Ship production-grade AI systems
German-Filipino engineering teams building scalable LLM apps and data pipelines.
Start your buildCustom AI Engineering
We build production-grade models tailored to your specific data constraints. Our team handles the full stack from data ingestion to model deployment, ensuring low-latency inference in live environments.
- Develop custom LLMs using PyTorch and TensorFlow
- Implement RAG pipelines for enterprise knowledge bases
- Optimize inference latency via model quantization
We focus on shipping scalable backends that integrate directly with your existing microservices. No pre-packaged black boxes; every component is auditable and version-controlled for your engineering team.
Core Engineering Capabilities
Custom Model Development
We train and fine-tune transformer architectures on your proprietary datasets to solve domain-specific problems. Our pipeline handles data cleaning, tokenization, and distributed training across multi-GPU clusters without relying on black-box APIs.
LLM Integration & Orchestration
We build deterministic retrieval-augmented generation (RAG) systems that connect LLMs to your existing SQL and NoSQL databases. The stack includes prompt versioning, latency optimization, and fallback mechanisms for production reliability.
MLOps Infrastructure
We containerize model serving using Kubernetes and implement CI/CD pipelines for automated retraining and A/B testing. This approach ensures reproducible builds and seamless scaling from prototype to enterprise load.
Data Engineering for AI
We construct robust ETL pipelines that transform raw logs and unstructured text into high-quality training corpora. Our focus is on schema evolution, data lineage tracking, and maintaining strict type safety throughout the feature store.
End-to-End AI Integration
We deploy production-grade pipelines that ingest your raw telemetry and output deterministic inference. Our stack prioritizes low-latency serving over experimental notebooks, ensuring models run efficiently on your existing infrastructure.
We replace abstract POCs with containerized microservices ready for Kubernetes orchestration. This approach eliminates dependency hell and guarantees reproducible builds across dev and prod environments.
- Implement custom RAG architectures for private data retrieval
- Optimize transformer models via quantization and distillation
- Build automated MLOps workflows for continuous retraining
Model Optimization & Deployment
We refactor research prototypes into low-latency inference engines. Our team quantizes large language models to run on edge hardware without sacrificing accuracy, ensuring your stack scales predictably under load.
- Convert PyTorch checkpoints to ONNX runtimes for cross-platform compatibility
- Implement custom kernel fusion to reduce memory bandwidth bottlenecks
- Design deterministic data loaders for consistent training reproducibility
We bypass abstract benchmarks to focus on your specific throughput constraints. The result is a deployable artifact, not a proof of concept.
Production Deployment Workflow
Data Ingestion & Validation
We connect directly to your existing telemetry streams and enforce strict schema validation at the ingress point. This prevents dirty data from corrupting downstream training pipelines.
Model Architecture Selection
Our engineers select transformer variants or lightweight CNNs based on your specific latency and memory constraints. We prioritize architectures that fit within your current cloud compute budget.
Distributed Training Setup
We configure multi-GPU clusters using NCCL backends to parallelize gradient updates across large datasets. Checkpointing logic ensures training resumes instantly after hardware preemption.
Quantization & Compilation
We apply INT8 quantization and compile models with TensorRT or ONNX Runtime to reduce inference footprint. This step cuts VRAM usage by 40% while maintaining F1 scores within 2% of the baseline.
CI/CD Pipeline Integration
We embed model versioning into your existing GitHub Actions or GitLab CI workflows for automated regression testing. Every commit triggers a shadow deployment to validate output consistency against production traffic.
Scalable AI Infrastructure
We architect distributed training clusters that handle petabyte-scale datasets without bottlenecks. Our stack enforces strict type safety across Python and C++ boundaries to prevent runtime drift in production.
- Implement model sharding for multi-GPU nodes
- Build idempotent data pipelines with exactly-once semantics
- Containerize inference engines for Kubernetes orchestration
We replace research notebooks with modular codebases ready for CI/CD integration. This ensures your AI assets remain maintainable as team velocity scales.