AI isn't a service line here — it's in every layer above.
On-device inference, predictive maintenance, computer vision and in-app copilots — where AI earns its place in the product, not where it makes a good slide.
AI & Intelligence
Not a bolt-on chatbot — AI woven into the product itself, and into how we build every other layer above.
- On-device / edge inference
- Predictive maintenance & anomaly detection
- Computer vision & sensor fusion
- LLM-powered in-app copilots
- Model lifecycle & retraining pipelines
Where AI accelerates
- Model integration & inference pipeline code
- Data-processing & feature-extraction scaffolding
- Evaluation-harness scaffolding
- Training-data pipeline scaffolding
- Model-serving boilerplate
Where it stays human
- Model selection & accuracy/recall tradeoffs
- False-positive management & validation
- Bias & fairness review
- Edge-case failure triage
- Production monitoring thresholds
Industries we've proven this layer in
Real builds that leaned on this layer.
Pulled from our case-study library — same AI/human split shown in full, industry context included.
What people ask before scoping this layer.
Is this on-device or cloud AI?
Both, chosen by the constraint — real-time and privacy-sensitive workloads run on-device; fleet-wide pattern detection (predictive maintenance) runs in the cloud.
Which frameworks do you use?
TensorFlow Lite and ONNX Runtime most often for edge inference, standard PyTorch/TensorFlow for training — chosen by target hardware, not by default preference.
What's the AI/human split in practice here?
AI accelerates model-integration code and data pipelines; a senior engineer owns model selection, accuracy/recall tradeoffs and false-positive management — the calls that actually determine whether the feature works in the field.
Can you add AI to a product we've already built?
Yes — this is one of our most common standalone engagements: adding predictive maintenance or on-device inference to hardware and firmware that already ships.
This layer rarely ships alone.
Embedded Hardware
Bare-metal to RTOS, matched to your power and timing budget.
Firmware
Boards designed for manufacturability, not just the bench.
Cloud & Device Platforms
The interface your customer actually touches, paired to the fleet underneath.
Mobile & Web Apps
Fleet-scale telemetry and APIs behind the screen.
What we've written about on-device AI, quantization and fleet MLOps.
On-Device vs. Cloud AI: Where Should a Model Actually Run?
Latency, privacy, connectivity and cost constraints that should decide this — not a default preference for one or the other.
Read the article →Model Quantization for Edge Deployment: INT8, FP16, and When It Matters
The real tradeoffs in shrinking a model to fit edge hardware — precision formats, accuracy loss, and when it's worth the complexity.
Read the article →MLOps for Fleet-Wide Models: Retraining, Versioning, and Rollout
How model updates get trained centrally and pushed to a device fleet safely, without silently degrading the whole fleet at once.
Read the article →Evaluating an AI-native engineering partner in India? See what to look for.