Deployment

From PoC to production, built around your site

Choose where AI workloads run, how manufacturing data is handled and how models reach production. BFAI supports site implementation, deployment and ongoing operations beyond the PoC.

Deployment options

Place your data and AI where they belong.

Compare cloud, on-premises, hybrid and edge configurations. The right design depends on data confidentiality, network conditions, workload placement and existing manufacturing systems.

Cloud

Host training, inference and model-management workloads in an approved cloud environment. A shared AI foundation can support collaboration across manufacturing sites where permitted.

The architecture must define which manufacturing data may leave the site, where it is stored and who may access it.

Deployment architecture Example configuration
Manufacturing site Data collection & result review Equipment sensor data, inspection data and engineering review
Cloud environment Training, inference & model management Cloud workloads using data approved for transfer

Data transfers, access permissions and storage locations are agreed with the customer.

On-premises

Keep selected AI workloads and manufacturing data within your internal infrastructure. Training, inference, model management and local LLM services can be configured for on-premises operation.

The design is aligned with available computing resources, internal networks, access controls and operating procedures.

Deployment architecture Example configuration
Manufacturing site Data collection & result review Inspection and process data within the site environment
Internal AI infrastructure Training, inference & model management On-premises analytics and local LLM services

The configured manufacturing data and AI workloads remain within the designated internal environment.

Hybrid

Combine cloud-based training and model management with on-site inference. Place workloads according to manufacturing requirements while keeping time-sensitive processing close to the production line.

Approved data transfers, model distribution, network connectivity and production approvals are defined for each site.

Deployment architecture Example configuration
Manufacturing site On-site inference & result review Process inspection images near the production line
Cloud environment Training & model management Evaluate model candidates and distribute approved versions

Transfer permitted data and distribute approved models through defined network and release procedures.

Edge PC

Run visual inspection inference on an edge PC close to the production line. Edge inference can be combined with on-premises, cloud or hybrid model-management arrangements.

The implementation defines local processing, data storage, network dependencies and how approved model updates reach the device.

Deployment architecture Example configuration
Manufacturing site Inference on an edge PC Process inspection images close to the production line
Model management environment Training & model management Cloud or internal infrastructure, depending on the deployment

On-site inference is supported by a separately defined model-management and update process.

These diagrams illustrate possible workload arrangements, not fixed product packages. Edge PC describes where inference runs and can form part of an on-premises or hybrid configuration.

Your data stays under your control

Data location, transfer permissions, storage and access are defined with your team. On-premises configurations can keep manufacturing data within your internal environment, while cloud and hybrid configurations use specifically approved data flows. Local LLM services can also be deployed on-premises for sensitive manufacturing knowledge.

From PoC to production

Implementation process

BFAI engineers work with your team to define requirements, evaluate the application, integrate with your environment and establish ongoing operations.

01

Requirements

Define target challenges, data sources, network constraints and success criteria.

02

PoC

Evaluate the selected application against agreed technical and operational criteria.

03

Site implementation

Adapt integration, deployment architecture and approval workflows to the site.

04

Production rollout

Validate release requirements and obtain necessary approvals before production use.

05

Operations & maintenance

Monitor performance, evaluate changes and manage updates with MLOps.

Adapted to your operation

Equipment interfaces, existing manufacturing systems, network boundaries, model distribution and approval responsibilities are tailored to site requirements. The final architecture and operating procedures are defined during implementation planning.

Start with your manufacturing challenge.

Talk to us about your process, data and deployment environment.

Talk to our team
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