Data & AI

AI features that hold up outside the demo.

We build the unglamorous parts that make AI products work: retrieval quality, evaluation harnesses, guardrails, cost control, and inference infrastructure that does not fall over on launch day.

Compose a team All disciplines

What we build

Inside ML & AI Engineering.

LLM product features

Assistants, extraction, summarisation and agents wired into real workflows.

Retrieval (RAG)

Chunking strategy, hybrid search, reranking, and citation-grade grounding.

Evaluation

Golden sets, offline evals, online A/B, and regression gates in CI.

Classical ML

Forecasting, ranking, churn, fraud and pricing models with monitored drift.

MLOps

Feature stores, model registries, reproducible training, and safe rollouts.

Inference infrastructure

GPU capacity planning, batching, caching, and token cost governance.

The stack

Tools we actually use in production.

Modelling

PyTorch
scikit-learn
XGBoost
Hugging Face
LightGBM

LLM tooling

OpenAI
Anthropic
Gemini
LangGraph
LlamaIndex
vLLM

Retrieval

pgvector
Pinecone
Qdrant
OpenSearch
Cohere Rerank

Ops & evals

MLflow
Weights & Biases
Ragas
LangSmith
Kubeflow
Airflow

Comparison

How to make a model fit your domain

Most teams reach for fine-tuning far too early. We escalate only when the cheaper option measurably fails.

Option	Strength	Trade-off	Choose it when
Prompting	Immediate, cheap, easy to iterate and revert.	Context limits; behaviour drifts when the model changes.	First attempt at nearly every use case.
RAG	Grounds answers in your data with citations and freshness.	Retrieval quality becomes the product; adds infrastructure.	Knowledge-heavy assistants and support automation.
Fine-tuning	Locks in tone, format and narrow-task accuracy.	Needs labelled data and repeats with every base-model upgrade.	Stable, high-volume tasks where output format matters.
Self-hosted open models	Data residency, predictable unit cost at scale.	GPU operations and evaluation burden move to you.	Regulated data or very high sustained volume.