AI & LLM stack
The right model is only part of the system.
How we choose
Model-agnostic, by design.
- 01
Task fit
Does it solve this specific task at the quality the business needs?
- 02
Data boundaries
Where can the data go, and what must stay in your environment?
- 03
Latency & cost
Is it fast and affordable enough at the volume you expect?
- 04
Operability
Can your team run, monitor and replace it over time?
The stack
What we build with.
Language models
We choose the model for each task by comparing quality, latency, cost and data requirements.
- OpenAI models
- Anthropic Claude
- Google Gemini
- Open-source modelsLlama, Mistral, Qwen and others, self-hosted when data must stay in-house
AI infrastructure
Orchestration, retrieval and evaluation, the parts that turn a model into a system.
- Python
- FastAPI
- LangGraphGraph-based agent orchestration
- Agent frameworksSelected per project; custom orchestration when simpler
- Vector databases
- Embeddings & re-ranking
- Evaluation & tracing
ML & computer vision
Training, fine-tuning and deploying models that work outside the lab.
- PyTorch
- OpenCV
- Detection & segmentation models
- Multimodal models
Application stack
Fast, accessible interfaces and typed services around the AI.
- Next.js
- React
- TypeScript
- Node.js
Cloud & delivery
Reproducible infrastructure that is straightforward to operate.
- AWS
- Docker
- Kubernetes
- CI/CD & infrastructure as code
Data
Reliable storage, caching and retrieval for operational and AI workloads.
- PostgreSQL
- Redis
- Vector databasesIncluding pgvector where it keeps the stack simpler
- Data pipelines
In practice
A retrieval pipeline, end to end.
- 01Documents
- 02Ingestion
- 03Chunking
- 04Embeddings
- 05Vector search
- 06Retrieval
- 07LLM
- 08Grounded answer
Idea → Architecture → Prototype → Production → Scale
Have an idea to build, or an agent to test?
Tell us what you're trying to build, test, automate or improve. We'll tell you honestly where we can make a real difference, and where we can't.