AI hardware automation engineering studio
MachineLoop is an engineering studio for AI that needs sensors, displays, cameras, audio, embedded systems, control boxes, actuators, and humans in the loop. We help teams turn software intelligence into real-world automation.
What MachineLoop builds
AI can reason, write, and call APIs. Many businesses still need the missing physical link: sensors that observe, firmware that controls, displays that guide, cameras and microphones that detect, and humans who verify edge cases before risky actions happen.
Automation partner
Focused automation projects for teams that need AI connected to sensors, control logic, data logging, displays, alerts, dashboards, or actuator control without hiring a full-time embedded team.
Read detailsHumans as API
Operational workflows where AI agents can ask trained humans to inspect, verify, label, decide, or handle edge cases that software and sensors should not own alone.
Read detailsDevice architecture
Architecture and prototype design for embedded systems that combine MCUs, sensors, power, wiring, enclosures, connectivity, and firmware.
Read detailsPrototype boxes
Small control boxes, test rigs, data capture systems, and operator panels for labs, workshops, greenhouses, and machine-adjacent workflows.
Read detailsAI with hardware
Camera, audio, and sensor intelligence on Jetson Nano-class devices, embedded Linux systems, and cloud-assisted AI workflows.
Read detailsBuild packets
Technical scopes, BOMs, firmware prototypes, wiring notes, vendor packets, and implementation support for teams that need the physical build to keep moving.
Read detailsRelevant project examples
These examples combine hardware constraints with software, AI, and deployment thinking. That is where many automation projects fail: the model, sensor, enclosure, network, operator workflow, human fallback, and maintenance path are treated as separate problems.
Horticulture / Jetson Nano / custom ML
Built around canopy-level plant observation: camera capture, Jetson Nano processing, custom ML models, and automation logic for detecting water need and insect pressure on plants. Useful for growers who need earlier warnings without checking every row manually.
Sports video / GStreamer / AWS APIs
A Jetson Nano system using GStreamer and AWS APIs for live camera handling and score tracking in school sports. The useful pattern: edge video capture, local processing, cloud APIs, and event-aware media workflows.
Control boxes / outsourced automation
The Jason-inspired lane: small control boxes with a microcontroller, sensor suite, Raspberry Pi or embedded Linux UI, CSV/data logging, and clear wiring so a one-off bench system can become a repeatable field unit.
AI integration / human-in-the-loop
AWS, OpenAI, Claude, and local ML can sit behind cameras, microphones, sensors, and device telemetry. The goal is not AI decoration; it is better detection, summaries, alerts, configuration, operator support, and human escalation when confidence is low.
Good fit
MachineLoop is a good fit when you need an engineering studio to connect AI to the real world: a working prototype, outsourced automation help, a debug sprint, a firmware layer, a sensor and camera plan, a human-in-the-loop workflow, or a technical scope another vendor can build from. Larger PCB layout, certified manufacturing, enclosure production, and on-site installation can be coordinated with specialists when needed.
Start small
A good first message includes current hardware, MCU or board, sensors, camera/audio inputs, what already works, what fails, who operates it, and what the system must do in the real world. If humans need to verify, label, inspect, approve, or handle exceptions, include that too.
Worldwide clients are welcome. Istanbul/Ankara-region physical installation can be discussed when the scope needs hands-on work.