What Makes On-Device AI Deployment Worth Doing Right
There are a few ways to approach on-device AI setup — and the approach matters more than most teams realise until something goes wrong mid-deployment.
← Back to HomeSix Reasons Teams Work With Skerry Labs
Deep Hardware Familiarity
We work with the actual device constraints — memory, compute, storage, thermal — not just with cloud-adjacent "edge" setups. Real embedded work.
Deliverables You Can Use
Every engagement produces documentation your team can act on: update scripts, review procedures, configuration notes in version-controlled form.
Honest Feasibility First
We start with a review of whether your use case is a good fit — before you commit to a larger setup. Not every workload belongs on the device.
Maintainable After Handover
Update routines and review cycles are designed into the deployment — so your team isn't stuck calling us every time a model needs refreshing.
Staged Service Options
Three service tiers let you engage at the right level — whether you're exploring, setting up one device, or rolling out across a fleet. No forced upsells.
Direct Engineer Access
You work with the engineers running the engagement — not a project manager relaying messages. Questions get answered by people who know the work.
What Each Benefit Means in Practice
Specialist Knowledge in a Narrow Field
On-device AI sits at the overlap of embedded systems engineering and machine learning infrastructure. Most ML practitioners have limited hardware experience; most hardware engineers have limited ML exposure. Skerry Labs was built at exactly that intersection.
- Experience across microcontroller-class and embedded SBC environments
- Hands-on model quantisation and inference pipeline tuning
- Understanding of real-world update and maintenance requirements
"The team understood our device constraints without needing a week of context. They'd clearly done this on real hardware before."
— Engineering Lead, IoT Product Company, Klang Valley
How a typical engagement flows:
- 01
Initial call to understand your device, use case, and constraints
- 02
Feasibility review with written summary of recommended approach
- 03
Setup and configuration work on device with your team present
- 04
Handover session covering documentation, update routine, and review schedule
A Clear Process at Every Stage
Each service tier has a defined scope and output. You know what you'll receive before work starts, and the engagement ends with a handover — not an open-ended dependency.
- Defined deliverables agreed at the outset
- Regular progress check-ins during setup work
- Structured handover to close every engagement
Transparent, Fixed Pricing
Our three service tiers are priced clearly in Malaysian Ringgit. There are no variable cloud compute costs added to the bill, no surprise additions, and no ongoing subscription after the engagement ends.
- Edge Feasibility Review from RM 640
- On-Device Setup from RM 1,820
- Edge Deployment Programme from RM 3,020
What the price includes:
- All scoping and review work
- Configuration and setup on your device
- Written documentation and handover materials
- Training session for your team (setup services)
- No recurring fees after engagement closes
Skerry Labs vs Typical Approaches
Most teams attempting on-device AI deployment face a common set of gaps. Here's how our approach addresses them.
| Consideration | General AI Consultancies | Skerry Labs |
|---|---|---|
| Hardware-aware configuration | ||
| Written feasibility review before commitment | ||
| Documented update routine included | ||
| Team training session as standard | Sometimes | |
| Fixed price with no recurring fees | Rarely | |
| Staged service tiers by deployment phase |
Distinctive Features of the Skerry Labs Approach
No Dependency by Design
Our stated goal is to leave your team able to manage the deployment without us. Every handover is structured around this — documentation, training, and defined procedures written for the engineers who will maintain the system.
Scope Matched to Your Stage
Most consultancies offer one engagement type. We offer three, each sized for a specific stage: early exploration, first device, or fleet rollout. You start where you are, not where the consultant wants you to be.
Data Stays with You
We don't request access to production data and we don't retain client device configurations after engagements close. On-device processing is partly about keeping data local — that principle applies to how we work too.
Malaysia-Based Team
Based in Shah Alam, Selangor. We understand the context local engineering teams operate in — including connectivity realities, regulatory considerations, and how product teams here work.
Where We've Got To
4+
Years focused on edge deployments
37
Engagements completed across Malaysia
94%
Deployments still active 12 months post-handover
12+
Device types and architectures worked with
MDec Technology Partnership
Registered technology service provider, 2023
MIGHT Edge Computing Panel
Contributing member since 2022
MOSTI Approved Vendor
SME technology services category, 2024
Ready to Take a Closer Look?
Start with the Edge Feasibility Review and get a clear picture of what on-device AI can actually do for your product — before committing to a larger project.
Get in Touch