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From model selection and retrieval design to agents, evaluation and the platform underneath — plus the automation, cloud and product engineering that turns it into something your business can run on.
The full AI stack, not just the API call
Anyone can wire up a chat completion. The distance between that and a system your business can depend on is six layers deep — and we work at every one of them.
Models & Fine-Tuning
Choosing, adapting and evaluating foundation models — from prompt-level engineering through LoRA/QLoRA fine-tuning to distillation for cost and latency.
- Model selection & benchmarking
- LoRA / QLoRA fine-tuning
- Distillation & quantisation
- Structured output & function schemas
What we build with
Model and tool choices follow the problem. We are not tied to a single provider, and the systems we build are designed so that swapping one is a configuration change.
Foundation Models
- Claude
- GPT
- Gemini
- Llama
- Mistral
- DeepSeek
- Qwen
Frameworks & Orchestration
- LangGraph
- LangChain
- LlamaIndex
- Agent SDKs
- MCP
- DSPy
- Temporal
Vector & Search
- pgvector
- Pinecone
- Qdrant
- Weaviate
- OpenSearch
- Elasticsearch
Training & Serving
- PyTorch
- Hugging Face
- vLLM
- Ollama
- SageMaker
- Vertex AI
- Bedrock
Data & Compute
- DuckDB
- BigQuery
- Snowflake
- Airflow
- dbt
- Kafka
- Spark
Evaluation & Observability
- Ragas
- LangSmith
- Langfuse
- Weights & Biases
- OpenTelemetry
Where the AI work actually happens
Eight practices covering the problems enterprises bring us most often. Each one is a service we deliver end to end, not a capability we outsource.
We run this stack on our own products
Seven live products built and operated by Brewcode. When we recommend an approach, it is one we already pay the inference bill for.
The engineering that carries it
AI rarely ships alone. These are the services that surround it — the automation, cloud foundations, applications and assurance that decide whether an AI feature survives contact with production.
Not sure which of these you actually need?
Most engagements start with a conversation about the problem rather than the technology. Tell us what is not working and we will tell you honestly whether AI is the right answer.