Industrial AI products and solutions
Start with Company Expert AI for private company knowledge, NeoVision for defined plant-video scenarios, NeoAssetHorizon for component-risk research, or a custom AI workflow for your operation or engineering project. We help you identify what fits, what needs testing and the work required to put it into use.
NeoVision is a working product configured and deployed for each site. NeoVision uses compatible camera feeds for configured safety and access events, people and goods counts, and vehicle checks. Video processing happens locally. Begin with the event your team needs to see, the available camera view and the person responsible for responding. Detailed product inspection is assessed separately. Food-packaging and date-code checks follow a separate quality-inspection assessment.
Choose the starting point by the repeated work and the output your team needs. In an illustrative manufacturing example, a loading-area count and a date-code check may share a department but need different imaging, acceptance tests and responses. They should not be treated as one ready-made capability.
Preserve approved company documents and expert-reviewed reasoning in a private, offline assistant. Help teams plan, design, develop, test and service with source-linked guidance, while qualified people retain important decisions.
Company Expert AI is a tailored implementation built around each company’s expertise and requirements. Start with one workflow, knowledge capture and expert validation. Agree source permissions, local infrastructure, acceptance and support before expanding.
NeoAssetHorizon is a working product configured and deployed for each customer. Bring hardware equipment lists, spreadsheets, plant records or a component bill of materials into a lifecycle view organized by plant and system. Review end-of-life, end-of-support and obsolescence signals, ask questions in plain English, and prepare replacement, upgrade or budget discussions. Engineers can examine supporting evidence and research possible alternatives. Source availability and correct part identification affect the result, and a suggested alternative still needs engineering and procurement review.
For work that depends on reading, copying or searching, we build workflows around your approved information. EPC tender review can start with a source-linked requirements register. A knowledge assistant can help engineers find procedures and check the source. Pharma batch-record review needs a defined set of checks and evidence for qualified reviewers. We agree the required output, access and human decisions with the people doing the work.
Custom analytical workflows can support production forecasting, equipment investigations, treatment-process review or manufacturing what-if studies. Each starts with an operational decision, usable data and specialists who can judge the result. These are scoped engineering engagements; their sector-specific data, models and integrations are assessed before delivery is agreed.
Your data, cameras, document ownership, interfaces and approval rules determine the implementation. We agree what is included, what needs additional engineering and how the result will be accepted. Deployment is assessed against each product and dependency. You do not need an AI department, but you do need people who understand the process and can own the resulting workflow. We agree who handles support, updates and day-to-day operation before work starts.
Ask what the first delivery contains: configured scenarios or research scope, required inputs, acceptance evidence, training and support. Confirm additional requirements such as cameras, compute, data preparation and interfaces. Operational owners should know what happens when a feed stops, a source is stale or an answer cannot be supported.
IT or automation owners review access and infrastructure; specialists judge output quality; budget owners assess complete cost. Use a focused acceptance decision before expanding across sites. Include checking and correction effort as well as work avoided, and distinguish a proposed business case from savings demonstrated after use.
More clarity
A useful installation starts with a fit check. Camera conditions, component information, system access and the intended use affect configuration and acceptance. We agree the site-specific work before implementation.
Begin with the task. We compare it with the named product capabilities and identify any gap. If the requirement calls for a different workflow, we scope the engineering needed rather than assume it is already included.
A partner engagement can be scoped around its customer relationship and domain work, with NeoBram providing the agreed AI engineering.
Site-specific acceptance is still needed. Its extent depends on the camera conditions, equipment records, source coverage and intended use. Agree the necessary evidence before wider deployment.
The included scenarios or data, customer responsibilities, infrastructure, integrations, acceptance checks, delivery outputs and support. Expansion and recurring costs should be explicit.
Start with one business problem
