Services
End-to-end device, cloud, and operations engineering
From firmware and hardware security to cloud onboarding, release engineering, and AI-assisted fleet operations — we help product teams own the whole system, not just one layer.
Embedded & Device Platform Engineering
When you need us
Embedded DevOps & CI/CD- Product teams stuck on BSP bring-up or integration
- Products that need both low-level Linux and cloud/backend ownership
- Edge devices requiring optimized ML inference
- Teams inheriting undocumented or EOL embedded systems
- Products that need hardware-bound security or licensing
- Teams that need one partner across device, release, and operations boundaries
Board bring-up & Linux BSP
Yocto, Buildroot, bootloader customization for custom hardware platforms.
Device drivers & kernel modules
Custom peripherals, I2C/SPI/UART, industrial buses, and real-time extensions.
C/C++ firmware development
Performance-optimized firmware with clean architecture your team can own.
System integration across boundaries
Device, security, cloud, and backend layers designed as one system instead of split across disconnected vendors.
AI-assisted operations workflows
Operational AI for log triage, support tooling, and engineering knowledge workflows around shipped devices.
Security & Licensing
Hardware-bound licensing, secure boot, and anti-cloning for shipped products.
Security & Licensing
Real-world outcomes
TPM-based licensing- Secure boot and provisioning designed for real shipped products
- TPM-based licensing without fragile copy protection schemes
- Practical hardening and release controls that fit the product lifecycle
TPM 2.0-based licensing
Device-bound anti-cloning with hardware root of trust. No license server fragility.
Hardware crypto engine integration
CAAM and similar — key storage, encryption, and signing in hardware.
Secure boot chain
Implementation and validation of full verified boot from bootloader to userspace.
Device identity & provisioning
Certificate lifecycle, device onboarding, and access control workflows.
IoT Cloud Integration
Platform architecture
AWS IoT Core or Azure IoT Hub — selection, design, and end-to-end implementation.
Secure device onboarding
Certificate provisioning, identity management, and least-privilege access control.
Data models & MQTT topics
Bidirectional communication, shadow/twin state, OTA update pipelines.
Fleet management & scalability
Patterns for managing thousands of devices without operational complexity.
Release Engineering & Embedded DevOps
Where strong teams still get stuck
A device can work on the bench and still be painful to ship. Slow builds, fragile labs, unsigned artifacts, and unreproducible releases are some of the most common blockers in embedded teams.
We turn release engineering into a real capability: CI/CD, HIL, signing, and release discipline that scales with the product instead of depending on heroics.
Where it helps, we also add AI-assisted triage for noisy CI and HIL failures so engineers spend less time parsing logs and more time fixing the right problem.
Reproducible build environments
Docker-based build environments, deterministic toolchains, and less “works on one machine only”.
CI/CD and release gates
Pipelines for firmware and supporting services with clear promotion rules, branch discipline, and actionable failures.
HIL automation
Flash-and-test workflows on real hardware so validation happens before release day, not after it.
Signing and release readiness
Artifact handling, signing flows, release reviews, and hotfix paths for teams shipping real products.
IoT Fleet Update Automation
Safe, staged deployment of firmware and security patches across production device fleets
When you need this
- Shipping security patches to a production fleet under CVE pressure
- Moving from manual SSH-and-pray updates to automated, staged rollouts
- Fleet grew beyond what a script-and-spreadsheet process can handle safely
- Compliance or audit requirements demand deployment traceability and rollback
Works with existing CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions) and IoT cloud platforms (AWS IoT Core, Azure IoT Hub). For devices behind NAT or cellular, Ansible orchestrates the rollout strategy while the cloud platform handles connectivity.
Ansible collections for device fleets
Custom Ansible playbooks and collections for firmware updates, configuration changes, and security patches — with device inventory, grouping, and idempotent execution.
Canary & staged rollout
Deploy to 1% → 10% → 100% with automated health checks between stages. Manual approval gates where the risk profile requires it.
Rolling updates with rollback
Devices update in batches while the fleet stays operational. Automatic rollback on failure — if health checks fail, the device reverts to the previous known-good state.
Security patch fast-track
Targeted CVE remediation across the fleet: update only affected devices, track patch status, and generate compliance reports.
AI at the edge
Most AI consultancies work on web apps and chatbots.We deploy AI where it's hard.
