Best Industrial AI and Process Optimization Software in 2026

By the editors at Reliable. Last updated November 1, 2026.

How We Evaluated

This guide covers software that uses machine learning, AI or advanced analytics to improve how a production process runs: yield, throughput, quality, energy and raw material use. It does not cover asset health and predictive maintenance platforms, which are in our APM guide, or MES, historians and OEE dashboards, which have guides of their own. The six entries are a selected shortlist.

The comparison is based on vendor documentation and announcements reviewed in November 2026. Reliable has not run comparative tests, and the "Best for" labels reflect our reading of documented capabilities and application fit, with the numbering organized by use case rather than overall performance. Outcome figures in this guide are the vendors' own; results vary by application and baseline, and Reliable has not reproduced them.

We evaluated whether each platform advises, prescribes or closes the loop in its documented configurations; what it connects to and where it runs; who builds and maintains the models; how it explains a recommendation; the industries each vendor documents; whether pricing is published; and company stability signals such as ownership, funding and recent launches.

We referenced OSHA's process safety management regulation, 29 CFR 1910.119, and the ISA/IEC 62443 series of consensus standards, for what a buyer has to live with once software starts moving operating conditions.

Reliable does not accept payment for rankings. Vendors may sponsor enhanced listings with additional detail, but editorial rankings are independent. Read our editorial policy.

Why Industrial AI and Process Optimization Software Is Different

Process optimization software is defined by what it is allowed to do, and the permission matters more than the product category. In an analytics role, the software reads from the historian and an engineer acts on what it shows. In a prescriptive role, it computes a recommended setpoint or recipe adjustment and shows it to an operator, who applies it. In a closed-loop role, it writes the move itself, through advanced process control or straight to the distributed control system.

The roles overlap in practice. Seeq, an analytics platform, licenses writing calculated signals and conditions back to the PI Data Archive, and its agents can initiate workflows in connected systems. Sight Machine's version for Siemens Industrial Edge describes recipes that can integrate with control systems for automated optimization. Historian write-back, workflow automation and setpoint control are three different permissions, so get the proposed configuration in writing.

The closed-loop layer is where the vocabulary gets slippery. Advanced process control (APC) has run multivariable model predictive controllers in refineries and chemical plants for decades, and AspenTech's DMC3 is the direct descendant of that lineage, now building nonlinear models from historical and simulated data with deep learning.

The newer AI systems, Imubit's among them, run in an application connected to the existing APC or DCS, learn a nonlinear model from plant history and move targets at a configured interval, as often as every minute. Both get called AI; ask which one you are being sold.

The second thing to understand is who owns the models. Every vendor on this list says plant engineers build or validate the models, and several say the trained models belong to the customer. The practical test is narrower: can your engineers inspect a model, retrain it, or keep running it if the contract ends? Imubit, Fero Labs and AspenTech all describe engineer-built models; the exit terms live in the contract, so read them.

The third shift is 2026's. Agentic AI, meaning software agents that run multistep investigations and propose actions on their own, spread across the category: Seeq Intelligence (March 3), AspenTech AVA (May 11), Sight Machine's agentic platform (June 11) and Honeywell's Forge Plant Cognition (October 26).

What the agents may do on their own varies by product and configuration. Seeq's agents can initiate workflows in connected systems, Sight Machine's start in recommendation mode, and Honeywell's Forge Cognition runs in manual, semi-autonomous or autonomous modes with operators able to override. Specify in the contract which actions need a person's approval and which may execute automatically.

The fourth is consolidation. Emerson has owned all of AspenTech since March 2025. Schneider Electric agreed on June 30, 2026 to buy Cognite for $3.1 billion and merge it into AVEVA, which agreed in May to buy TwinThread.

GE Vernova sold its Proficy software to TPG in March 2026, and the business now operates as Velotic. Honeywell's automation business became Honeywell Technologies after the company's 2026 separation. Ask every vendor who owns them and what the roadmap says for the product you are buying.

6 Best Industrial AI and Process Optimization Platforms in 2026, Ranked by Use Case

1. Seeq — Best for Self-Service Analytics by Process Engineers

Seeq built its business on a simple premise: the historian already holds the data, and the process engineer should be able to analyze it without a data scientist or an export to a spreadsheet. Engineers trend, cleanse, search for events and build monitors in a browser.

