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Manufacturing: Custom AI Solution

Manufacturing AI’s real sweet spot is collapsing pre-sales engineering — customers send PDF specs, Excel sheets, drawings; AI extracts machine-readable fields so RCA (Revenue Cloud Advanced, formerly Salesforce CPQ) engineers move from "read + type" to "review." Design principle: AI is augmentation; engineers sign every quote, sales sign every contract commitment.

// AI use cases to start with

RFP / spec structured extraction

Extract machine-readable fields from PDF specs, Excel sheets, drawings (with confidence scores) so RCA engineers review rather than type.

Technical support + warranty case summary

Product manuals, prior cases, warranty rules feed RAG; service agents get a draft reply and relevant case history in the first second.

Catalog multilingualisation

Auto-produce multilingual versions of an EN catalog (human QA before publish), reducing the marketing team’s multilingual maintenance load.

// How EKel would deliver it
  1. 01Define hard constraints first: which commercial-sensitive data (negotiation floors, costs, process IP) must never reach commercial LLMs; which use cases are off-limits (autonomous quoting, contract change); which require mandatory engineer review.
  2. 02Choose deployment: negotiation floors / costs / process IP go through private deployment; catalogs and public specs may use commercial LLMs. Models prefer Llama / Mistral / Gemma open-source families.
  3. 03Build a reference dataset from real RFPs, complex specs, return disputes, warranty rulings — 100+ scenarios with explicit confidence-labelling tests and low-confidence forced-review verification.
  4. 04Post-launch: full audit log + monthly human-sampled review + regression tests on every model upgrade. If the client already runs Salesforce, our stronger recommendation is Salesforce + Agentforce + Manufacturing Cloud.
// Best fit
  • →B2B manufacturers with high pre-sales engineering load, large RFP / spec volume, and complex RCA — wanting AI to free engineer time for high-judgement work.
  • →Manufacturers with a clear commercial-sensitive-data classification policy — knowing what is allowed and what is not.
  • →Programs that want to prove ROI on one vertical use case (e.g., spec extraction) before rebuilding the whole pre-sales platform.
// Sources
Salesforce Manufacturing CloudSalesforce Trust & Compliance
Back to industriesSee manufacturing Salesforce →
// Custom AI architecture

Manufacturing AI is a four-layer pre-sales augmentation stack — engineers sign every quote.

// LAYER L4
User layer
Sales, sales ops, RCA / quoting engineers, product PMs, service, demand planners, installed-base ops — interacting via web, RCA UI, agent desk, partner portal. Any AI output affecting quotes or contract commitments is engineer-signed.
Sales / Sales OpsRCA EngineerService / Planning
// LAYER L3
Application layer
Built with **Vibe Coding** — spec extraction, RCA rule assistance, technical-support case summaries, warranty-ruling drafts, installed-base lifecycle triggers. **Agentic workflow** fits cross-system status checks, dealer replenishment suggestions, Sales Agreement deviation investigation; anything affecting money or contract commitment requires human checkpoint.
Vibe CodingAgentic WorkflowEngineer-signed
// LAYER L2
AI layer
LLM Gateway + catalog / specs / contracts RAG + eval pipeline + guardrails. Common model mix: Llama / Mistral / Gemma open-source models alongside commercial LLMs. Customer pricing history and negotiation floors never reach commercial LLMs.
LLM GatewayLlama / Mistral / GemmaEval · Guardrails
// LAYER L1
Data layer
Vector DB (catalog, specifications, technical docs, warranty policies embedded) + structured data (CRM accounts, orders, contracts, warranty records) + full audit log. Negotiation floors, margin data, and process IP stay in private deployment.
Vector DBCRM RecordsAudit log
// Pre-launch · 6 operational disciplines

Six things to resolve before any manufacturing AI launch.

01
Commercial-sensitive data classification

Customer negotiation floors, product costs, margins, process IP — which may go to commercial LLMs vs only private deployment? Without clarity, do not start; this red line costs more than PII.

02
AI does not quote autonomously

AI can structure RFPs, recommend products, prefill RCA, and check rules — but the "final price" is always Revenue Cloud Advanced-computed and sales-signed. AI suggested prices must be tagged "recommendation" and routed through human approval.

