Edge AI Services
A cloud round trip slows every decision and stalls when connectivity drops. Edge AI development services put trained models directly on the device instead. Kodexo Labs builds on-device AI inference that runs offline, protects sensitive data, and holds up under real production load. 51 AI-powered products across 25+ industries. Founded 2021. 94% client retention.
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TRUSTED BY ENTERPRISES
When a decision has to happen the instant a sensor fires or a user speaks, waiting on a server is not an option. These are the capabilities we build to move that decision onto the device.
Our Core capabilities
On-device inference tuned to return predictions in milliseconds, offline.
Edge model deployment and orchestration across mixed device fleets.
Hardware-aware compression that fits models onto constrained chips.
Offline-first data sync that survives dropped and intermittent networks.
Edge-to-cloud pipelines that stream results without lag or loss.
Real-time sensor and computer vision processing at the source.
IN THE NEWS
AI-Powered Products · across 25+ industries
Client Retention · teams that stay, project to project
Founded · 51 products shipped since
on Clutch · verified AI development company
Edge AI Development Services: Core Capabilities
Edge AI development services span six problems that only show up once a model has to run outside the data center. Each capability below solves one of them, from the model itself to the fleet it runs on.
On-Device Inference Optimization
Every cloud round trip adds delay your users feel. We rebuild the model to run its predictions directly on the device, returning answers in milliseconds. This is the core of on-device AI inference.
we find the slowest steps in the model and cut them, so inference meets real deadlines. Built with TensorFlow Lite and ONNX Runtime.
the model keeps working when the network does not, because nothing depends on a server reply.
Ready to Move AI Inference to the Edge?
Your users are waiting on a server when the answer could be on the device. That delay is a cost you can remove.
Roadmaps That Shipped Results
Diesel Laptops
Fleet technicians lost minutes on every job hunting through 160,000 parts records. Solution: Kodexo Labs built an AI parts-lookup running inside their own self-hosted AWS VPC, keeping all data on their private cloud. Outcome: lookup time fell 85%, so a technician finds the right part in seconds, not minutes, on every daily job.
85%
Faster Lookup
160,000
Records Searched
Inc. 5000
Client
Extensiv
This $130M-funded logistics platform had operations staff waiting on engineers for every data question spread across 4 databases. Solution: Kodexo Labs built a LangGraph agentic system that reads plain-English questions and answers them directly. Outcome: staff now query 207 tables themselves, at over 90% accuracy, with answers arriving in seconds instead of days.
90%+
Accuracy
207
Tables
$130M+
Funded (Hg Capital)
Therapy Talk
Therapy Talk needed GDPR built into a mental-health platform, not patched in after launch when the data is already exposed. We architected it in from day one. The privacy controls a risk assessment looks for were part of the design, not a later fix. Today the platform serves 1,923 users at 93% accuracy, GDPR-compliant throughout.
1,923
Users
93%
Accuracy
99.9%
uptime
What Clients Say About The Team
Fast-growing organisations do not applaud a consulting partner for polished slide presentations; they praise it for showing up when something actually breaks. The notes below come from founders who watched Kodexo Labs work the problem in real time.
Kodexo Labs has met all expectations; the team delivers on time and manages the project seamlessly. They respond promptly to needs and communicate effectively through virtual meetings, Google Chat, and WhatsApp. Overall, they're highly passionate about the project and excel in customer service.
Christopher Brigham
MD President, Brigham and Associates, Inc.
WATCH VIDEO
- Bedside vitals monitoringWearable device inferenceHIPAA-compliant edge dataOffline-capable patient devices
Edge AI Solutions by Industry
Edge AI looks different in a hospital than it does on a warehouse floor. Below is how on-device inference maps to the work in each of the eight industries we build for most.
See Your Edge AI Roadmap in 30 Minutes
Bring the decision you need to make faster or the data you cannot send to the cloud. In one call, our engineers will tell you what runs at the edge, what stays central, and what it takes to get there.
