AI Pipeline

Everyone talks about the model. Almost nobody talks about what goes into it.

The quality of an AI output — whether it’s a chatbot answer, a generated report, a recommendation, a classification — is determined almost entirely by what data the model had access to, how it was prepared, and how it was retrieved. A well-prompted frontier model fed stale, incomplete, or poorly chunked data will give worse answers than a smaller model fed clean, current, well-structured context. We design and build the pipelines that feed the model.

RAG pipeline design

Retrieval Augmented Generation is the most common pattern for using internal data with a model. Done well, RAG gives you accurate, grounded, up-to-date answers from a model that knows your data. Done badly — which is how most RAG implementations are done — it gives you answers that sound confident and are wrong.

The difference is almost always in the pipeline: how documents are chunked, how embeddings are generated and stored, how retrieval is ranked and filtered, how context is assembled before the prompt, and how staleness is managed as your underlying data changes. We design the full retrieval pipeline, not just the “call the API” part.

Vector store design and ingestion

Every RAG implementation needs a vector store — a database that stores embeddings and supports similarity search. Choosing the right one (pgvector, Pinecone, Weaviate, OpenSearch, Bedrock Knowledge Bases) depends on your data volumes, latency requirements, and existing infrastructure. We design the embedding pipeline: which model generates the embeddings, how documents are pre-processed, how metadata is stored for filtered retrieval, and how the index stays current.

Streaming vs batch

Some AI pipelines need data in real time — a customer support tool that needs to know about an order from ten minutes ago. Others can tolerate batch — an internal knowledge base updated nightly is fine. Getting this wrong is expensive in either direction. We assess the latency requirements for each use case and design accordingly.

Context management

The context window is finite. What you put in it matters. We design context assembly logic that prioritises the most relevant retrieved chunks, trims intelligently, and keeps system instructions and retrieved context in the right balance — including re-ranking, conflict handling across sources, and conversation history management in multi-turn applications.

Integration with your data platform

An AI pipeline isn’t separate from your data platform — it’s an additional consumer of it. We design AI pipelines that sit on top of your governed data, consuming from the Gold layer where data is clean, curated, and trustworthy. That means the AI pipeline inherits your data quality, your access controls, and your freshness guarantees. Not a separate silo pulling from raw sources.