Predictive maintenance AI,
shipped on your plant stack — not on a slide deck.

An `ai manufacturing company` like ours ships production AI workflows on your plant stack — not slide decks, not pilots that die at month 4. The day-to-day work: scope which workflow moves a P&L line (most often predictive maintenance, AI visual inspection, supply-chain reorder, production scheduling, or shop-floor copilot), pass the ISA-99 / IEC 62443 read-only review, build the integration against your SAP / Oracle ERP + Ignition / Wonderback / FactoryTalk MES + OPC UA OT stack, pick the right model per workflow (Sonnet 4.6 for technical narrative, GPT-5.4 vision for AOI inspection, Haiku 4.5 for high-volume handoff summaries, GPT-5.4-mini for structured PR/PO drafting), bake in idempotent writeback handlers and a reconcile job, ship behind a planner-in-loop or operator-in-loop gate, then operate the workflow long enough to prove cost-of-ownership before scaling. We do not sell a product; we ship one workflow at a time and report cost-per-decision monthly. If you want a `manufacturing ai company` that delivers a live integration in six to eight weeks, this is it.

`Predictive maintenance ai` ($36.95 CPC — the highest in this cluster) is the workflow most plants fund first. The shipped pattern: vibration + current + temperature telemetry from the OPC UA endpoint and the historian (PI / Aveva Insight) feeds a finetuned remaining-useful-life classifier; the classifier scores assets weekly (or daily for high-criticality units); a Sonnet 4.6 narrative drafts the CMMS work order with parts list, torque spec, and a 2-line rationale citing the specific signal trend; the maintenance planner approves and dispatches. Read-only on the PLC, never autonomous. Realistic outcomes on a clean dataset: 30–45% reduction in unplanned downtime hours on the modeled asset class within 6 months, and 30–45% fewer service-truck rolls on field-fleet equipment. Both numbers depend on your historian data quality — if your tag-naming is inconsistent or your historian has < 90 days of clean signal, the audit will say so before the pilot.

`Ai visual inspection`, `ai quality control`, `computer vision manufacturing` — yes, with one operator-grade qualifier: AI alone runs at a false-positive rate too high for safety-relevant or regulated categories. The Pareto in §5 shows it: AI-assisted alone runs around 3.6% false-positive, which is fine for cosmetic categories (color drift, scratch, label misprint) but not for safety-rated features. AI + human reviewer collapses the false-positive rate to roughly 0.4%. The shipped pattern is GPT-5.4 vision + a finetuned classifier (trained on YOUR last 60–90 days of confirmed-defect images, not generic ImageNet) that re-screens every AOI candidate; confirmed-defect units route to a fast-track lane, ambiguous cases queue for QC engineer review. Inspector throughput per shift roughly 3.5× baseline because the engineer is reviewing a pre-filtered queue instead of every unit. If your scope touches FDA-regulated medical-device manufacturing (21 CFR Part 11, ISO 13485) or IATF 16949 automotive supply, the audit narrows aggressively — validated dataset, signed-off model card, full inference audit log, documented MOC + revalidation plan.

Yes — and the integration is 80% of the engagement. Quick recap of the failure modes per stack. SAP S/4HANA: BAPI / OData via the S/4HANA gateway, with idempotent webhook handlers and a per-tenant staging table for writeback (we never autonomous-commit a PO). Oracle ERP Cloud: REST/JSON APIs, similar staging pattern. Ignition (Inductive Automation): MQTT Sparkplug B is the friendly read path; writeback goes through the WebDev module or Perspective. Wonderware / AVEVA: System Platform read via OPC UA, writeback through the Application Server. Rockwell FactoryTalk: DataLink REST shim for read, FactoryTalk Edge for the gateway. OPC UA OT: ALWAYS read-only session for the agent — we don't open a write session on a PLC, no exceptions. `Ai mes integration`, `ai erp integration manufacturing`, `ai scada integration` are plumbing — picking the right model is the easy 10%, the 90% is the SAP BAPI contract, the OPC UA endpoint, the Sparkplug B namespace, and the idempotent reconcile job.

No. Read-only on PLC and SCADA, full stop. Two reasons. (1) Safety: ISA-99 / IEC 62443 zone-and-conduit principles, OSHA Process Safety Management (29 CFR 1910.119) for covered processes, Class I/II/III hazardous-location classifications, and your plant's own Management of Change process all make autonomous PLC writeback a hard line. (2) Operability: even if it were safe, your controls engineers will pull the integration in week 9 the first time a model output deviates from a setpoint nobody approved. The shipped pattern keeps OPC UA read-only, posts AI-drafted actions to the MES level (Ignition annotations, FactoryTalk DataLink notes, Wonderback events) for the line lead or operator to confirm, and lets the existing HMI + MOC process push the actual setpoint change. AI assists the operator's decision; the operator still pulls the lever.

