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OB360

Custom Computer Vision & Applied ML

Develop domain-specific models when general-purpose APIs cannot solve a specialized vision problem.

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Definition

Create specialized visual intelligence for specialized problems.

In brief: Custom computer vision is the engineering of image models for a specific domain problem that general-purpose image services cannot solve. OB360 covers data preparation, annotation, segmentation of the relevant region, model development, evaluation and explainability.

When a general image API is not suitable, OB360 can engineer domain-specific computer vision pipelines around the visual information that matters. The work spans data preparation, annotation, region selection or segmentation, model development, evaluation and explanation. Careful dataset handling and validation are essential, particularly when results could influence health or safety decisions.

Problems

Designed around your business

  • Architecture before tools

    We define the business workflow, constraints and right level of AI before choosing implementation patterns.

  • Engineering accountability

    Our work spans concept, integration, software delivery and handover.

  • Controlled enterprise AI

    We design with boundaries, permissions, human review and evaluation in mind.

  • Specialist depth

    Agents, graph intelligence, document AI, applied ML and cloud-native engineering can be combined when the problem requires them.

Who it is for

Where this service fits

This service fits problems where the visual information that matters is small, specialized or captured in uncontrolled conditions.
  • Enterprise leaders planning practical AI adoption.
  • Software and product teams extending existing applications.
  • Business and engineering teams connecting workflows, knowledge and systems.

Use cases

An illustrative business scenario

  • A possible engagement

    In livestock research, visual risk screening may depend on the color and texture of a small eye region. A custom model must isolate that region and be evaluated for reliability under varying field images.

Related case studies

Approach

From requirements to delivery

Each engagement moves through four stages.

  1. Understand

    Agree the business problem, users, existing systems and constraints.

  2. Design

    Choose the architecture, data boundaries and appropriate human oversight.

  3. Engineer

    Implement the agreed scope with testing and integration.

  4. Enable

    Share implementation knowledge and agree operational next steps.

Technology

Typical scope

A typical engagement can cover the following areas.
  • Data preparation
  • Synthetic image research when appropriate
  • Segmentation
  • Feature modeling
  • Evaluation and explainability
  • Integration design

Delivery

Delivery model

Engagement scope, team structure and delivery priorities are agreed around your business needs.

Specific deliverables and implementation boundaries depend on the agreed engagement.

What to expect from an engagement

Dataset and annotation approach, preprocessing, custom segmentation and modeling, evaluation report, explainability and integration options.

Questions

Questions about this service

Can a custom vision model replace expert diagnosis?

Not by default. High-stakes use cases need independent domain validation and appropriate professional oversight before operational decisions are made.

Can this work connect to our existing software?

OB360 can integrate AI capabilities with existing applications, data and APIs, with access boundaries defined for the engagement.

How do we start?

Discuss your workflow and constraints with OB360 to agree the relevant discovery and engineering scope.

When is a custom computer vision model needed?

When the relevant visual information is small or domain-specific, images vary widely in lighting and angle, labeled data is limited, and a general-purpose image API cannot isolate what matters.

What techniques does OB360 use for custom vision models?

In its livestock screening research, OB360 combined synthetic data generation with DDPM, SAM-assisted annotation, Attention U-Net segmentation, hybrid deep-learning classification and explainability with Integrated Gradients, LIME and Grad-CAM.

Next steps

Related services

Explore complementary consulting and engineering services for your initiative.

Discuss a Custom AI Model

Tell us about your business workflow, users and existing systems. We will discuss the architecture, engineering support and next steps that fit your needs.

Discuss your AI initiativebusiness@onboard360.com