AI Engine Integration Built Into Your Product

Under Services → TinyML & Edge AI → AI Engine Integration, WorkSprout wires inference runtimes into your firmware and application layers — TensorFlow Lite, ONNX Runtime, Coral, Jetson, and STM32 NPUs behind one stable API with observability your team can ship on every release.

Lightweight ML for Embedded Systems TinyML Solutions AI Engine Integration Edge AI Deployment Edge Model Optimization

Lightweight ML for Embedded Systems

Model selection, quantization, and deployment pipelines for microcontrollers and embedded targets — accurate inference within tight memory and power budgets.

TinyML Solutions

End-to-end TinyML programmes — sensor fusion, on-device training workflows, and production firmware integration for real-world edge products.

AI Engine Integration

Integrate TensorFlow Lite, ONNX Runtime, and vendor NPUs into existing firmware and application layers with stable APIs and observability.

Edge AI Deployment

Field deployment of edge AI — OTA update paths, device fleets, monitoring, and rollback strategies for production edge inference.

Edge Model Optimization

Pruning, distillation, INT8 quantization, and kernel tuning so models meet latency and energy targets on target silicon.

NexusCam

AI Engine Integration

Q4 2025

WorkSprout Inference Team

NexusCam: Unified Inference API Across TFLite, ONNX, and Jetson NPU in a Vision Product

WorkSprout integrated TensorFlow Lite, ONNX Runtime, and Jetson NPU offload for NexusCam — replacing brittle glue code with a single C++ inference surface, per-inference telemetry, and CI/CD model promotion that unblocked the embedded release train.

TensorFlow Lite ONNX Runtime TensorRT Coral Prometheus GitHub Actions

10 Wk.Audit to Production

3Runtimes Unified

99.9%Runtime Uptime

100%Client Satisfaction

Inference Engine Integration

What WorkSprout delivers for AI engine integration — runtime bindings, unified APIs, NPU offload, and observability wired into your existing product stack.

1 Unified inference API

TensorFlow Lite & ONNX Runtime

Stable C/C++ and Python bindings inside your product stack.

Vendor NPU & DSP offload

Coral, Jetson, Apple Neural Engine, and STM32 NPUs where available.

Unified inference APIs

One interface for cloud fallback and on-device execution paths.

Pre/post-processing libraries

Shared vision and audio pipelines reused across product SKUs.

Observability hooks

Per-inference latency, memory, and confidence exported to your metrics stack.

CI/CD for model artifacts

Signed model bundles promoted through staging to production firmware.

Explore TinyML services

What You Get with AI Engine Integration

Production-grade inference layers and signed model pipelines — not ad-hoc bindings — so ML and firmware teams ship on the same release cadence.

Unified C++ inference SDK

Single API surface over TFLite, ONNX, and hardware offload backends.

Runtime abstraction layer

Swap backends without rewriting application inference calls.

Per-inference telemetry

Latency, memory, confidence, and error codes on every call.

Signed model promotion

Checksums, staging gates, and production promotion through CI/CD.

Regression test harness

Accuracy and latency thresholds enforced on every model promotion.

Integration documentation

API references and architecture guides for firmware and ML teams.

01 — Problem

Why Inference Integration Breaks

NexusCam had three inference runtimes stitched with custom glue code — no shared API, silent model failures in production, and every silicon revision broke the integration layer.

"We had TFLite, ONNX, and Jetson paths — but no single API, no telemetry when inference failed, and ML could not ship without blocking firmware releases."

Multiple inference runtimes stitched with brittle custom glue code
No observability when models fail silently in production firmware
Integration breaks on every new silicon revision or OS update
ML team cannot ship without blocking the embedded release train
Different preprocessing paths per runtime with no shared libraries

Product Stack

Embedded Linux camera product with Jetson NPU and cloud fallback path.

Runtimes

TensorFlow Lite, ONNX Runtime, and vendor NPU delegates in parallel.

Timeline

10-week integration: 3 weeks audit/API, 4 weeks bindings, 3 weeks CI/CD cutover.

Deliverables

Unified SDK, observability hooks, signed bundles, and CI/CD pipelines.

