Data & AI Engineering
The engineering discipline behind the model, not just the demo.
Anyone can wire an API to a chatbot. Production AI systems need retrieval that doesn't hallucinate, pipelines that don't silently drop data, and evaluations that catch regressions.
What we do
The data & ai engineering work your roadmap needs.
- LLM applications
- RAG
- Retrieval systems
- AI agents
- Agentic workflows
- Model evaluation
- Model deployment
- Data pipelines
- Analytics infrastructure
- Guardrails
- Monitoring
- AI observability
Production AI system
Context → Retrieval → Model → Evaluation → Human decision
Models are one part of the system. Reliable data, constrained retrieval, measurable evaluations, observability, and engineering ownership make it production-ready.
01
Context
02
Retrieval
03
Model
04
Evaluation
05
Human decision
Technologies
Tools chosen for the system, not the sales deck.
- Python
- LangChain
- PyTorch
- TensorFlow
- Keras
- Scikit-learn
- Hugging Face
- Anthropic
- Google Gemini
- Pandas
- NumPy
- Jupyter
- Snowflake
- Databricks
- Apache Spark
- Airflow
- BigQuery
Start a conversation
Build with senior data & ai engineering talent.
Tell us what you need to ship. We'll tell you honestly whether we're the right fit.