--- title: "Embeddings & Semantic Search: Kensink Labs" description: "Embedding pipelines from a senior lab: model selection, chunking, vector storage, eval-tuned for RAG and search. Full source ownership." source: "https://www.kensink.com/models/embeddings/" canonical: "https://www.kensink.com/models/embeddings/" --- ★ Embeddings LLM Models 8-week engagement EMBEDDINGS · SEMANTIC VECTORS # Production embedding pipelines. Model selection, chunking, vector storage. Embeddings turn text and other data into vectors you can search by meaning. They power RAG, semantic search, clustering, and recommendations. The model choice and chunking strategy decide the quality. Vector store pgvector LLM API [Start a conversation →](https://www.kensink.com/contact) [All llm models →](https://www.kensink.com/models) Cycle 8 weeks · fixed price Stack Embeddings + pgvector Output Production code + eval suite Handoff Full source ownership \[THE SHORT VERSION\] ## Most RAG quality problems are embedding problems. Embeddings are the unglamorous core of retrieval. Which model you use, how you chunk content, and how you store and query the vectors matter more to RAG quality than the generation model people obsess over. We treat embedding and chunking as a tuned, evaluated part of the system. When it fits - RAG and semantic search - Clustering, deduplication, and recommendations - Any feature that matches by meaning, not keywords \[HOW WE BUILD IT\] ## How we build with Embeddings. 01 ### Scope and fit We decide where Embeddings earns its place in your system, and where a simpler tool wins. No resume-driven architecture. 02 ### Build on a tested foundation We integrate Embeddings against a foundation we trust: typed code, CI, and observability from the first commit. Boring infrastructure, modern surface. 03 ### Eval before launch An eval suite proves the build behaves before it reaches a user. We measure, then ship. 04 ### Handoff with ownership Your team gets the code, the tests, and a runbook. No lock-in to us or to a vendor framework. \[ WHAT YOU GET \] ## What the engagement leaves behind. Senior Engineers who have shipped this before 100% Source ownership at handoff Eval-first Tested before it ships 0 Framework lock-in \[METHODOLOGY · K-FRAMEWORK\] ## Integrated through the K-Framework. Every model we integrate runs through the same operating system. Three pillars, sixteen layers, one Compound Growth Loop. The methodology that keeps AI work from rotting after the first ship. [Read the K-Framework](https://www.kensink.com/k-framework) 01 ### Foundations Direct API integration with the model. No LangChain, no orchestration vendor, no agent framework built on quicksand. Typed contracts, the same way we wire up Postgres. 02 ### Amplification An eval suite built from your real tasks gates every prompt and model change. Quality is measured before it ships, not vibed in a demo. 03 ### Judgment Governance, audit, and oversight wired in from day one. Who called what, with which prompt version, at what cost. Your auditors get answers, not screenshots. \[OBSERVABILITY\] ## Observability your team can read. A model in production without observability is roulette. We instrument every integration so engineering and finance can see the same numbers, and so a regression at 3am surfaces before a customer opens a ticket. Instrumented ### Cost per call Tokens in, tokens out, dollars spent. Sliced by feature, tenant, and route. Budgets enforced where it matters. Instrumented ### Latency p50 / p95 / p99 Real distributions, not averages. We know which routes are slow, and why. Instrumented ### Eval pass rates The same eval suite that gates a release runs continuously in production. A regression on real traffic surfaces fast. Instrumented ### Prompt + completion logs PII scrubbed at the proxy, shipped to your SIEM. Retention controls match your compliance window. Dashboards your team owns, not ours. At handoff you get the queries, the alerts, and the runbook. We are not in the path to read your metrics. Share[](https://twitter.com/intent/tweet?url=https%3A%2F%2Fwww.kensink.com%2Fmodels%2Fembeddings%2F&text=Embeddings%20%C2%B7%20LLM%20Models)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fwww.kensink.com%2Fmodels%2Fembeddings%2F) [View .md](https://www.kensink.com/models/embeddings.md) \[RELATED\] ## Worth a look next. [ Design Patterns RAG Read more ](https://www.kensink.com/llm/rag)[ Technologies pgvector Read more ](https://www.kensink.com/technologies/pgvector)[ LLM Models Claude Read more ](https://www.kensink.com/models/claude) APPLIED K-FRAMEWORK ## Bring the problem. We’ll bring the build. Senior engineers, eval suite at handoff, full source ownership. Sprint, program, or ongoing. We shape the engagement to the work. [Start a conversation →](https://www.kensink.com/contact) [Read the K-Framework](https://www.kensink.com/k-framework)