Compliance-Ready Edge AI Deployments
Edge work changes where data lives, so it changes what compliance looks like. We design on-device AI inference to keep sensitive data on hardware you control. Listen AI shipped HIPAA-compliant from day one, and Diesel Laptops runs entirely inside its own self-hosted AWS VPC, with nothing crossing into a shared cloud.
SOC TYPE 2
ISO 27001
HIPAA
GDPR
CCPA
COPPA
NIST AI RMF
ISO 42001
FERPA
PCI-DSS
SOC TYPE 2
ISO 27001
HIPAA
GDPR
CCPA
COPPA
NIST AI RMF
ISO 42001
FERPA
PCI-DSS
Why Kodexo Labs for Edge AI Development
The question is never whether a model can run at the edge in a demo. It is whether it holds up in production, under real constraints, with your data. Here is where that experience shows.
Real Production Edge Inference
A cloud round trip bets against your product when a call must land now. We test on-device AI inference under real load. Listen AI ships voice replies under 100ms and stays online 99.9%.
Data Sovereignty By Default
Sending client data to another cloud is a risk many teams cannot take. Diesel Laptops runs parts-lookup on-device AI inference in its own AWS VPC, cutting lookup time by 85% across 160,000 records.
Pipelines That Survive Dropouts
Networks drop out, and old data costs you. We build pipelines that feed on-device AI inference during outages. Dynasty Pulse went from 15-minute-old data to a 30-second refresh, a 98% cut in latency.
Catching Sensor Signals Early
A late sensor reading is a problem found late. We tune on-device AI inference to find real signal in noisy sensor data. Vital Connect flags conditions three times earlier, cutting diagnosis time 40%.
Edge AI Is Part of a Bigger Infrastructure Strategy
Most edge projects touch the cloud, the data pipeline, and the security model behind them. If you are weighing where inference should live across your whole stack, that is a conversation worth having early.
Recognised By The Platforms That Vet AI Companies
Kodexo Labs is reviewed where technical buyers do their diligence: Clutch and Upwork. Every badge below links to the live profile.
Overcoming Edge AI Services Challenges
Edge delivery has five failure points that catch most teams by surprise. These are the ones we design around from the first sprint, before they turn into rework.
Every tool listed is in active production on a Kodexo Labs.
Every framework, runtime, and cloud service named here is running on a live client product right now. No theoretical stack, no resume keywords, no tools added for marketing weight.
Our Edge AI Services Process: From Model to Deployed Device
We move from feasibility to a monitored fleet in five phases, each with edge-specific deliverables you can check against.
Discovery and Edge Feasibility Assessment
We start with the decision that has to run at the edge and the hardware it has to run on. You get a feasibility read on latency, memory, and power budgets, plus a clear call on what belongs on-device and what stays in the cloud.
Model Development and Optimization
We build or adapt the model for on-device AI inference, then optimize it against your accuracy and speed targets. You get a working model profiled on the real target constraints, not on data-center hardware that hides the problems.
Hardware Integration and On-Device Testing
We compress the model for the target chip and test it on the actual device, from microcontrollers to NVIDIA Jetson boards. You get measured latency, accuracy, and power figures from hardware, so there are no surprises at deployment.
Deployment and Edge-to-Cloud Pipeline Setup
We roll the model out with staged deployment and rollback, and wire up the pipeline that streams results back with AWS IoT Greengrass and Kafka. You get a live edge fleet and a working link between device and cloud.
Monitoring, Iteration, and Fleet Management
We set up remote monitoring across every device, so drift and failures surface early. You get over-the-air updates, fleet-wide visibility, and a plan for iterating the model as real-world data comes back.
Related Insights on Edge AI
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Frequently Asked Questions About Edge AI Development Services
Edge AI development means building and optimizing AI models to run directly on a device, such as a camera, sensor, phone, or embedded board, instead of sending data to a remote server. The model makes its predictions locally, so results come back in milliseconds and sensitive data can stay on hardware you control.