Build AI agents for manufacturing teams that optimize production workflows, reduce downtime, automate operational decisions, improve throughput, and connect factory systems for smarter performance.
Fixed logic works only when conditions stay predictable, but manufacturing rarely does. That is why traditional automation hits its limits before your operations reach their full potential.
Pre-written scripts handle only the conditions they were built for. Any line disruption outside that parameter range stops the automation entirely and waits for a human.
RPA reads structured fields reliably. It cannot interpret raw sensor feeds, variable OPC data, or equipment states that fall outside its predefined format expectations.
Legacy automation runs in silos. It cannot correlate a quality alert in QMS with a capacity constraint in MES and generate a corrective response across both systems.
When every exception routes to a supervisor for review, output slows proportionally to operational complexity. You are scaling headcount, not production capacity.
Monitors asset health using vibration, temperature, and run-time data. Flags anomalies and creates work orders in your CMMS before a fault window opens on the line.
Processes visual and sensor data to detect defects during production. Classifies defect type and severity, then triggers alerts or automated line holds based on thresholds.
Balances capacity, demand, and machine availability to generate feasible production schedules. Replans automatically when throughput drops or customer orders shift mid-run.
Tracks lead times, inventory positions, and supplier performance. Initiates purchase orders and escalations when procurement thresholds are crossed without waiting for a buyer.
Coordinates task assignments, material movements, and work order routing based on real-time machine and labor availability. Routes work around bottlenecks before they form.
Reads demand signals and current stock levels to trigger replenishment before shortages occur. Adjusts reorder points dynamically based on actual consumption patterns.
Monitors environmental readings and operator activity against regulatory thresholds. Logs deviations automatically and routes incidents for review without manual data entry.
Correlates process parameters with yield data to identify the conditions that produce the best output. Surfaces specific parameter adjustments to operators in real time.
Embeds product-level intelligence into manufactured equipment to monitor field usage, surface diagnostics, and deliver service insights directly to the OEM or end customer.
Handles make-to-order and make-to-stock workflows with agent-managed scheduling, assembly tracking, and inventory coordination across multi-plant and multi-shift setups.
Manages line balancing, torque verification, paint inspection, and supplier coordination for ICE, hybrid, and battery electric vehicle production environments.
Operates under FDA CFR Part 11 and GMP constraints with full audit trails, batch record management, and automated deviation flagging for regulated production lines.
Monitors CCP compliance, allergen controls, and cold chain continuity. Generates HACCP records and triggers corrective actions automatically when readings drift.
Tracks reactor conditions, blend ratios, and material handling sequences. Agents respond to process deviations within defined safety parameters without operator intervention.
Manages yield data at the die level, monitors ESD conditions, and coordinates changeover sequences on SMT and functional test lines with minimal manual input.
Autonomous agents in manufacturing need guardrails. Every system we build includes controls for operational safety, audit accountability, and regulatory compliance.
Request a QuoteAgents are configured to recommend, request approval, or act autonomously based on action type, risk level, and operator-defined preferences. You set the autonomy ceiling.
Operators, supervisors, and engineers each see and control only the functions relevant to their role. Override capability is available at every decision layer.
Every agent output includes the data it processed, the logic it applied, and the outcome it recommended. Nothing runs as a black box. Everything is reviewable on demand.
Agents deploy on-premise or in a private VPC for manufacturers with data sovereignty requirements, network isolation policies, or air-gap constraints on the OT side.
Agent architecture follows ISA-95 hierarchy definitions and ISA-99 security guidelines, keeping decisions within the correct operational level and outside control system boundaries.
We design separate data paths for OT and IT traffic. Agents interacting with PLCs and SCADA use data diodes or DMZ architectures to keep control system traffic isolated.
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We have built working integrations across the major platforms that manufacturers run. No forklift replacements. No parallel systems. Agents connect to what you already have.
Discuss Your Manufacturing AI Use CaseNative ERP connectors cover production orders, inventory positions, procurement data flows, demand signals, and fulfillment updates across your manufacturing operations.
MES integrations support work order management, production tracking, shop floor execution data, batch records, and real-time visibility into manufacturing activity.
Agent-to-system connections support maintenance scheduling, spare parts management, asset health workflows, work order updates, and preventive maintenance execution.
Agents read from and write to quality management platforms and SPC tools to trigger non-conformance records, initiate corrective action workflows, and log inspection outcomes.
Agents communicate with PLC environments and read SCADA historian data to monitor process states, detect threshold breaches, and support near real-time operational response.
Agents connect through industrial protocols and edge gateway APIs to bring sensor and telemetry data directly into decision logic without intermediate aggregation delays.
Our legal AI agent development services begin with workflow discovery, not architecture. The first phase defines the problem before we build anything.
We map your existing plant workflows, identify decision points that consume operator time, and document where agent intervention creates the most measurable operational value.
We assess your OT and IT data sources, confirm integration feasibility, and scope the API connections and data contracts required to feed each agent reliably.
We define the agent structure, select the LLM that fits the use case and data volume, and design the orchestration logic before writing a single line of code.
Agents are tested against historical plant data and digital twin environments to validate decision logic under real operating conditions before touching a live system.
We configure for edge or cloud based on your latency requirements, network constraints, and data residency preferences. Deployment architecture is finalized before go-live.
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We run a controlled pilot with defined escalation paths, live monitoring, and operator override active. Nothing expands to full scale until the pilot results support it.
Post-rollout, we monitor agent performance against baseline metrics and track results across live production environments.
We retrain models on production data and expand deployment scope as results validate further investment.
Outcomes:
We do not configure SaaS platforms or wrap pre-built tools in a layer of branding. Every agent is written specifically for your data structures and operational workflows.
Our team has production integration experience with SAP, Siemens Opcenter, IBM Maximo, and Rockwell systems, not just working knowledge from vendor documentation.
We deploy where your operations require, from on-premise edge nodes in air-gapped facilities to multi-cloud architectures with private VPC and hybrid configurations.
Security controls are designed from the OT layer up, following ISA-99 principles. They are not added as a retrofit once the architecture is already built for the cloud.
We build systems that run in live plant environments under real production conditions. We do not deliver proofs of concept that perform in test but fail on the floor.
We stay involved from initial discovery through post-deployment support and retraining cycles. The engagement does not end when the first demo runs successfully.
Deploy manufacturing AI agents that connect with your plant systems, automate decisions, improve uptime, and operate within your safety, compliance, and production constraints.
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Fill the form below or Contact us at +1 408 365-4638 / email us via contact@folio3.ai
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+1 408 365-4638
contact@folio3.ai
6701 Koll Center Parkway, #250 Pleasanton, CA 94566
