AI & ML engineering

What we build

• RAG and retrieval — answer questions over your own documents and data, with citations and evaluation, not hallucinations
• LLM apps and agents — wired to real tools and APIs, with guardrails, evals, and cost controls
• Embeddings and semantic search — vector indexes tuned for recall and latency, not a toy demo
• Applied ML on large datasets — classification, ranking, extraction, and forecasting where the data actually warrants it
• Data pipelines and feature stores that turn messy big data into something models can learn from
• MLOps: versioning, monitoring, and retraining so models don’t quietly rot — served on AWS SageMaker and Google Vertex AI, GPU or serverless, cost-aware

Big data and content at scale

We build systems that ingest, enrich, embed, and serve large volumes of content and events. The hard part is rarely the model — it’s the pipeline, the latency budget, and the bill. We design for all three, and we measure what ships.

Honest about scope

We won’t bolt an LLM onto a problem that doesn’t need one. If a smaller model, a retrieval layer, a rules engine, or simply better data solves it cheaper, we’ll tell you. We’re a small engineer-led shop — we take on AI work we can deliver and stand behind, and we’re straight about where that line is.