03
Spec extraction confidence labelling

Every field AI extracts from RFPs / specs carries a confidence score. Low-confidence fields force human review; high-confidence fields are sampled. Wrong spec extraction directly hits delivery and warranty.

04
Industry standards & safety data

When ISO / IEC / industry-specific standards or MSDS (chemical safety) content is involved, AI does not generate — only cites. Every citation includes source + version. Wrong safety info carries legal liability.

05
Dealer data boundaries

If the partner portal has an AI assistant, design strictly so each dealer accesses only their own customers’ data. AI prompts must carry and verify partner_id — leaking another dealer’s data is contract breach.

06
Eval & manufacturing context dataset

Before launch, run 100+ real scenarios (complex RFPs, edge specs, return disputes, warranty calls, standards lookups). Re-run on every model version. No pass, no ship.

// FAQ

Five questions that come up most in manufacturing AI discussions.

01Which manufacturing AI use case has the best ROI?
Three consistently high-ROI use cases: (1) **RFP / spec structured extraction** — customers send PDF specs, Excel sheets, engineering drawings; AI extracts machine-readable fields so RCA engineers move from "read + type" to "review," compressing pre-sales engineering effort; (2) **technical-support case summary + suggested resolution** — product manuals, prior cases, warranty rules, and Asset Service Management installed-base history feed RAG so service agents get a draft reply in the first second; (3) **catalog multilingualisation** — auto-produce multilingual versions of an EN catalog (human QA before publish). All three are augmentation, not decision replacement. Money-affecting use cases (quoting, warranty rulings) are best deferred until guardrails are mature.
02Why not use off-the-shelf manufacturing AI products?
If a vertical manufacturing SaaS fits, buy it — engineer’s judgement. Custom is right when: (1) your catalog is unusual (highly configured, jobshop, semi-custom) and SaaS schema does not match; (2) spec / contract formats are unique to your company or customers (outside the SaaS pre-training); (3) commercial-sensitive data (negotiation floors, process IP) cannot live on a commercial multi-tenant platform. If the client already runs Salesforce, our stronger recommendation is **Salesforce + Agentforce + Manufacturing Cloud** — service / sales / dealer AI in one platform.
03How do commercial-sensitive data and AI coexist?
Principle: negotiation floors, product costs, margins, and process IP never go to commercial LLMs. Practical move: redact before prompting (strip cost fields, replace floors with “FLOOR” tags) so the LLM sees only anonymised decision context. When a use case truly needs them (e.g., generating negotiation-strategy advice for sales leaders), route through private deployment or a compliance LLM gateway. Process IP and technical know-how must never be indexed — this costs more than PII.
04Where does agentic workflow fit in manufacturing?
Good fits: (1) cross-system status checks (ERP order status, MES production progress, PLM drawing version); (2) dealer replenishment suggestions (assemble suggested orders from inventory + sales history + seasonality + competitive signal, dealer confirms before submit); (3) forecast anomaly investigation (which accounts deviate most from historical pattern, with reasons). Bad fits: (1) autonomous quoting or contract change; (2) irreversible operations (cancelling production orders, changing delivery commitments); (3) final-confirmation steps in direct customer communication. Principle: agents handle “gather / look up / propose,” humans decide “commit / change / communicate externally.”
05You have no published manufacturing AI case — is that a risk?
Honest answer: we have no publicly referenceable manufacturing AI case. But the methodology (LLM Gateway design, RAG pipeline, eval framework, guardrails, Vibe Coding custom apps, agentic workflow design + limits) is industry-neutral; we have applied this discipline in financial-services, aviation, retail, government, and education AI work — commercial-sensitive data tiering, mixed PII / trade-secret handling, extraction-confidence labelling, and human-review queue design are not manufacturing-only problems. Manufacturing-specific domain (industry standards, process know-how, customer negotiation conventions) we will learn alongside your team from week one.

Manufacturing AI is most stable with “spec extraction + engineer sign-out.”

In 30 minutes we can map your commercial-sensitive data tiering, RCA complexity, and acceptable risk — then decide which AI use cases truly belong in production.

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