AI Integration Services for Hardware and Software Products
AI integration services for teams that need a model to run inside a real product: on-device inference on microcontrollers and Linux SoCs, computer vision and audio pipelines, and LLM-backed features in apps and SaaS. We build it with the firmware, the app and the back end, measure it against real data, and keep it running after launch.
What AI integration services actually cover
AI integration services take a model and make it a dependable part of a product: a device that recognizes a sound or a fault, an app that answers from your own documents. The model is rarely the hard part. Everything around it is: the right data in, inference where it fits, outputs the rest of the system can trust, and a way to know next month whether it still works. That work is engineering, and at inteRFace R&D it is what we do.
Edge AI and on-device inference on MCUs and Linux SoCs
Edge AI development starts from the budget, not the model. A microcontroller offers kilobytes of RAM and milliwatts of power; a Linux SoC with a neural accelerator offers far more at the price of a bigger board and a heavier supply. The question is not which model is most accurate but which is accurate enough inside the memory, latency and power the product can afford, and we answer it with measurements on the target part before anyone commits to a board.
Model selection, quantization and the memory budget
Embedded machine learning on a Cortex-M class part is a real option for keyword spotting, anomaly detection on vibration or current, and gesture or activity classification. The model is quantized to 8-bit integers, the runtime (TensorFlow Lite for Microcontrollers, CMSIS-NN or the vendor's toolchain) is matched to the silicon, and the tensor arena is sized against what the rest of the firmware still needs. The microcontroller choice matters most here: the ESP32-S3 carries vector instructions for this work and the newest STM32 parts add a neural accelerator. Our free MCU Selector shows how we score candidates.
Computer vision and audio pipelines on embedded devices
Computer vision on embedded devices is a pipeline, not a model, and every stage from capture to action has a time and a power budget. Person detection at a few frames a second runs on a microcontroller with a small camera; full-resolution inspection wants a Linux SoC with an NPU, the i.MX 8M Plus and RK3588 class of part. Audio has the same shape: a cheap wake stage runs continuously and the expensive model runs only when woken. The state machine, the duty cycling, the thermal behavior and the update path for new model versions are the parts a model card never mentions, and most of the work.
We have delivered that shape: firmware and system integration for an embedded-Linux audio device with on-device inference and over-the-air updates, about 120 engineering hours. The firmware side on its own is our firmware development page.
When the cloud is the better place
Sometimes the honest answer is that the model should not run on the device. Large models, models that change weekly and features that need the fleet's whole history belong in the cloud, where a bigger model costs an API call instead of a board respin. The usual middle ground is a hybrid, with a small on-device model gating the expensive call. The ingestion, storage and fleet side of that is cloud and IoT back end work.
LLM integration for apps and SaaS products
LLM integration for a product is a different job from a demo, and the prompt is the small part. The rest is retrieval over your own data so the model answers from facts, tool use so it can act in your system, output constrained to a schema your code validates, refusal and fallback paths, a cost and latency budget for every call, and a plan for the day the provider retires the model name. An AI feature for a SaaS product that skips any of these works in the demo and fails in the second month.
We run products built this way ourselves. The AI Project Estimator on this site is an LLM-driven estimator built from our real delivered quotes: describe a product in a paragraph and it returns a planning range in engineering hours. ArduinoStudio, another product of ours, generates the logic, the app and the wiring for real Arduino hardware. The platform around such a feature is covered on our SaaS and software development page.
Evaluation, guardrails and cost per call
Nothing ships without an evaluation set: real inputs the feature will see, paired with outputs a domain expert would accept. Every change to the prompt, the model or the retrieval is scored against it, and after launch a sample of production traffic is reviewed the same way, so drift shows up as a number rather than a support ticket. Cost per call gets the same treatment: budgeted at design time, alarmed in production.
AI for hardware products: decided with the firmware, the app and the cloud
AI for hardware products is where the cross-domain decisions pile up. A model that needs two megabytes of RAM chooses the microcontroller. A camera that must run at ten frames a second chooses the SoC, the power supply and the thermal design. A model that will be updated chooses the OTA architecture. Each is cheap to decide in week one and expensive to reverse once the board is laid out, so we decide them together rather than one vendor at a time.
If the feature needs a new board or a different SoC, our embedded hardware design team runs it as one accountable project. If the risk is the real-time data path, that is the problem we solved for ELDA BrainTech: the software behind a neurodiagnostic medical device, with real-time acquisition from FPGA-based hardware and the GUIs the team uses daily, now stable and on its way to the FDA. Data has to arrive, be timestamped and be trusted before anything is inferred from it; the ELDA case study has the details.
