimensional drawings, tolerances, GDT) are harder — vision models handle basic extraction but miss nuance a senior engineer catches. For drawings, AI helps triage and extract high-level data; human review remains critical for anything fabrication-bound. Scope the AI role carefully — extract what is obvious, flag for human review on anything ambiguous.
- How does RAG work for internal specs?
- Internal specs, SOPs, and reference documents get chunked, embedded (converted to semantic vectors), and stored in a vector database (Pinecone, Qdrant, Postgres pgvector). When staff ask questions, relevant chunks retrieve based on semantic similarity. LLM reads the chunks and generates an answer citing source documents. For manufacturing, citations are mandatory — staff need to verify the answer against the actual spec before acting. Typical setup: 4 to 6 weeks; improves over time with feedback.
- What about data privacy for customer RFQs?
- Customer RFQs often contain proprietary information (specs, quantities, target markets). For manufacturers handling sensitive customer data, hosted LLMs with DPAs and no-training commitments handle most needs. For extreme sensitivity (defence, pharma IP), self-hosted open-source models on your infrastructure. Documented in customer-facing privacy and security posture.
- Can AI learn from past jobs?
- Yes. Past job data (material, quantities, pricing, margin, outcome) feeds a RAG system or structured pricing model. New RFQs match against past similar jobs to inform pricing suggestions. For manufacturers with clean ERP data, this is one of the highest-ROI AI features. For manufacturers with messy data, initial data cleanup is part of the first-month work.
- How do you handle multi-language for export markets?
- AI handles multi-language well — same prompt infrastructure, different language outputs. For manufacturers with customers in Spanish-speaking, Portuguese, German, French markets, AI drafts multilingual responses consistently. Human review remains for business-critical customer communication. For internal knowledge search, RAG can answer in the staff's language regardless of source document language. Common asked for Latin American and European manufacturers.
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