AI Transformation

Edge AI

Deploy AI inference on devices or near the systems producing the data. Design around connectivity, hardware limits, response time, and the realities of maintaining a distributed fleet.

AI Transformation / Specialist service

Bring intelligence closer to the point of work.

Deploy AI inference on devices or near the systems producing the data. Design around connectivity, hardware limits, response time, and the realities of maintaining a distributed fleet.

Discuss your project

01 / Match models to devices

Match models to devices

Profile compute, memory, power, and thermal constraints. Select or optimize models against the quality the use case needs.

02 / Work through connectivity gaps

Work through connectivity gaps

Define what runs locally, what synchronizes later, and how the system behaves when a network or upstream service is unavailable.

03 / Manage the fleet lifecycle

Manage the fleet lifecycle

Plan model versioning, staged updates, diagnostics, and rollback so field deployments remain supportable.

04 / Your project

Talk through your use case

Discuss edge ai in the context of your current systems, your team, and the outcome you need. Define a focused starting point before implementation.

Talk to DEQX

Clarity before commitment.

Shape the engagement around the work you need. Specific deliverables, access, responsibilities, and acceptance criteria are agreed during discovery.

What we start with

  • Workflow examples and process owners
  • Approved data and system access
  • Quality, security, and review requirements

What the scope can cover

  • Match models to devices
  • Work through connectivity gaps
  • Manage the fleet lifecycle

What we agree together

  • A defined scope and prioritized delivery plan
  • Review points and acceptance criteria
  • Documentation and ongoing ownership

A practical path from idea to operation.

A staged approach gives your team clear review points. Timing and responsibilities depend on the scope and the readiness of the systems involved.

  1. 01

    Define the task

    Agree the workflow, users, data sources, and the evidence needed to judge whether the approach is useful.

  2. 02

    Test the approach

    Evaluate representative examples, failure cases, and human review needs before expanding the scope.

  3. 03

    Build and integrate

    Connect the agreed capability to the application and its permitted tools, with access and exception handling defined.

  4. 04

    Prepare to operate

    Document evaluation, monitoring, ownership, and the improvement cycle needed after release.

Before we begin

Useful answers. Clear expectations.

A few questions to help frame your edge ai project.

Can an edge application work without internet access?

Some inference workflows can run offline. The design must still account for provisioning, synchronization, updates, and any external data or services the task depends on.

How is the scope and delivery plan defined?

We begin with your current situation and the outcome you need. For this service, the discussion covers match models to devices, work through connectivity gaps, manage the fleet lifecycle. Deliverables, dependencies, review points, and commercial terms are agreed before implementation.

What happens after the initial delivery?

Handover, maintenance, and ongoing improvement are discussed as part of the engagement. The arrangement depends on your internal team, the systems involved, and the operating responsibilities you want DEQX to support.

Let’s talk about your project

Bring us the challenge. Let’s shape the next step.

Tell us what you want to improve, what you have today, and the constraints that matter. We’ll help define a practical starting point.

Start a conversation

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