Seeq connects to time-series sources including the AVEVA PI System and CONNECT, to cloud platforms on AWS and Microsoft Azure, and to Databricks, and it says it has been integrated with AVEVA for more than a decade. It can also write back: Seeq's documentation describes a separately licensed capability to export calculated signals and conditions to the PI Data Archive, so they appear in PI Vision displays.

The product now ships in three packages. Seeq Analytics is the self-service analytics layer, and Seeq Enterprise adds collaborative workflows so a fix found at one plant becomes a monitored best practice across the fleet. Seeq Intelligence, launched March 3, 2026, includes everything in Enterprise plus the new agent capabilities, and Seeq says it is available now.

The agents are the 2026 story. Agent Q is a natural language analyst that pulls historical events, prior analyses, documents and know-how into an investigation with traceable recommendations. Build Your Own Agent runs multistep workflows on demand, on a schedule or on a trigger.

Agent Extensibility connects Seeq agents to customer systems such as work orders, and Seeq says it supports initiating workflows and automating actions in those systems. Document Access reads procedures and manuals. Seeq frames all of it as amplifying experts, with a person deciding.

Seeq is privately held and describes itself as a virtual company with employees around the globe; its releases are datelined Seattle, and Dr. Lisa Graham is CEO. Its named industries are oil and gas, chemicals and petrochemicals, pharma and life sciences, mining, metals and materials, power and utilities, food and beverage, and semiconductors.

Published customer stories include Roche and International Paper. Seeq announced ISO/IEC 27001:2022 and ISO/IEC 27018:2019 certification in January 2026.

Best for: Plants with a historian in place and process engineers who want to do their own analytics, monitoring and reporting, and that want agents layered onto a tool those engineers already use.

Pricing: Quote-based on the pages reviewed; Seeq Intelligence is sold as a package above Seeq Enterprise.

Control loop: Analytics and recommendations in the documented workflow. Seeq licenses writing calculated signals and conditions to the PI Data Archive, and its agents can initiate workflows in connected systems; its pages describe no setpoint control.

Deployment: SaaS is Seeq's primary and recommended deployment. Existing on-premises Linux servers remain supported with limitations, and Seeq says some newer features are SaaS-only, so confirm which package features are available on-premises.

Connects to: AVEVA PI System and CONNECT, AWS, Microsoft Azure, Databricks

Industries named: Oil and gas, chemicals, pharma, mining and metals, power, food and beverage, semiconductors

2. AspenTech (Emerson) — Best for Closed-Loop Control and Optimization in Process Plants

Aspen Technology, Bedford, Massachusetts, has been wholly owned by Emerson since March 2025 and operates as Emerson's Aspen Technology business under president Vincent Servello. Its place on this list rests on the control layer. Aspen DMC3 is a multivariable advanced process controller that AspenTech describes as AI-enabled adaptive process control: it combines linear and nonlinear variables in one model, builds nonlinear models from historical or simulated first-principles data with deep learning, and keeps improving the model while the controller runs.

Around DMC3 sit the pieces a process plant uses to optimize. Aspen Inferential Qualities uses deep learning to estimate lab properties in real time. Aspen GDOT ties planning, scheduling and APC together in closed loop. Aspen Unified handles refinery planning and scheduling, and in July 2026 Emerson added Crude Schedule Optimization and Multi-Blend Optimization as separately licensed products within Aspen Unified Scheduling.

AspenTech Inmation is the data fabric. Aspen Mtell, released in a new version in January 2026, is the asset health side, which our APM guide covers.

AspenTech AVA is the 2026 AI platform, introduced May 11 at OPTIMIZE 26 in Houston. Emerson describes it as agentic, domain-aware AI that embeds first-principles models in operating workflows, with four optimization and decision-support advisors; its event preview named AVA advisors inside Aspen DMC3, Aspen GDOT and Aspen Unified PIMS that answer process control questions in natural language. Emerson also offers AspenTech.ai, a web experience for trying AVA's workflows. Emerson announced AspenTech V15 on May 13, 2025.