`Ai supply chain` (vol 2,400, $21.78 CPC) and `ai production scheduling` ($36.29 CPC) are two workflows that share a stack but run on different cadences. Supply chain: agent reads supplier portals + open POs + 12 months of lead-time + on-time-delivery history + demand-forecast inputs, drafts reorder recommendations with risk-of-stockout reasoning and supplier-tier risk callouts, and routes to the planner for approval. `Ai demand forecasting manufacturing` is a Sonnet 4.6 or GPT-5.4 job depending on stack preference. `Ai logistics` extends the same loop with carrier + lane data. Production scheduling: agent reads the MES schedule, open work orders, machine availability, changeover times, and demand priority; when a constraint shifts (rush order, machine down, materials slip), the agent drafts a swap and the planner confirms. Neither autonomous-commits — every writeback lands in a staging table that a planner approves before the ERP transaction fires. Cost band sits in the $400–$1,500/mo range per workflow with prompt caching warm; expect 4–8 week build for the first, 2–3 weeks for each subsequent one on the same stack.

Four places we'll say no — covered in §8 above and worth repeating. (1) Safety-critical autonomous control without human override — autonomous PLC writeback, autonomous setpoint changes on safety-rated functions, autonomous interlock changes. Read-only on OT, full stop. (2) Regulated certification gaps — FDA 21 CFR Part 11 + ISO 13485 medical-device manufacturing, IATF 16949 automotive supply, ASTM E1417 / ASNT SNT-TC-1A NDT inspections all need documented training data, validated change control, signed-off model cards, and a documented MOC + revalidation plan. If your scope touches a regulated category, the audit narrows. (3) Environmental-hazard automation — autonomous control in OSHA PSM-covered processes or Class I/II/III hazardous locations where a control action could discharge a hazardous substance. AI assists; operator commits. (4) Regulated calibration — NIST-traceable calibration, A2LA / ANAB accreditation scope. AI can pre-screen drift trends and draft work orders; AI can't sign the cert. Beyond those four, we'll also say no on any workflow where the historian data isn't clean enough to build an honest baseline, where the regulatory posture is unclear, or where the metric won't move within the pilot window.

No — augment not replace. Every shipped manufacturing AI workflow we run keeps a human-in-loop at the consequential decision. Predictive maintenance: AI drafts the work order; the maintenance planner approves and dispatches. AI visual inspection: AI flags candidates; the QC engineer reviews and confirms. Supply chain: AI drafts the reorder; the planner approves before the PO fires. Production scheduling: AI drafts the swap; the planner confirms before the MES commits. Shop-floor copilot: AI answers diagnostic questions with citations; the line lead decides what to do. The realistic claim a `manufacturing ai company` should make: operators keep the safety-critical decisions, planners keep the commit authority, technicians keep the wrench — and the AI returns 30–50% of the time those teams currently spend on routine routing, summarization, drafting, and pre-inspection. Augmentation patterns ship; replacement patterns don't.

Three tiers, pricing-locked across our service + industry cluster. (1) `Manufacturing ai consulting` audit: $3K fixed, 1–2 weeks. We shadow plant ops + controls + IT/OT + maintenance + quality leads, score candidate workflows by downtime hours × defect-escape × time-to-ship × regulatory risk, and deliver a 90-day roadmap with per-workflow cost bands and an honest "these won't pay back yet" list. (2) Pilot to production: $10–25K fixed, 6–8 weeks. One workflow shipped end-to-end on your real SAP / Oracle / Ignition / Wonderback / FactoryTalk stack, human-in-loop, gated writeback, with steady + surge-mode config tested and a walk-away point at week 6 — if the metric won't move, we stop before production hardening and you don't pay phase 2. (3) Continuous team: from $5K/month, no annual contract. Embedded PM + AI engineer + controls-savvy analyst shipping the next workflow on your roadmap, with per-workflow monthly cost-of-ownership reporting and a surge-readiness review before quarter-end + peak season. Most `industrial ai` engagements we run start with the audit, ship the first workflow on the pilot, and move to monthly for workflows two through five. Cost-per-decision reported monthly on every shipped workflow — no per-decision number, no engagement.