ML in production is a different problem than ML in a notebook.

We build and own the AI infrastructure layer — RAG pipelines, vector databases, fine-tuning, inference infrastructure, and observability. Built for production from day one, not retrofitted after the fact.

Talk to our engineering team

The problem

The gap between prototype and production

Most teams can get a model working locally. Getting it to production — low latency, cost-controlled, monitored, reliable under real traffic, integrated with the data pipeline — is a completely different problem.

The failures we see most often:

RAG systems that hallucinate because retrieval wasn't tuned properly
Inference costs that spiral 10× as usage grows
Latency that breaks the user experience at scale
No observability — you don't know the model is degrading until customers complain
Data pipelines that break when the input format changes slightly

These aren't edge cases. They're predictable failures that happen when production AI infrastructure is treated as an afterthought.

We build it right from the start.

What we build

What we ship

RAG pipelines

Chunking strategy, embedding model selection, vector store setup, retrieval tuning, reranking. Built to return accurate, relevant results — not just semantically similar ones.

Vector databases

Setup, indexing, and optimisation for Pinecone, Qdrant, Weaviate, and pgvector. We select the right one for your scale, latency requirements, and cost profile.

Fine-tuning pipelines

Data preparation, training runs, evaluation frameworks, model versioning. We build the pipeline, not just the one-off fine-tune.

Inference infrastructure

AWS SageMaker endpoints, autoscaling, latency optimisation, cost controls. Built to handle real production traffic with monitoring from day one.

LLM observability

Prompt logging, output evaluation, drift detection, cost tracking. You know what the model is doing and when it's going wrong before your users do.

Data pipelines for AI

ETL pipelines that feed your AI systems with clean, structured data. Feature stores, training data management, real-time inference data flows.

Proof

What we've shipped

Funded Medical EdTech · California

ML recommendation engine

Full ML infrastructure on AWS SageMaker — 14 weeks to production

The situation: Platform needed an ML recommendation system to surface relevant educational content for physicians. Required integration with an existing video library and user behaviour data.
What we built: Full ML infrastructure on AWS SageMaker — data pipeline, training pipeline, model serving, and integration with the existing platform.
The result: Recommendation engine in production in 14 weeks. 4× overall platform performance improvement. The system continues to improve as it accumulates user interaction data.

Have a model that works in a notebook?

We'll take it to production.

Free 45-minute technical review. We'll identify where the production risk is. No charge, no pitch at the end.

Book a technical review