Technology capabilities
Built on modern engineering foundations.
We choose technology per problem, not per trend. These are the foundations our engineers work with most, organized by the layer of the system they serve.
Stack
Technology by category
AI and machine learning
Model development, training, and classical ML.
- Python
- PyTorch
- TensorFlow
- OpenCV
- Hugging Face
Generative AI
LLM applications, orchestration, and retrieval.
- OpenAI
- Anthropic
- Gemini
- LangChain
- LangGraph
- RAG architectures
Data and infrastructure
Storage, vector search, and deployment.
- PostgreSQL
- Qdrant
- Pinecone
- Docker
- Cloud platforms
- APIs and microservices
Computer vision and edge
Visual models and on device inference.
- NVIDIA Jetson
- MediaPipe
- YOLO based vision systems
- OpenPose
- Edge inference
Application engineering
Interfaces, services, and APIs.
- React
- Next.js
- Node.js
- FastAPI
Engineering practice
How we apply it
- Model selection
- We benchmark candidate models on your data for quality, latency, cost, and data residency before choosing.
- Retrieval and data
- Hybrid search, reranking, and permission aware indexing over PostgreSQL, Qdrant, or Pinecone.
- Orchestration
- LangChain and LangGraph for explicit, inspectable agent graphs with human approval steps.
- Evaluation
- Automated and human scored test sets integrated into continuous integration.
- Deployment
- Containerized services with Docker on cloud platforms or on premises, plus edge deployment on NVIDIA Jetson.
- Observability
- Tracing of model and tool calls, cost tracking, and drift monitoring in production.
Have an architecture question?
Talk to our engineers about the right stack for your constraints.