Service // All IoT development services
Harness AI/ML and IoT for Intelligent Automation
Our services harness AI/ML and IoT to automate operations, enhance efficiency, and enable intelligent data analysis for timely business decisions. Add artificial intelligence to your IoT ecosystem for smarter, more responsive systems.
OUTCOMES
What this work is measured on.
The outcomes engagements in this practice aim at, and how we track them.
We train models on your operational data and test predictions against held-out history before go-live. Accuracy is reported on your data, not a vendor benchmark.
We baseline how long an anomaly goes unnoticed today, then track the same interval once models watch the stream.
We record what a monitored decision costs before automation and count the same cost after models take the routine cases.
You see how many decisions still need a person each month, so the value of the automation is measured, not asserted.
WHERE THIS SITS // CWR90
This is Walk work: days 31-60 of the 90. The build phase, where the agent takes shape inside your systems.
COVERAGE
What the engagement covers.
From strategy to implementation, every layer of the build is owned.
Predictive Analytics
Leverage ML algorithms to predict equipment failures, maintenance needs, and operational trends from IoT data.
Intelligent Automation
Implement automation that learns and adapts to optimize operations and reduce manual intervention.
Anomaly Detection
Detect unusual patterns and anomalies in IoT data streams using advanced machine learning algorithms.
Edge AI Processing
Deploy AI models at the edge for real-time processing, reduced latency, and improved response times.
Natural Language Processing
Enable voice control and natural language interfaces for IoT devices and systems.
Computer Vision Integration
Integrate computer vision capabilities for visual inspection, quality control, and automated monitoring.
INDUSTRIES
Where this already runs.
Sector experience that shortens the path from scoping to shipping.
Smart Manufacturing
Predictive maintenance, quality control, and production optimization
Smart Cities
Intelligent traffic management, energy optimization, and urban planning
Healthcare
Patient monitoring, diagnostic assistance, and treatment optimization
Agriculture
Precision farming with crop monitoring and yield optimization
Energy & Utilities
Smart grid management, energy consumption optimization, and predictive maintenance
Transportation
Autonomous vehicle systems, traffic optimization, and fleet management
PROCESS
How the work runs.
A fixed sequence with sign-off gates, so you always know where the engagement stands.
- 01
Data Assessment
Analyze IoT data sources and AI/ML requirements for intelligent automation
- 02
AI Model Development
Develop and train custom AI models for specific IoT use cases
- 03
Integration & Deployment
Integrate AI models with IoT infrastructure and deploy edge processing
- 04
Optimization & Learning
Continuously optimize AI models and enable adaptive learning capabilities
FAQ // QUESTIONS
Frequently asked questions.
Direct answers about scope, timelines, and how delivery works.
What is AIoT and how does it differ from traditional IoT?
AIoT (Artificial Intelligence of Things) combines AI/ML capabilities with IoT devices to create intelligent, self-learning systems. Unlike traditional IoT that simply collects and transmits data, AIoT analyzes data in real-time, makes autonomous decisions, predicts outcomes, and continuously improves performance through machine learning.
What types of AI algorithms do you implement in IoT systems?
We implement various AI algorithms including machine learning for pattern recognition, deep learning for complex data analysis, computer vision for image processing, natural language processing for voice interfaces, predictive analytics for forecasting, and reinforcement learning for autonomous decision-making.
How do you handle edge AI processing in IoT environments?
We deploy lightweight AI models on edge devices for real-time processing, reducing latency and bandwidth requirements. Our edge AI solutions include model optimization, local inference capabilities, federated learning, and intelligent data filtering to ensure efficient processing at the network edge.
What are the key benefits of implementing AIoT in business operations?
Key benefits include predictive maintenance reducing downtime, intelligent automation improving efficiency, real-time anomaly detection preventing failures, adaptive systems that learn and improve, reduced operational costs, enhanced decision-making speed, and improved customer experiences through personalized interactions.
How long does an AIoT project take and what do you need to get started?
AIoT development engagements run weeks to months depending on scope; data readiness, device variety, and edge versus cloud inference are what move the timeline most. To start, we need access to your device data or a plan to collect it, a technical contact for your current systems, and a clear statement of the decision the AI is supposed to improve. We confirm all three in the first engineering call.
Who owns the models, code, and device data after an AIoT project?
You do. The firmware, application code, trained models, and every byte of device data produced during an AIoT engagement belong to your organization, transferred with documentation at handover. We keep nothing proprietary in the stack that would lock you into working with us.
How do you measure whether an AIoT solution is actually working?
We agree a baseline before development starts: the downtime, defect rate, or cost figure the AIoT system is meant to move. The same number is tracked once the system is in operation, so success is read from your data rather than our reporting. If the number does not move, that is a finding we deal with, not one we hide.
What does post-launch support look like for AIoT systems?
AIoT systems drift: models degrade as conditions change and device fleets need patching. Our post-launch support covers model monitoring and retraining, firmware and security updates across the fleet, and performance reviews on a schedule agreed with you. You can run this in-house with our documentation or keep us on a retainer.
How do you secure AIoT devices and the data they generate?
We encrypt data in transit and at rest, use certificate-based device authentication, and segment device networks from business systems. Model endpoints and data pipelines are access-controlled and audited. Our processes are ISO 9001:2015 certified and CMMI L5 assessed, and security review is part of every AIoT delivery phase, not a final checkbox.
How is AIoT development priced?
We do not publish rate cards because AIoT scope varies too much for a generic figure to be honest. After an engineering call we send a scoped proposal, priced either fixed-scope for well-defined builds or as a retainer for ongoing development and model maintenance. You see exactly what is being built before anything is signed.
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