AspenTech publishes outcome ranges for DMC3: 2 to 5 percent more throughput, 3 percent more yield, 10 percent less energy and more than half off product quality standard deviation (AspenTech's figures, separate from any one site). Cameron LNG's deployment across three LNG trains is the case study AspenTech features. The trade-off is scope. This is a suite for refineries, chemicals, LNG, polymers and similar continuous processes, and it assumes an APC engineering capability, in-house or from AspenTech's services.

Best for: Refineries, chemical and petrochemical plants, LNG and polymer producers that want control, optimization, planning and scheduling from one supplier, with AI advisors inside the tools their APC engineers already run.

Pricing: Quote-based on the pages reviewed; several 2026 additions are separately licensed products.

Control loop: Closed loop for DMC3 and GDOT: DMC3 writes to the DCS, and GDOT closes the loop between planning, scheduling and APC. AVA advisors, simulation and planning products advise.

Deployment: Depends on the products bought. DMC3 and GDOT run as on-premises control applications; a remote monitoring relay gives read-only access from the business network; AspenTech Inmation provides the data layer.

Connects to: Distributed control systems, Aspen InfoPlus.21 and AspenTech Inmation, Aspen HYSYS and Aspen Plus models

Industries named: Bulk and specialty chemicals, downstream refining, midstream and LNG, polymers, upstream, metals and mining, pharmaceuticals, pulp and paper, power

3. Imubit — Best for Closed-Loop AI in Refining, Chemicals and Cement

Imubit, founded in 2016 and headquartered in Houston, sells what it calls Closed-Loop Physical AI, and the phrase is literal. Process engineers build a nonlinear dynamic model of a unit from the plant's own history using DLPC Builder (Deep Learning Process Control). A controller trained with reinforcement learning then moves the key setpoints inside the envelope the engineers set, writing through the APC where there is one or to the DCS where there is not.

How often it moves them is set per application. Imubit's home page says every minute; its technical FAQ says the read and write interval is configured for each application, from one minute to an hour.

Imubit organizes the platform as Map, Model and Control. Map ranks the gaps between plan and plant and traces each to a recurring operating decision. Model builds the cause-and-effect model, which Imubit says is deterministic: the same inputs give the same move every time, and engineers can inspect every learned relationship. Control executes, operators can take over at any time, and models are retrained and recertified as the plant changes.

The numbers are Imubit's own. It reports more than 100 closed-loop controllers deployed worldwide, more than 300 applications from inferentials to closed loop, and 13 approved US patents. Its logo wall names Chevron, Citgo, Marathon Petroleum, HF Sinclair, Flint Hills Resources, Preem, Oxbow, Ash Grove, Eagle Materials and Invista, among others. Headline results, also Imubit's, are 1 to 3 percent yield improvement, 15 to 30 percent energy reduction where energy is the target, and $0.25 a barrel in refining.

Imubit has added cement and mining and minerals to its refining and chemicals base, naming raw mix, kiln, clinker cooler, grinding, flotation and leaching applications. The pilot is paid and fixed in scope, with the fee agreed before work starts. The plant uploads a data package, Imubit says first results come within a few working days of approval, and nothing in the pilot writes to the plant. Closing the loop on a unit comes after the pilot, under a subscription.

Imubit also keeps closed loop out of the language model path. It says its assistants can query the models, and closed loop never runs through an LLM.

Best for: Refineries, petrochemical and polymer plants, and cement and minerals processors that already run APC or a modern DCS and want AI to move targets in closed loop, with engineers owning the models.

Pricing: Quote-based on the pages reviewed. Imubit sells a paid, fixed-scope pilot on the plant's data; pilot models advise without writing to the plant, and closed-loop deployment follows under a subscription.

Control loop: Closed loop, through existing APC or connected to the DCS; an open-loop mode has operators review and apply each move.

Deployment: Hybrid. Models are built and evaluated on the Imubit platform, which runs on AWS and Google Cloud; the closed-loop controller runs in an on-premises application inside the plant network, in the DMZ or on the process control network, connected to the DCS or APC over OPC and OPC UA.