Thermal constraints, data residency, hardware-specific inference runtimes, and device logs that don't look like server metrics. If your AI challenge involves real hardware, we're probably a better fit than a general-purpose AI shop.
Edge AI Deployment
From trained model to production inference on real embedded hardware
We don't train models — we make them run
Most edge AI projects don't fail because the model is wrong. They fail because the model doesn't run on the target hardware at production quality — wrong FPS, accuracy cliff after INT8, thermal throttling in the enclosure, or a single unsupported op that silently falls back to CPU.
We take your trained model (or a standard open-source one) and get it to production on your specific board. Profiling, optimization, hardware-specific compilation, and validation on the actual deployment hardware — not a dev kit with the heatsink off.
Model optimization for constrained targets
INT8/INT4 quantization, structured pruning, and hardware-specific graph compilation for Jetson, i.MX, AM62A, and similar platforms.
Hardware-specific deployment
TensorRT on Jetson, eIQ on i.MX NPU, Edge AI SDK on TI — we handle the gap between "works in Python" and "runs at 30 FPS on the board".
Deployment failure debugging
Accuracy collapse after quantization, thermal throttling, power spikes, and NPU compatibility issues — the failures that kill projects after the prototype worked.
Integration into product pipeline
Inference integrated into your firmware, CI-validated, and optimized for your specific board variant — not a lab demo.
AI System Security
Protecting AI models, pipelines, and agent workflows on edge and embedded devices
Where AI security meets real hardware
Most AI security platforms focus on cloud-hosted models and SaaS guardrails. We focus on the harder problem: securing AI systems that run on edge devices, embedded hardware, and constrained infrastructure where you control the full stack.
This combines our hardware security expertise (TPM, secure boot, platform hardening) with practical AI deployment experience — threat modeling, model signing, data residency architecture, and guardrails for on-device LLM agents that operate offline or with limited connectivity.
AI threat modeling
Systematic analysis of attack surfaces for edge AI deployments — adversarial inputs, model extraction, data poisoning, and inference manipulation.
Model integrity & secure delivery
Signed model artifacts, TPM-backed attestation, and secure OTA pipelines so only authenticated models run on your devices.
Data residency architecture
On-premise inference, local embeddings, and controlled data flows — so sensitive inputs and model outputs never leave your network.
LLM & agent guardrails
Input validation, output filtering, tool-access control, and PII/secret leakage prevention for on-device LLM and agent workflows.
Firmware Log Intelligence & Fleet Observability
Focused on real support and operations workflows
We use AI where it helps teams move faster on real incidents: log triage, evidence gathering, recurring issue handling, and operational reviews. This is not a generic chatbot offering.
The best fit is a live fleet where device logs, firmware versions, backend behavior, release state, and known issues all need to be reasoned about together.
Business impact
- Faster first-level triage for support and R&D teams
- Evidence-grounded summaries instead of generic AI guesses
- Private deployment options when telemetry cannot leave your network
- Better signal from noisy fleet incidents and recurring issue patterns
Evidence-grounded log triage
LLM-assisted triage of device and backend logs with incident isolation, supporting evidence, and human-readable summaries.
Fleet anomaly surfacing
Highlight recurring failure patterns, noisy cohorts, and signals that deserve engineering attention across live fleets.
Private deployment options
On-prem and controlled-data-flow setups for organizations that cannot send code or telemetry to third parties.
Support workflow integration
Known-issue matching, retrieval over docs/changelogs, and outputs that support and R&D teams can actually use.
Firmware Log Intelligence (Linux edition)
High-volume device logs → incident window → evidence-grounded root cause summary
This is the “big logs” edition: Linux-based devices, mixed kernel/app output, and complex failure sequences. The key to accuracy is isolating the failure window first — then performing retrieval and analysis only on the high-signal slice.
Typical output includes: a human-readable RCA summary, supporting evidence lines, relevant source-code snippets, and next-step actions.
Built for noisy Linux logs
Handles thousands of lines per minute (kernel + userspace) using an incident-window stage before analysis.
Grounded in your code and docs
Local vectorization of your repo/docs for retrieval — only top relevant snippets are used for the final analysis.
Known-issue matching
Optional Jira knowledge base matching so recurring issues resolve instantly with the right ticket.
