At ANT Frameworks Private Limited, Hardware-Software Co-Design brings device engineering and software development together as one discipline. We integrate embedded controllers, industrial gateways, and on-device machine-vision units with the applications that manage them, focusing on reliable operation, practical integration, and engineering continuity throughout the product lifecycle. AI and security are built into this approach, encompassing on-device inference and machine vision, device identity, controlled access, and software updates designed for recovery.
Talk to Our TeamWe begin with the physical device: what it must control or observe, how quickly it must respond, and what should happen when a component or connection fails. Hardware interfaces, software responsibilities, and acceptance criteria are defined together. The design separates essential device behavior from connectivity and management features, so a remote service outage does not silently become a control failure. Validation follows the same operating conditions and recovery scenarios agreed at the start of the engagement.
Software for embedded controllers that reads inputs, manages device states, and drives outputs. Work begins with explicit timing, startup, shutdown, and fault-handling requirements. Hardware interfaces and application logic are organized so changes can be reviewed and verified independently.
Gateway software that connects equipment to local applications and cloud services. Interface mapping, message validation, buffering, and reconnection behavior are designed around the connected devices. The aim is to preserve useful data and make communication failures visible to operators.
Software pipelines for image capture, preprocessing, inference, and application decisions on the device. Camera inputs, model requirements, processing budgets, and output behavior are considered together. Evaluation covers representative images and operating conditions, with agreed criteria for uncertain or unusable inputs.
Integrate on-device inference with sensor and image processing workflows. Model evaluation considers accuracy, latency, memory, and power together, with representative inputs and explicit handling of uncertain results. Versioned models and runtime diagnostics help teams understand changes in device behavior across releases.
Define device identity, trust boundaries, authorized interfaces, and update integrity alongside the control software. Review exposed services and protect credentials and sensitive data according to the deployment requirements. Diagnostic signals can be coordinated with our cybersecurity operations and proprietary Neuro® Cybersecurity hub as part of an agreed integration scope.
Bring-up and integration work that checks how the software behaves on the actual target hardware. Sensor readings, peripheral behavior, interface timing, and resource usage are examined together, helping distinguish software defects from hardware or configuration issues.
Update and management workflows designed around the device capabilities and deployment environment. Planning covers device identity, authorized access, update integrity, version tracking, and recovery from interrupted updates. Remote management is scoped to preserve essential local behavior.
Diagnostic events, reproducible builds, release notes, and version compatibility records support ongoing engineering. Our hardware practice retains lifecycle engineering responsibility for the majority of approximately eleven thousand units presently in service; the software process supports that continuity as devices and requirements evolve.
Review target hardware, existing firmware, interfaces, operating conditions, and service expectations. Agree measurable acceptance criteria and identify the behavior that must remain local to the device.
Define software boundaries and hardware interfaces, then develop and integrate device functions in increments. Exercise representative inputs early to surface timing, connectivity, and resource constraints.
Check normal operation, boundary conditions, connection loss, restart, and recovery on the target hardware. Record results against the agreed requirements and resolve defects before release.
Prepare versioned releases, configuration records, diagnostic guidance, and handover documentation. Plan updates and compatibility checks around the deployed hardware and its expected service life.
Hardware-Software Co-Design for embedded controllers, industrial gateways, and on-device machine-vision units, integrating device engineering, software, AI, and security.
Building custom Generative AI tools and autonomous AI agents to automate corporate workflows.
Helping companies migrate their legacy data systems to massive public clouds like AWS and Google Cloud.
Threat detection, risk management, and security operations supported by our proprietary Neuro® Cybersecurity hub.
The practice focuses on embedded controllers, industrial gateways, and on-device machine-vision units. Each engagement starts with a review of the target hardware, interfaces, operating environment, and required software behavior.
An engagement can begin with an existing device. We review available source code, documentation, build tools, hardware access, and known issues before defining an integration or modernization plan. The findings determine what can be retained and what needs to change.
We identify which functions must remain available locally and define how the device should behave during a connection loss. Buffering, retry limits, recovery, and synchronization are then designed around the application requirements and available resources.
In an on-device workflow, image processing and inference run on the unit itself. The engineering work must fit the available compute, memory, power, and timing budgets. We assess those constraints alongside image quality and the application decisions the output needs to support.
The agreed handover should cover source and build instructions, configuration and interface documentation, versioned release artifacts, validation results, known limitations, and diagnostic guidance. Exact deliverables are defined with the project scope.
Yes. Embedded software work can be coordinated with our cloud transformation, digital product engineering, AI and agentic systems, and cybersecurity practices. Interfaces and ownership are defined so the device and platform can evolve with clear compatibility expectations.
AI workloads are evaluated against the device resources and application requirements, while security requirements guide access, data handling, and update design. Both are validated alongside the core device functions, including behavior when inputs are uncertain or a connection is unavailable.
Device diagnostics and security events can be assessed for integration with our proprietary Neuro® Cybersecurity hub. The scope depends on the available device interfaces, connectivity, event formats, and operational response requirements.
Embedded controllers, industrial gateways, and on-device machine-vision units alongside AI, cloud, digital product engineering, and cybersecurity operations.
Secure-by-design practices and risk management run through our delivery process from day one, not as a final audit.
Two India delivery centers with round-the-clock operational coverage for clients across time zones.
Approximately eleven thousand hardware units are presently in service, and we retain lifecycle engineering responsibility for the majority of them.
Let's discuss how ANT Frameworks Private Limited can support your Hardware-Software Co-Design and technology engineering needs - from embedded systems to AI, cloud, digital products, and cybersecurity.
hello@antframeworks.com