What AI integration work does not include
We do not train foundation models from scratch on huge datasets. We select, fine-tune where a small, well-annotated dataset justifies it, quantize, integrate and evaluate. We do not promise an accuracy figure before there is evaluation data to measure it on; if you do not have that data yet, collecting it is the first phase. And when a threshold and a rule beat a model, we say so.
What you receive from an AI integration engagement
Every engagement ends in files and numbers you own and could hand to anyone:
- A feature specification: inputs, outputs, latency, the accuracy target and how it is measured, and the power budget or cost per call it must fit.
- A model selection report: the candidates tried on the target hardware or provider, with measured accuracy, memory, latency and cost, and why one was chosen.
- The integrated model: the quantized artifact or the prompt, retrieval and tool definitions, with the code that runs it, in your repository.
- An evaluation set and the harness that scores it, with baseline results, so every later change has a number to beat.
- Production monitoring for accuracy, drift, latency and cost, with alarms.
- Documentation and a handover. You keep 100% of the IP, we never take equity, and a mutual NDA precedes any technical detail.
How an AI integration engagement is priced
Priced in engineering hours; you get a written quote with phases before any work starts. The first phase is usually a bounded feasibility spike that answers one question with numbers, such as whether this model runs on this part inside this budget. For calibration, the audio project above was about 120 engineering hours, and most projects we quote total between 40 and 250 engineering hours. For a planning range before the call, the free project cost estimator turns a scope into hours in minutes.
Where we do AI integration work, and how an AI integration engagement starts
We are based in Silicon Valley and work with teams across the United States, mostly remotely; how a nationwide engagement runs is on our United States page. Devices ship to our bench by carrier and inference runs are demonstrated on video with the hardware on camera. In-person days in the Bay Area are available by arrangement.
The first step is a free 30-minute call with a senior engineer; bring the feature and the constraint you are not sure it can meet. Next comes the bounded feasibility spike, quoted in writing: at its end you hold the measurements, the model recommendation and a written quote with phases for the integration, whether or not we do it.
If you are comparing AI integration services, the fastest way to find out whether we are the right partner is that call: book a time. If you would rather start with a number, describe your product to the free AI Project Estimator and bring the result.
Frequently Asked Questions
What do AI integration services include?
The work that turns a model into a dependable product feature: choosing where it runs (microcontroller, Linux SoC, application or cloud), selecting and quantizing or prompting the model, building the data path and pipeline around it, constraining and validating its outputs, scoring it against an evaluation set, and monitoring accuracy, latency and cost after launch. It covers on-device inference on embedded hardware, vision and audio pipelines, and LLM features in apps and SaaS, delivered by the same engineers who write the firmware, the application and the back end.
Can a machine learning model run on a microcontroller?
Yes, for the right model. Keyword spotting, anomaly detection on vibration or current, gesture and activity classification and simple sensor fusion all run on Cortex-M class parts once the model is quantized to 8-bit integers and the runtime matches the silicon. The limits are RAM, flash and power, and they are hard limits, so we measure the model on the target part before the board is committed. Larger vision models and anything language-based want a Linux SoC with an accelerator, or the cloud.
Should the model run on the device or in the cloud?
It depends on latency, privacy, connectivity, power and cost. On-device wins when the product must respond instantly, work offline, keep data local or avoid a cost on every call. The cloud wins for large models, models that change often and features that need the whole fleet's history. Many products end up hybrid: a small on-device model handles the common case and escalates the hard ones. We put numbers on both sides before recommending either.
How do you add an LLM feature to a SaaS product without it making things up?
By never letting the model be the only safeguard. Retrieval grounds answers in your own data, output is constrained to a schema your code validates, tool calls go through your own functions, and an evaluation set of real inputs with expert-approved outputs scores every change before it ships. In production a sample of live traffic is reviewed against the same set, so a drop in quality shows up as a number. The provider is abstracted too, so a retired model name is a configuration change, not an outage.
Do you train models from scratch?
No. We do not train foundation models on huge datasets, and we say so up front. We select from open-weight and commercial models, fine-tune when a small, well-annotated dataset justifies it, quantize for the target hardware, integrate and evaluate. If you do not yet have evaluation data, collecting and annotating it is the first phase, because we will not promise an accuracy figure that nothing can measure. When a rule beats a model, we recommend the rule.
How much does AI integration cost?
It is priced in engineering hours, and you get a written quote with phases before any work starts. The first phase is usually a bounded feasibility spike that answers one question with measurements. As a reference from delivered work, firmware and system integration for an embedded-Linux audio device with on-device inference and over-the-air updates came to about 120 engineering hours, and most projects we quote total 40 to 250 hours. The free AI Project Estimator on the homepage gives a planning range from a paragraph describing your product.
Ready to talk specifics?
A 30-minute call with a senior engineer beats three rounds of email. Tell us what you're building.