02 — Strategy

Our Integration Approach

Audit existing runtimes, design one inference surface with cloud fallback paths, add observability from day one, then promote signed model bundles through CI/CD.

Runtime audit

Map existing inference paths, backends, and failure modes in production.

API design

One inference surface with cloud fallback and hardware offload hooks.

Bind & observe

Implement bindings, telemetry, and regression gates before cutover.

03 — Stack

Runtime & NPU Toolkit

Inference engines and hardware accelerators we integrate into product firmware and application stacks.

On-Device ML Runtimes

TensorFlow Lite Micro, ONNX Runtime, CMSIS-NN, and PyTorch export paths sized for MCU flash and SRAM. Applied to AI Engine Integration engagements.

Tensorflow

ONNX

Tensorflow

Pytorch

ARM

Arduino

Embedded RTOS & MCU

FreeRTOS, Zephyr, STM32, and ESP32 firmware integration with deterministic inference scheduling. Applied to AI Engine Integration engagements.

ARM

Linux

STM32

ARM

Arduino

ESP32

Edge Compute & Vision

Jetson, Coral, OpenCV, and GPU-class pipelines for perception workloads at the edge. Applied to AI Engine Integration engagements.

Python

Tensorflow

ONNX

Raspberry Pi

MQTT

NVIDIA

Fleet OTA & Observability

MQTT telemetry, Grafana dashboards, Prometheus metrics, and CI/CD for model promotion. Applied to AI Engine Integration engagements.

MQTT

Grafana

Prometheus

GitHub Actions

Docker

Python

04 — Process

Integration Delivery Process

Runtime audit → API design → binding implementation → observability → CI/CD — with regression gates before every production promotion.

Audit

Inventory runtimes, call sites, and production failure patterns.

Design API

Unified inference interface with backend selection and fallback rules.

Implement bindings

TFLite, ONNX, and NPU delegates behind one SDK surface.

Add observability

Per-call telemetry, error reporting, and dashboard integration.

CI/CD cutover

Signed model promotion with staging gates and regression tests.

Care

Runtime updates, backend compatibility, and optional support retainer.

Tools Used: TensorFlow LiteONNX RuntimeTensorRTPrometheusGitHub Actions

05 — Milestones

Integration Snapshots

Visual milestones across a typical AI engine integration engagement — from runtime audit through production cutover.

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06 — Delivery

Integration Deliverables

Unified inference SDK, observability hooks, signed model bundles, and CI/CD pipelines delivered for NexusCam production releases.

Unified API, observability pack, and CI/CD model promotion in production

07 — In Product

Inference Live in Production

How NexusCam runs multi-runtime inference in the field — latency telemetry, confidence scores, and model version tracking per device.

On-device inference Telemetry dashboard Model promotions

worksprout.us/portfolio

Live

NexusCam Inference Layer

TFLite · ONNX Runtime · Jetson NPU · Unified C++ API · Per-inference telemetry

View portfolio

DeliveredQ4 2025

Duration10 Weeks

ServiceAI Engine Integration

Runtimes3

Uptime99.9%

Satisfaction100%

08 — Impact

Results from Engine Integration Work

Within 60 days of cutover, NexusCam achieved 99.9% runtime uptime, sub-5ms integration overhead, and parallel ML/firmware release trains.

1 Unified Inference API

Single surface for TFLite, ONNX, and Jetson NPU backends.

99.9% Runtime Uptime

Production inference layer stable across silicon revisions.

<5ms Integration Overhead

Abstraction layer added minimal latency versus direct runtime calls.

Key outcome: One inference API across TFLite, ONNX, and Jetson NPU meant the ML team promoted models through CI/CD without blocking firmware — silent failures became visible through per-inference telemetry.

09 — Docs

Architecture & API Visuals

Integration diagrams, API references, and observability artefacts from the NexusCam AI engine engagement.

10 — Client Voice

Client Testimonial

★★★★★

"WorkSprout replaced our runtime glue code with a proper inference layer — one API, TFLite and ONNX behind it, Jetson offload when available, and telemetry on every call. Our ML team finally ships on the same cadence as firmware."

11 — Workflow