On-prem by default
Runs inside your infrastructure. Supports hosted LLM APIs for best quality, with private/OSS options where feasible.
Hard Legacy Embedded Projects
A real example
We inherited a production system built on a hand-assembled Linux with kernel 3.x, compiled on a CentOS version that had been end-of-life for years. No build documentation. No tests. The engineer who built it had left.
We could not simply rewrite it — real products depended on it. Instead: first we wrote tests that captured every observable behaviour of the existing system. Then we migrated the BSP to Buildroot, the build system to CMake, and the host environment to Ubuntu 24. Then we fixed what broke until every test passed.
The client got a system they could actually build, understand, and hand to a new engineer. Production was never interrupted.
Undocumented system takeover
We take full ownership of systems where the original author is gone and the documentation never existed. Production stays running while we learn the system from the inside.
Test-first migration methodology
Before touching anything, we write tests that capture actual system behaviour. Only then do we refactor — so every change is verifiable against what the system was doing in production.
Build system and BSP modernization
From hand-rolled Linux on EOL distributions to modern Buildroot, CMake, and supported toolchains — without rewriting the application logic.
Knowledge transfer
We document as we go. By the end of the engagement your team understands the system, can maintain it, and is not dependent on us.
Selected Case Studies
Representative projects delivered by principal engineers (client identities confidential)
We mention technologies and ecosystems for context. We do not disclose client identities or proprietary details publicly.
Firmware log intelligence pipeline
On-prem log triage with local retrieval over code/docs and evidence-grounded root cause summaries for support and R&D teams.
Release engineering transformation for embedded team
Jenkins, Docker registry, reproducible builds, HIL automation, and better release discipline for a hardware product team.
Cloud platform for mesh/network devices
Device management backend, fleet telemetry, and operator workflows for large deployments.
TPM-based licensing for embedded products
Hardware-bound licensing with secure activation flows and CI/CD signing integration.
Ongoing Support & Long-Term Engineering
Recurring engagements built around concrete technical ownership
For many clients, the right next step after delivery is not a vague support retainer. It is long-term ownership of one important layer: sustaining engineering, security maintenance, observability, release engineering, or licensing operations.
We package that as monthly, quarterly, or long-term recurring support depending on the product stage and the level of responsibility required. Duration is not capped — engagements run as long as the client has ongoing need.
Embedded LTS & sustaining work
Kernel/BSP maintenance, dependency updates, release support, and product upkeep after launch.
Release engineering support
CI/CD reliability, HIL upkeep, signing flows, release reviews, and hotfix support for live products.
Fleet update automation support
Ongoing maintenance of fleet update playbooks, rollout strategy tuning, new device variant onboarding, and deployment pipeline reliability.
Fleet log triage & observability support
Ongoing improvement of incident analysis, retrieval quality, and recurring issue handling for active device fleets.
Security maintenance & secure coding
Scanner integration, CI/CD security gates, Yocto CVE review, release hardening, secure coding workshops, and review standards.
AI Security Retainer
Model integrity, guardrail tuning, agent access review, data residency audit, and supply-chain hygiene for products deploying AI on edge or on-prem.
Licensing operations support
TPM-based activation workflow maintenance, customer issue triage, and resilience improvements for production license flows.
FAQ
Common questions
Quick answers about process, security, and engagement
How do we start working together?
We start with a 30-minute technical call. No sales pitch — we clarify scope, constraints, and what success looks like.
Do you sign NDAs?
Yes — confidentiality is standard. We can work under your NDA or provide ours.
How quickly can you start?
Typically within 1–2 weeks depending on scope and access to hardware/code.
Can you join an in-flight codebase?
Yes. Code review and technical debt assessment is often our first step.
Is our code/telemetry sent to the cloud?
By default, vectorization and retrieval run inside your infrastructure. If an LLM API is used, only minimal relevant snippets are sent.
Do you do hardware design?
We do software with strong electronics knowledge for bring-up/debugging, but not PCB layout or schematic design.
Do you offer ongoing or annual support?
Yes. We offer maintenance retainers and long-term engineering support tied to a concrete technical layer such as BSP maintenance, fleet update automation, observability, security hardening, release engineering, or licensing operations. Duration is not capped — engagements run as long as the client has ongoing need.
Not sure which service fits?
Describe your system and we'll figure it out together. Most engagements start with a 1-week discovery call.