Connects to: Existing APC, DCS, historian data and lab results

Industries named: Refining and gas processing, chemicals and polymers, cement, mining and minerals

4. Fero Labs — Best for Explainable Models in Metals and Chemicals

Fero Labs, with offices in New York, Pittsburgh and Düsseldorf, built its platform for process engineers and metallurgists in heavy industry, and it leads with explainability. Fero describes its models as white-box machine learning: every recommendation comes with the cause-and-effect reasoning behind it, so an engineer can check it before acting. Its reference customers include the steelmakers Gerdau, Celsa Nordic, SIJ Group and Vallourec, along with Covestro, Henkel and the energy company Uniper.

The suite has four parts. Fero Diagnostics explores process data and finds root causes, and Fero Simulator identifies setpoints to fix an issue without wasting material. Fero Production is the live layer, with real-time alerts, forecasting and back-testing of past decisions. Fero Foundation prepares data and maps the process.

Fero's use cases read like a mill engineer's to-do list: scrap mix and alloy optimization, raw material cost minimization, slag and steam optimization, caster breakout diagnostics and quality variation. Its newest line is a Saw Optimizer for long-product mills that recalculates the best cut when the sequence changes.

In its documented workflow the platform is decision support. Fero Production's page says engineers and operators stay firmly in control, and nothing on it describes writing to a Level 2 or control system; confirm any integration beyond that for the deployment you are proposing.

Fero publishes a defined evaluation path: a qualification call, demos on your own data and a 14-day free trial before what it calls a Ramp Year License. In December 2025 Fero announced a partnership with Carnegie Mellon University's Center for Iron and Steelmaking Research that puts its software into CISR coursework, with students working on donated industrial datasets.

Best for: Steel, metals, cement and chemical plants where quality, raw material and energy decisions sit with process engineers and metallurgists who need to see why a model recommends a change.

Pricing: Quote-based after a 14-day free trial; Fero calls the first contract a Ramp Year License and shows no prices on the pages reviewed.

Control loop: Decision support in the documented Production workflow. Recommendations, alerts and forecasts go to engineers and operators, who apply them.

Deployment: Web application; get the hosting and integration architecture for the proposed deployment in writing.

Connects to: Plant historians, lab and quality data, with Fero Foundation handling data preparation and process mapping

Industries named: Steel, chemicals, oil and gas, cement, consumer packaged goods

5. Braincube — Best for Multi-Plant Process Manufacturers

Braincube, founded in France in 2007 by manufacturing engineers who had worked at Valeo and Safran, positions its platform as real-time process optimization for plants whose conditions never stand still. It says more than 250 manufacturers in 30 countries run it (Braincube's figure), across pulp and paper, building materials, tires and elastomers, food and beverage, and mining and metals. Customer stories include CertainTeed, Norske Skog, Sappi and Ofi (Olam Food Ingredients).

The core idea is the Product Clone, Braincube's digital record of each unit of production that links every process variable to the product it made. From there the platform follows five steps it names Reveal, See potential, Steer, Align and Improve: find which variables matter under current conditions, expose the hidden margin, adjust controllable settings in real time, keep operators and engineers on the same actions, and keep learning.

Steering is the part to probe, because Braincube's own stories describe both roles. Its Norske Skog Golbey account describes integrating findings into closed-loop control and real-time process adjustments; a tire manufacturer story describes rules and digital alerts that guide operators. Whether a given deployment pushes recommendations to a screen or writes to a controller is a configuration question, so ask for the integration diagram of a reference plant in your industry.

Braincube raised €12 million in a 2018 Series A and €84 million in a 2023 Series B, and its IT page names adherence to ISO 27001, SOC 2 and GDPR. Its US user conference, the Go Beyond Summit, was held in Chicago in June 2026. Co-founder Laurent Laporte is president and CEO, and co-founder Sylvain Rubat du Mérac is CTO.

Best for: Manufacturers running several plants in paper, building materials, tires, food or metals that want one platform to standardize data, surface margin and guide operators on every shift.

Pricing: Quote-based on the pages reviewed.

Control loop: Prescriptive, with real-time steering of controllable settings; Braincube documents both operator-applied and closed-loop deployments, so confirm which one is proposed.

