Manufacturing
From the plant floor to the boardroom — AI governed, measured, and kept on-site.
Manufacturing organizations are deploying AI agents in operational environments alongside corporate AI programs across engineering, procurement, and supply chain. Olakai gives you complete visibility into every AI tool, every agent decision, and every dollar spent — deployed as SaaS for corporate teams or on-premises where your OT environment requires it.
Operational AI is making decisions on the plant floor. Who’s governing it?
Predictive maintenance agents are flagging equipment issues. Quality control AI is rejecting parts on the line. Supply chain agents are adjusting orders based on sensor data. Each of these is an autonomous AI decision with real operational and financial consequences — and most manufacturers we talk to have no audit trail for any of them.
Meanwhile, your knowledge workers in engineering, procurement, and supply chain are using AI tools that weren’t part of any deployment plan. Your IT team is trying to map what’s running where. And your operations leadership is asking what AI is actually returning per dollar spent — not at the tooling level, but across the entire program. That’s three different problems that require the same underlying visibility.
By the Numbers
Manufacturing organizations deploying AI agents in operational environments report that fewer than 30% of agent decisions are logged with sufficient detail to investigate an anomaly or production failure.
When an AI agent makes a decision that affects the line, you need the audit trail. Most manufacturers don’t have it.
Deploy where your environment requires — SaaS, private cloud, or fully on-premises.
Many manufacturing organizations run Olakai as SaaS for their corporate AI programs — knowledge workers, engineering teams, and supply chain functions that operate on standard IT infrastructure. For environments where operational data can’t leave the facility — plant-floor AI agents, OT-connected systems, or production workflows under IEC 62443 or NIST CSF — Olakai also deploys fully on-premises or inside your own private cloud. One platform, deployed where each part of your operation actually runs.
Two lenses, one platform
Olakai is one platform, one data model, one set of controls. Olakai Agentic and Olakai Assistive are the two lenses you use to look at it — they share data, share context, and share Kai. What you see in one lens is immediately visible in the other.
Olakai Agentic
Govern every operational agent running in your manufacturing environment — predictive maintenance, quality control, supply chain optimization — with full execution audit trails and policy enforcement before a bad action affects the line. Also measures AI coding tool ROI for the engineering and IT teams building your OT applications and MES integrations: token spend by provider and team, tied to actual velocity gains.
Olakai Assistive
Track every AI tool your engineering, procurement, supply chain, and operations teams are using — approved tools and shadow AI alike. License utilization across every seat. Time saved per knowledge worker per week. Shadow AI detection before unsanctioned tools touch proprietary process data.
Olakai Agentic — Operational agent governance
Every decision an AI agent makes on the plant floor. Logged and governed.
Olakai Agentic gives your operations and engineering teams full visibility into every autonomous agent running in your manufacturing environment — predictive maintenance, quality control, inventory, supply chain. Every execution logged: inputs received, data accessed, decisions made, outcomes produced. When an anomaly occurs, the audit trail is there. When a regulator or customer auditor asks what your AI systems decided and why, the documentation exists.
- Full execution log per operational agent: inputs, data accessed, decisions, outcomes
- Policy enforcement at the workflow level — violations blocked before production impact
- Audit-ready logs for customer audits, quality certifications, and regulatory review — generated automatically on-site
Olakai Agentic — Engineering AI spend
Control what your engineering teams spend on AI. Before the invoice lands.
Your digital manufacturing and OT engineering teams are using AI coding tools to build faster. Token spend is growing — and without run-rate forecasting, you won’t know there’s a problem until the month-end invoice arrives. Olakai Agentic tracks every provider, every team, every developer, and projects month-end spend from a 7-day trailing average. All across every vendor and team.
- Token spend by provider, team, and developer — normalized across Cursor, Copilot, Anthropic, OpenAI
- Run-rate month-end forecast with trajectory alerts at 50%, 80%, and 100% of budget
- Idle license identification — reclaim seats before the next renewal cycle
The KPIs that matter in manufacturing
Every pillar gives you a different lens on AI performance. Here are the metrics Olakai actually measures — across every vendor, every team, across every vendor and team.
Olakai Agentic
Olakai Assistive
Why Measurement Changes Everything
An AI agent that adjusts a supply chain order or flags a quality defect is making a consequential decision. When something goes wrong — and eventually something will — you need the audit trail. Olakai logs every decision before you need it, not after.
Your engineering teams are spending on AI coding tools faster than procurement can track. Olakai gives you run-rate forecasting and spend visibility by team and provider — so overruns show up as trajectory problems weeks before the invoice.
Your engineers, procurement teams, and supply chain managers are using AI tools you didn’t issue — on proprietary process data, on supplier relationships, on production plans. Olakai makes every tool visible, classifies the risk, and gives you the data to govern rather than block.
Ask your AI program a question. Get a reasoned answer.
Kai synthesizes every Olakai data source — agent logs, tool usage, spend data, governance status — and answers in plain English. With the reasoning shown and the evidence attached.
- “Which operational agents have had policy violations or anomalous decisions in the last 7 days?”
- “What’s our total AI spend across engineering this month, and are we on track to overrun our budget?”
- “Which AI tools are our knowledge workers using on proprietary operational data that aren’t formally approved?”
Illustrative conversation on the right: pick a seat, ask, and watch Kai reason over the same data every Olakai dashboard uses.