AI for IoT & Edge AI
AI and Edge AI engineering for connected products
Bring a model, a signal problem or a product decision. Indeema can help determine what logic is needed, where it should run, and how it fits the hardware, firmware, data path and operating lifecycle of the connected product.
Exploring what intelligence could change in the product? See Intelligent Products & AI
See what we take responsibility for- 01Signal
- 02Method
- 03Place
- 04Fit
- 05Integrate
- 06Prove
- 07Release
- 08Sustain
Starting conditions
Make intelligence work within the product that has to carry it
AI engineering starts with the product decision and its constraints – not with a framework. These are the situations AI engineering usually starts from.
Signals without a decision
We have device data, but have not decided what intelligence belongs in the product.
A model without a runtime
The prototype works, but the real target introduces compute, power, timing and update constraints.
An open placement decision
We need to compare device, gateway and cloud inference against the product’s operating conditions.
An integration boundary
AI has to exchange data and decisions with sensors, firmware, applications and services.
If the product decision and feasibility are still open, start with Product Discovery & Technical Strategy.
Method and placement
Use the simplest method that can support the decision – then place it deliberately
A rule or signal-processing method may be enough. When a model is justified, inference can run on the product, on nearby gateway compute, in the cloud or in an application. None is better in general.
Device
Closest to the sensor and product behavior
Compute, memory, power, thermal limits and field updatesGateway / edge
Local compute can combine context from nearby devices
Gateway availability, topology, versioning and failure behaviorCloud
Central context and larger compute can be available
Connectivity, response time, data movement and operating modelApplication
A person or another system can remain in the decision
Workflow, explanation, confidence and human controlEdge AI means machine-learning inference on the product or nearby local compute rather than relying on a remote cloud for every inference. Placement compares response time, offline behavior, data policy, compute, power, cost, observability and update requirements. These are design considerations, not guaranteed outcomes.
Fit to the real target
A model result is not yet an integrated product capability
Input fidelity, runtime compatibility, memory, compute, power, thermal limits, timing and failure behavior have to agree with the target that carries the method.
- Input
- Are the real signals representative and measurable?
- Runtime
- Can the method execute inside the available envelope?
- Interface
- How do firmware, applications and services consume the result?
- Failure
- What happens when the result is missing, uncertain or wrong?
Scope of responsibility
Hand over one problem, the AI layer or the connected interfaces around it
- 01
Discipline
A bounded signal, algorithm, evaluation or inference-integration problem.
- 02
AI layer
Responsibility from signal and method through validated runtime integration.
- 03
Connected layers
AI coordinated with the required hardware, firmware, cloud and application interfaces.
- 04
Whole product
Product-wide architecture, implementation and verification belong with IoT Product Engineering.
IoT Product Engineering
Engineering lifecycle
AI engineering continues beyond model evaluation
Each stage produces a decision and evidence for the next. Versioning, deployment strategy, observability, rollback requirements and update architecture are designed from the start, scaled to the product: a proof of concept on one device needs far less of them than a model updated across a fleet.
- 01
Signal
Define what the product can sense, and how far that input can be trusted.
- 02
Method
Choose rules, signal processing, a model or a combination against the decision.
- 03
Place
Decide whether inference belongs on the device, a gateway, in the cloud or an application.
- 04
Fit
Test the method against memory, compute, power, thermal and timing limits.
- 05
Integrate
Connect the result to firmware, applications, services and product behavior.
- 06
Prove
Validate the decision in representative conditions, including uncertainty and failure.
- 07
Release
Define how a version is packaged, approved, deployed and rolled back.
- 08
Sustain
Decide what is observed and how a change is reviewed, validated and released.
The product does not improve by itself. A change returns through evidence review, validation, approval and a controlled release.
Evidence, with its limits visible
Three engineering lessons, stated at their real scope
Fit detection to a constrained target
A concept-stage drone proof of concept paired a shark-detection neural network, trained in our lab on drone-view images, with an alert path to a person.
Read the case studyTurn vibration signals into a fault diagnosis
AI analyzes vibration from rotating machinery and identifies the type of fault in bearings and other rotating parts, with alarms and a diagnostic dashboard.
Read the case studyAdapt logic to installed equipment
Detection thresholds were adapted to lifts with different starting currents.
Read the case studyFAQ
AI for IoT questions
What do artificial intelligence development services include?
For connected products, AI work covers the whole path: defining the signals and the decision, choosing rules, signal processing or a model, deciding where inference runs, fitting it to the target hardware, integrating it with firmware, applications and services, validating it in representative conditions, and releasing and updating it under control.
How long does it take to develop an AI solution?
It depends on the decision the product has to make, the data that already exists and the target the method must run on. A bounded pilot that tests feasibility is usually the first step, and its result decides the plan for integration and release.
Can Indeema integrate AI solutions into my existing systems?
Yes. Integration is often the main task: connecting a method to existing sensors, devices, gateways, cloud services and applications, defining the data and decisions exchanged, and deciding what happens when the result is uncertain or unavailable.
Does the AI run on the device, at the edge or in the cloud?
It depends on the product. The device is closest to the sensor but limited in compute, memory and power; a gateway can combine context from nearby devices; the cloud offers central context and larger compute but depends on connectivity. We compare the options against the product’s operating conditions before choosing.
How much does AI solution development cost?
The cost depends on the scope: the data available, the method, the target hardware, the integration work and the validation needed. After a discovery phase we provide an estimate based on your goals and the required functionality.
How do I start my AI project with Indeema Software?
Start with a conversation about the decision you want the product to make and the data you have. We review the system, identify where intelligence can add value and propose a first bounded step, from a feasibility test to a placement study.
How do you ensure the security and privacy of my data?
Privacy is designed into the architecture: deciding which data leaves the device, processing locally at the edge where that reduces what is transmitted, and controlling access to data and models in the cloud. Data handling is agreed with you before the work starts.
Will my AI solution be scalable and future-proof?
No system is future-proof, but it can be built to change safely. We define how a model version is packaged, approved, deployed, observed and rolled back, so the method can be improved as data and requirements change without destabilizing the product.
Ways to work
Define the responsibility before choosing the team shape
Engineering ownership
Indeema owns an agreed AI engineering scope and its interfaces with agreed acceptance criteria at each stage.
Joint product team
Indeema works with your product, domain, data and platform owners around shared decisions.
Specialist team extension
A defined AI or embedded specialty joins a product team you lead, with an explicit responsibility and handoff.
Bring the product decision, available signals, target constraints and what a wrong result would cost.
Discuss your AI engineering scope