Deployment: Cloud platform. Braincube advertises flexible deployment options for IT and OT teams; get the proposed hosting and integration architecture in writing.

Connects to: IT and OT data sources through Product Clones; Braincube documents use alongside AVEVA PI System, AVEVA MES and CONNECT. Confirm the interfaces and licenses your installation needs.

Industries named: Pulp and paper, building materials, tires and elastomers, food and beverage, mining and metals

6. Sight Machine — Best for Discrete and Hybrid Plants Adopting AI Agents

Sight Machine, San Francisco, spent its first decade building a manufacturing data platform that turns raw machine, MES and ERP data into a structured model of lines, machines and products. On June 11, 2026 it relaunched the platform around AI agents. The Manufacturing Context Layer maps every signal to an asset, every asset to a process and every process to the KPIs the operations team runs on, and a proprietary small language model called Blueprint automates tag mapping.

Dynamic Production is where optimization happens. Agents investigate continuously, chasing quality drift, downtime patterns and yield gaps, and bring validated findings to process experts as plain-language recommendations in Microsoft Teams, Microsoft Excel or a floor kiosk. Agents investigate and propose; people validate and approve. Sight Machine calls the path from there progressive autonomy, with manufacturers deciding which agents operate, which tools they use and how far their authority extends.

That is the new agents' starting mode, and it should not be read as a limit on the platform. Sight Machine for Siemens Industrial Edge, announced April 15, 2024, runs on edge, on-premises and hybrid edge-cloud systems and describes Dynamic Recipes that can integrate with control systems for automated optimization.

One readiness note: Sight Machine's documentation for its Microsoft 365 agent says the agent is currently optimized for a demonstration bottling environment and may need tenant IT approval, so confirm production availability for the specific agent being proposed.

Enterprise Agents publish Sight Machine's manufacturing intelligence as an MCP server, so other permissioned agents in the company can query plant-floor data, and the platform integrates with Microsoft Azure, Microsoft Fabric, NVIDIA Omniverse and Databricks. Each deployment is single-tenant with its own database, namespace and keys. Sight Machine says it works with manufacturers in more than 20 industries and 20 countries and claims output gains of 10 percent or more (Sight Machine's figure).

Sight Machine is also the one public pricing reference we found. Its AWS Marketplace listing showed the Sight Machine Agentic Manufacturing Platform at $5,000 a month for a one-month contract when checked on November 1, 2026, with 12-, 24- and 36-month terms also listed. The listing does not say how many plants or lines that covers, and AWS notes that additional infrastructure charges may apply, so treat it as a reference point and get a scoped proposal.

Best for: Discrete, packaging, food and other hybrid manufacturers with machine, MES and ERP data spread across systems, that want agents investigating production and recommending actions inside Teams and Excel.

Pricing: Public reference point of $5,000 per month for a one-month AWS Marketplace contract (checked November 1, 2026); longer terms listed, scope not stated, AWS infrastructure charges may apply. Direct deals are quoted.

Control loop: Recommendation mode by default for the new agents, with people approving and autonomy expanded at the manufacturer's pace; the Siemens Industrial Edge version describes recipes that can integrate with control systems for automated optimization.

Deployment: SaaS, single-tenant per customer, integrated with Microsoft Azure and Fabric; edge, on-premises and hybrid deployment documented through Siemens Industrial Edge. Confirm availability for the modules being licensed.

Connects to: Controls, historians, MES, ERP, documents and SOPs, Microsoft Fabric, Databricks, NVIDIA Omniverse

Industries named: More than 20 industries across discrete and process manufacturing (Sight Machine's statement)

Industrial AI and Process Optimization Software Comparison Table

How to Choose Industrial AI and Process Optimization Software

1. Advise, prescribe or close the loop?

Decide what the software will be allowed to do before you compare features, because the answer sets the project's risk, its governance and its vendor list. These are deployment roles, and one product can play more than one. In their documented workflows, analytics and decision support (Seeq, TrendMiner, Fero Labs) leave the move to a person, though Seeq also licenses historian write-back and its agents can trigger workflows.

