Pakistan

Edge AI Deployment Services

Edge deployment solves a specific set of problems - and adds real complexity

Running AI inference at the edge (on-device, or on local hardware near the data source) instead of calling a cloud API makes sense for specific reasons: latency that a network round-trip can't meet, environments with unreliable connectivity, or data privacy requirements that mean sensitive data shouldn't leave the local network. It also means giving up the easy updates and elastic scaling of a cloud API, and taking on real device/hardware constraints. We only recommend edge deployment when one of those specific justifications actually applies to your project.

What we build

Model optimization for edge hardware constraints - quantization and pruning to fit models within the memory/compute budget of target devices, with honest tradeoffs on accuracy
On-device inference integration into your application, for mobile or embedded deployment
Local edge server deployment for scenarios needing more compute than a single device but still avoiding a round-trip to the cloud
Update/monitoring strategy for models running in the field, since edge deployment makes rolling out model improvements genuinely harder than a cloud API

Our process

1. Confirm edge deployment is actually justified

Based on your specific latency, connectivity or data-privacy requirement - if a cloud API would work fine, we'll say so rather than add unnecessary complexity.

2. Optimize the model for target hardware

Quantization/pruning scoped to your actual device constraints, with clear tradeoffs on accuracy communicated upfront.

3. Integrate and test on real target devices

Not just a development machine - the actual hardware constraints of where this will run in production.

4. Plan for updates

A strategy for rolling out model improvements to deployed devices, since this is genuinely harder than updating a cloud endpoint.

Pricing

Rs 200,000-800,000 depending on model complexity, target hardware constraints, and number of deployment environments.

MLOps Pipeline · Hire AI Developers · All services

Frequently asked questions

Why not just use a cloud AI API?

For most use cases, a cloud API is genuinely simpler and we'd recommend it. Edge deployment is justified specifically when you have a real latency requirement a network round-trip can't meet, unreliable connectivity, or data that shouldn't leave the local network.

Does the model lose accuracy when optimized for edge devices?

Often some, yes - quantization and pruning trade some accuracy for fitting within device memory/compute constraints. We communicate this tradeoff honestly upfront rather than after the fact.

How do we update the model once it's deployed to devices in the field?

This is genuinely harder than updating a cloud endpoint, which is why we plan an update/monitoring strategy as part of the project rather than treating deployment as a one-time event.

What devices/hardware can this run on?

Depends on your target - mobile devices, embedded hardware, or local edge servers each have different constraints that shape the optimization approach.

Do we need this for our AI feature?

Only if you have a specific latency, connectivity or privacy requirement - we'll assess honestly and recommend a simpler cloud API approach if edge deployment isn't actually justified.

What does it cost?

Rs 200,000-800,000 depending on model complexity, hardware constraints and deployment environments.

Get Quote WhatsApp Contact Book Meeting