Most RAG implementations underperform because the chunking is wrong or the retrieval strategy is naive. We build pipelines tuned to your document types and query patterns. Chunk sizing, embedding model selection, metadata extraction, hybrid search, re-ranking, and query rewriting.

Point-to-point integrations carry recurring maintenance burdens, schema changes in one system break downstream consumers, and every new endpoint multiplies the regression surface. By refactoring these into API-led layers, our clients move from brittle, high-touch connections to reusable System APIs owned by the source team

AI tools might generate your boilerplate, but the companies writing the checks still want people who understand what the boilerplate actually does.

Most RAG implementations underperform because the chunking is wrong or the retrieval strategy is naive. We build pipelines tuned to your document types and query patterns. Chunk sizing, embedding model selection, metadata extraction, hybrid search, re-ranking, and query rewriting.

Point-to-point integrations carry recurring maintenance burdens, schema changes in one system break downstream consumers, and every new endpoint multiplies the regression surface. By refactoring these into API-led layers, our clients move from brittle, high-touch connections to reusable System APIs owned by the source team

Most RAG implementations underperform because the chunking is wrong or the retrieval strategy is naive. We build pipelines tuned to your document types and query patterns. Chunk sizing, embedding model selection, metadata extraction, hybrid search, re-ranking, and query rewriting.

Point-to-point integrations carry recurring maintenance burdens, schema changes in one system break downstream consumers, and every new endpoint multiplies the regression surface. By refactoring these into API-led layers, our clients move from brittle, high-touch connections to reusable System APIs owned by the source team

AI tools might generate your boilerplate, but the companies writing the checks still want people who understand what the boilerplate actually does.

Most RAG implementations underperform because the chunking is wrong or the retrieval strategy is naive. We build pipelines tuned to your document types and query patterns. Chunk sizing, embedding model selection, metadata extraction, hybrid search, re-ranking, and query rewriting.

Point-to-point integrations carry recurring maintenance burdens, schema changes in one system break downstream consumers, and every new endpoint multiplies the regression surface. By refactoring these into API-led layers, our clients move from brittle, high-touch connections to reusable System APIs owned by the source team

AI tools might generate your boilerplate, but the companies writing the checks still want people who understand what the boilerplate actually does.

Your Swing application, that old Jsp portal, the Struts monolith from 2008… it still works. It also scares off new hires, blocks every compliance audit, and can’t integrate with anything built after 2015. Replacing it from scratch would cost millions and take years. So it sits there.

Most RAG implementations underperform because the chunking is wrong or the retrieval strategy is naive. We build pipelines tuned to your document types and query patterns. Chunk sizing, embedding model selection, metadata extraction, hybrid search, re-ranking, and query rewriting.

Point-to-point integrations carry recurring maintenance burdens, schema changes in one system break downstream consumers, and every new endpoint multiplies the regression surface. By refactoring these into API-led layers, our clients move from brittle, high-touch connections to reusable System APIs owned by the source team

AI tools might generate your boilerplate, but the companies writing the checks still want people who understand what the boilerplate actually does.

Your Swing application, that old Jsp portal, the Struts monolith from 2008… it still works. It also scares off new hires, blocks every compliance audit, and can’t integrate with anything built after 2015. Replacing it from scratch would cost millions and take years. So it sits there.

Point-to-point integrations carry recurring maintenance burdens, schema changes in one system break downstream consumers, and every new endpoint multiplies the regression surface. By refactoring these into API-led layers, our clients move from brittle, high-touch connections to reusable System APIs owned by the source team

Most RAG implementations underperform because the chunking is wrong or the retrieval strategy is naive. We build pipelines tuned to your document types and query patterns. Chunk sizing, embedding model selection, metadata extraction, hybrid search, re-ranking, and query rewriting.

AI tools might generate your boilerplate, but the companies writing the checks still want people who understand what the boilerplate actually does.

Your Swing application, that old Jsp portal, the Struts monolith from 2008… it still works. It also scares off new hires, blocks every compliance audit, and can’t integrate with anything built after 2015. Replacing it from scratch would cost millions and take years. So it sits there.