Prescriptive steering (Braincube, Sight Machine) puts a recommended move in front of an operator, and both vendors also document integrations that act on control systems. Closed loop (Aspen DMC3, Imubit) writes the move. Plants typically earn their way up that ladder one unit at a time; verify separately whether the proposed configuration can change setpoints, how those changes are authorized, and which control layer executes them.

2. Who builds the models, and who owns them when you leave?

Every vendor here says plant engineers build or validate the models, which is a good sign and also a staffing commitment. Ask how many engineer-hours a first application took at a reference plant, how model drift is detected and who approves a retrain.

Then read the contract for what you keep. Being able to build or inspect a model does not by itself give you the right to export it or to run it after the term ends; the model, the training data you supplied and the right to keep running belong in writing.

3. How will management of change work?

If the unit is covered by OSHA's process safety management standard, 29 CFR 1910.119, the binding provision is paragraph (l). It requires written procedures to manage changes to process chemicals, technology, equipment and procedures, except replacements in kind, and to address the technical basis, the safety and health impact, operating procedure changes, timing and authorization before any change.

The non-mandatory Appendix C adds that operating outside established parameters requires MOC review, that changes in operating conditions to improve yield or quality are process technology changes, and that computer program revisions are equipment changes.

Vendors such as Imubit say they run inside an envelope the engineers set. Staying inside existing limits does not by itself settle whether installing the software, changing the controller or retraining the model is a replacement in kind, so put those changes through the site's MOC procedure and let the process safety team make the call.

4. How does it reach the control network?

A read-only analytics connection and a write path to the DCS are different cybersecurity problems. The ISA/IEC 62443 series of standards, and its system design part 62443-3-2 in particular, frames this as zones and conduits: which zone the optimizer lives in, which conduit carries the write, and who holds the credentials. It is an engineering framework, so use it as one.

Pin down the actual read and write permissions of the proposed configuration, because historian write-back, automated workflows and setpoint changes on the control system each need different access and safeguards. Ask each vendor for its reference architecture and have your OT security team review it before the pilot, and govern any pilot that changes a live process as a live operational change.

5. Can your engineers explain the recommendation?

Fero Labs sells white-box models, Imubit says its models are deterministic and inspectable, and Seeq describes Agent Q's output as traceable. Those are the right claims, and they are vendor claims; test them.

Ask each vendor to show a live recommendation with the reasoning behind it, then ask what happens when an operator rejects it and whether the rejection feeds back into the model. A transparent model is easier to check; it is still your engineers who confirm it is right for your process.

6. Who owns the vendor, and what does that do to the roadmap?

Emerson owns AspenTech; Schneider Electric is buying Cognite and merging it into AVEVA, which is buying TwinThread; TrendMiner and Proemion operate under Vercore, the renamed Proemion Holding group; Velotic took Proficy from GE Vernova. Each deal changes product names, support paths and sometimes where data is hosted.

Ask for the written roadmap for the specific product you are licensing, and get the data residency and exit terms into the contract.

The Honest Middle Ground

The six platforms above are a shortlist, and several names a buyer expects to see sit just outside it.

AVEVA and Cognite. AVEVA, Schneider Electric's industrial software company, sells AVEVA Advanced Analytics on CONNECT, with applications for process optimization, predictive quality, throughput and energy, perfect batch and perfect centerline. Alongside it sit AVEVA Process Optimization for rigorous online modeling and AVEVA Predictive Analytics for asset anomalies.

At AVEVA World in Milan in May 2026, AVEVA agreed to acquire TwinThread, subject to regulatory approval. Its May 20 product announcement extended CONNECT to Snowflake and ServiceNow and said a major CONNECT release with an industrial knowledge graph is planned for the first quarter of 2027.

Then on June 30 Schneider Electric agreed to buy Cognite, the Norwegian maker of Cognite Data Fusion and the Atlas AI platform, for $3.1 billion in cash, to be integrated into AVEVA. Schneider said Cognite's 2025 revenue exceeded $170 million and that the deal, subject to regulatory approval, would close in the coming quarters.

For a plant standardized on the PI System this combination will be the default conversation. The product map is in motion, so ask which product you would be buying in 2027 and under what name.

