AI-POWERED
SMART
DEVICES
Child voice recognition tuned on PF-STAR corpus, wake word at sub-1mW, TFLite Micro INT8 quantized models, MobileNetV2 edge vision, EN 71 / ASTM F963 toy safety, and COPPA-compliant data architecture — built at consumer price points.
The Engineering Problem
AI Toys Fail in Three Ways — Bad AI, Bad Safety, Bad Price Points
Cloud voice APIs mean 2-second latency, no offline operation, and persistent audio streaming that fails COPPA. Real conversational toys require on-device wake word at sub-1mW so the microphone always listens without draining the battery, and local intent classification fast enough that children — who have zero patience for loading spinners — stay engaged.
Children's speech has higher F0, greater formant variability, and more disfluency — models trained on adult corpora hit 40–60% WER on 5-year-old speech. Proper child ASR requires PF-STAR corpus training data, per-age-band fine-tuning, and noise robustness for a living room, not an anechoic chamber.
EN 71 and ASTM F963 define bite force, pull force, edge radii, flammability, and electrical clearance requirements that constrain hardware design from the beginning — not at the testing phase. These must be architected in, not retrofitted at certification time.
AI Toy & Smart Device Categories We Build
From conversational companions to robotic kits — custom AI electronics across the interactive toy and consumer smart device spectrum.
Voice & Conversational Toys
Computer Vision Toys
Robotic & Programmable Toys
Educational Smart Devices
Interactive Plush & Companions
AR & Mixed Reality Toy Platforms
Kids Smart Home Devices
Kids Activity & Fitness Wearables
Child Voice Recognition
Wake word detection using DS-CNN at sub-1mW draw, child-optimized ASR trained on PF-STAR and CMU Kids corpora, per-age-band fine-tuning, and noise-robust acoustic models for living room environments.
TFLite Micro Edge Inference
TensorFlow Lite Micro INT8 quantized model deployment, CMSIS-NN kernel acceleration on Arm Cortex-M, operator fusion for latency reduction, and memory footprint optimization for MCUs under 512KB SRAM.
Computer Vision at Toy Cost
MobileNetV2 and EfficientDet-Lite SSD inference on Kendryte K210 / Espressif ESP32-S3 / NXP i.MX RT, camera pipeline design, and model pruning for target accuracy/latency/cost tradeoffs.
EN 71 & ASTM F963 Safety Design
Toy safety requirements translated into hardware constraints — bite force test preparation for input devices, pull force margins on tethered components, sharp edge radius control, and UL94 V0 material qualification.
COPPA-Compliant Architecture
On-device audio processing to avoid persistent cloud audio streaming, minimal data collection architecture, parental consent mechanism design, and FTC COPPA compliance documentation support.
Play-Session Battery Engineering
Dynamic power state management for AI-active vs. standby modes, sub-1mW always-on listening, and battery sizing for 2+ hour active play sessions — critical for parent purchase decisions.
BOM Cost Engineering
Consumer electronics require aggressive BOM cost targets — $8–15 hardware cost for a $40 retail toy. We design for target BOM from the first architecture review, choosing between MCU+NPU options based on capability-per-dollar.
BLE & Wi-Fi Companion App Integration
BLE 5.0 GATT profile design for companion app connectivity, parental control and monitoring features, OTA firmware update architecture, and App Store/Play Store app hardware specification.
Chipsets & Platforms
Platforms, Models & Standards
Tested silicon and proven stacks — no experimental platform dependencies.
Conversational Voice Toys
On-Device Child Speech AI · No Cloud Required
Sub-1mW wake word detection via DS-CNN on child speech data achieves >95% accuracy at <1% false positive in living room noise — entirely on-device, zero cloud. Post-wake intent classification handles 200–500 categories on a second model. Procedural audio library variation creates conversational richness without TTS hardware cost. Wake-to-response in under 500ms.
Computer Vision Toys & Robotics
Edge Vision at Consumer Cost
MobileNetV2 SSD on a $4 NPU module delivers 15 fps object detection — sufficient for color tracking, face recognition, and gesture control. INT8 quantization holds model size to <1MB and inference to <100ms. For robotic toys, the vision pipeline feeds line following, obstacle avoidance, and target tracking directly into the motion control system.
Toy Safety & COPPA Compliance
EN 71 · ASTM F963 · COPPA 16 CFR 312
EN 71 Part 1 bite force, pull force, and compression criteria are geometry and material constraints that must be designed in — not external tests applied to a finished product. We map every EN 71 and ASTM F963 requirement to a specific hardware decision at project kickoff. COPPA keeps AI inference on-device and minimizes cloud data flows, so compliance is structural rather than procedural.
Why Engineering Teams Choose Ankh
AI That Works on Kids
We've solved the child speech recognition problem — not by using a cloud API, but by understanding the phonetic characteristics of children's speech and training models accordingly. Our toys work for a 4-year-old, not just a 34-year-old.
Toy Safety Built In
EN 71 and ASTM F963 requirements are designed into hardware from the first CAD sketch — not bolted on at certification time. We've been through the testing process and know exactly which design choices prevent failure.
Consumer Price Point Engineering
We understand that a toy with a $60 BOM doesn't sell at retail. Every architecture decision is made with the cost target in mind. We've hit $12 BOM targets for sophisticated AI toy hardware.
Privacy by Design
COPPA compliance isn't a policy question — it's a hardware and firmware architecture question. We design the data flows to never create a compliance problem in the first place.
Conversational Learning Companion
A toy company needed an offline conversational learning companion for ages 3–6 — EN 71/ASTM F963 first-pass certification, no cloud voice processing, and battery life parents wouldn't complain about.
On-device pipeline: DS-CNN wake word at sub-1mW → 400-intent classifier → phonics audio library with procedural variation. Child ASR tuned on PF-STAR per age band.
- 96.2% wake word accuracy on 4-year-old test panel
- EN 71 and ASTM F963 passed first submission
- 38-month battery life in typical use pattern (2×AA)
Programmable Vision Robot Kit
An educational robotics brand needed real-time on-device vision in a programmable robot kit at a $40 retail price — COPPA compliant, BLE companion app, zero persistent cloud media storage.
$4 NPU module running MobileNetV2 INT8 at 15 fps for object/color recognition and line following. Block-based BLE programming app. All AI inference on-device — no media leaves the device.
- 15 fps real-time object recognition on $4 NPU module
- COPPA compliant — zero persistent cloud media storage
- $14.20 BOM at 50K unit volume
Let's Make It Magical.
Whether you're building a conversational AI companion, a computer vision robot kit, or a connected consumer device — we engineer the hardware and on-device AI that makes play genuinely intelligent.
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