PyTorch models inferring on-device — server cost eliminated.
We exported PyTorch models to Core ML·ONNX for on-device inference, removing server round-trips and their cost.
View case →● DATA & ML ENGINEERING
RAG and MLOps: from “it ran” to reliable production.
We own the entire ML lifecycle — from ingestion through deployment and operations.
100s
GPU orchestration
on-device
deployment
24/7
monitoring
● THE PROBLEM
If this sounds familiar.
If even one of these is familiar, this service closes that gap.
The notebook trap
The model works, but serving, scaling, and cost don't.
GPU waste
Expensive cards sit idle without scheduling.
Edge constraints
You need to move on-device, but conversion·optimization is daunting.
● SCOPE
What, how far, what we leave behind.
We lift models into production services, owning the ML lifecycle from ingestion to operations.
How far
- Data pipelines
- Model serving·deployment
- GPU orchestration·scheduling
- On-device·edge ML
- Monitoring·optimization
Deliverables
- Data·ML pipelines
- Deployed endpoints or on-device builds
- GPU operations framework
- Performance·cost report
● HOW WE WORK
How we work.
- PipelineData ingestion·cleaning·features
- ServingEndpoints or on-device builds
- SchedulingGPU allocation·autoscaling
- OperateMonitoring·drift·cost optimization
● EVIDENCE — NOT COPY
A record of what we shipped.
On-device ML · edge inference
on-device
inference
Core ML/ONNX
conversion
0$0
edge server cost
● RELATED INSIGHTS
Go deeper from assessment to cutover.
Change the model or fix the prompt? An LLM selection evaluation on 200 production recordsEngineering deep dives · August 8, 2026We compared six configurations built from three models and three prompt stages on 200 production records without labeled ground truth. A model change added 14 percentage points of proxy agreement; a prompt revision added 37. The selected configuration costs $7.20 per month.→
Is private connectivity enough for LLM APIs? — Four boundaries from Bedrock to self-hostingRegulation & compliance · August 2, 2026A comparison of direct OpenAI and Anthropic APIs, Amazon Bedrock, Microsoft Foundry, Google Cloud, VPC deployments, and on-premises hosting across network path, processing location, retention, and legal basis.→
How far should AI content automation go? — Moving high-volume tagging and translation into productionAX cases & methodology · July 24, 2026How we validated manual content tagging and translation in a four-week PoC, then moved it into production at tens of thousands of items per month.→
How do we evaluate RAG? A regression suite built from 30 questions and 8 follow-up setsEngineering deep dives · July 17, 2026We used 30 questions and 8 follow-up sets to isolate retrieval, grounded generation, and conversation context, then turn the results into RAG release gates.→
Should you chase an agent-readiness score? From Level 1 to Agent-NativeEngineering deep dives · Updated July 16, 2026isitagentready.com scored our site 29, Level 1. Instead of filling the scorecard, we filtered by substance: Content Signals, markdown negotiation, a real MCP server, DNS-AID and DNSSEC — reaching 71, Level 5 (Agent-Native). And a hard look at what agent scores actually measure.→
Did fully-managed RAG beat the pipeline we built?Engineering deep dives · Updated July 16, 2026We pit a hand-built pgvector pipeline against Amazon Bedrock's fully-managed Knowledge Base for a school-health advisory chatbot. On Korean table documents, what decided it?→
Got models but no ops? We start there.
Request a technical review →● STACK & DOMAIN
Stack & domain.
PyTorchONNXCore MLTritonKueueSageMakerRay
A team that has handled large-scale GPU operations and edge constraints at the same time.
● FAQ
Frequently asked.
We have the model — can you just deploy it?+
Yes. Serving·scheduling is our most common standalone request.
Can you take it from data collection?+
Yes. We own the whole ML lifecycle — from ingestion, cleaning, and features through serving and operations.
What if quality drifts after deployment?+
We add monitoring and drift detection to track performance and cost, and pin down when to retrain.
Does on-device really cut cost?+
The more inference traffic, the more server cost disappears — the bigger the win.
Our GPU cost is out of control.+
Scheduling, autoscaling, and visibility raise utilization and throughput per card.
Take your model to production.
We review the environment and challenge first, then define the right execution scope together.
● Already trusted by teams across finance · healthcare · media · public