Honeywell Technologies. Honeywell's automation company launched Forge Cognition with NVIDIA and Dell Technologies on October 14, 2026, an agentic AI and simulation platform that customers can run in manual, semi-autonomous or autonomous modes with operator override. Forge Plant Cognition followed on October 26, aimed at refining, petrochemical and LNG operators, with Dangote's Lekki refinery in Nigeria as launch customer.

Honeywell estimates more than 29 cents a barrel of operational benefit there, over $60 million a year at full throughput, a preliminary figure it states is gross of implementation costs and specific to that site. Honeywell UOP's process expertise and physics-based models are the differentiator. The product was days old at publication, so ask what is orderable now and what is roadmap.

TrendMiner. Based in Hasselt, Belgium, TrendMiner is the most direct alternative to Seeq: self-service time-series analytics for process engineers, with an Analytics Agent added for agentic workflows. TrendMiner says 48 percent of the 50 largest chemical manufacturers use it (TrendMiner's 2024 listing) and names BASF, Bayer, Arkema, Huntsman, Evonik and Pfizer among its customers.

Proemion, a Battery Ventures-backed German industrial telematics company, bought TrendMiner from Software AG in 2024. In February 2026 the holding group took the name Vercore, with TrendMiner and Proemion continuing as separate businesses under it. TrendMiner was held out of the six to keep one self-service example on the list.

C3 AI. The Redwood City company sells packaged enterprise AI applications for asset-intensive industries, including process optimization, usually in large top-down deals. Founder Thomas Siebel resumed the CEO role effective May 8, 2026, after a year in which revenue fell sharply.

C3 AI reported fiscal 2026 revenue of $250.3 million in its June 3 results, and in May it described a restructuring designed to deliver about $135 million in annualized non-GAAP cost savings. The applications are real; the business changes belong in your evaluation, so ask about the roadmap, the services model and the customer references still in production.

The automation vendors and the machine health side. Siemens sells Industrial Copilot and Rockwell Automation sells FactoryTalk Analytics LogixAI, both tied to their own control platforms. Proficy CSense, the process optimization module in the Proficy suite, now belongs to Velotic after GE Vernova's March 2026 sale of the software business to TPG.

Augury, better known for machine health, has sold a process optimization product since buying Seebo in 2022. Any of these is a reasonable first call if you already own the parent platform.

Related Guides

• Best APM Software

• Best Industrial IoT Platforms

• Best Production Monitoring and OEE Software

• Best MES Platforms for Manufacturing

• AI in Manufacturing Adoption Rates: Deploying vs Piloting

Sources

• Seeq: Introducing Seeq Intelligence (press release of March 3, 2026; Seeq AI product page; AVEVA connectivity partner page; support articles on exporting signals to a historian and on SaaS as the primary deployment model; ISO certification release of January 29, 2026)

• Emerson: New Emerson Industrial AI Platform Delivers Enterprise-Scale AI (AspenTech AVA launch release of May 11, 2026; OPTIMIZE 26 news brief of May 6, 2026; V15 release of May 13, 2025; refinery scheduling release of July 16, 2026; Aspen DMC3 product page and Phillips 66 webinar page on aspentech.com)

• Imubit: Closed-Loop Physical AI (home page and customer listing; How it works page on the paid pilot and platform hosting; technical FAQ of June 18, 2024 on architecture and configurable interval)

• Fero Labs: Industrial Process Optimization Software (home page and evaluation process; Fero Production page; Carnegie Mellon CISR partnership release of December 2025)

• Braincube: Real-Time Process Optimization (home page; platform page; Our Story milestones; IT teams page; AVEVA article of December 16, 2025; Norske Skog Golbey closed-loop account)

• Sight Machine: Sight Machine Launches Agentic Manufacturing Platform (press release of June 11, 2026; Siemens Industrial Edge release of April 15, 2024; AWS Marketplace listing checked November 1, 2026; Microsoft 365 agent documentation)

• OSHA: 29 CFR 1910.119, Process safety management of highly hazardous chemicals (paragraph (l), management of change; Appendix C compliance guidelines, Managing Change section)

This guide is updated quarterly. Last review: November 2026. View all Reliable guides.