01
Scope and fit
We decide where Embeddings earns its place in your system, and where a simpler tool wins. No resume-driven architecture.
★ EmbeddingsLLM Models8-week engagement
EMBEDDINGS · SEMANTIC VECTORSEmbeddings, the quiet engine of retrieval.
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 storepgvectorLLM API
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.
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.
8 wks
Problem to production
100%
Source ownership at handoff
Eval-first
Tested before it ships
0
Framework lock-in
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APPLIED K-FRAMEWORKBring the problem.
Bring the problem.
We’ll bring the build.
Eight weeks, fixed price, eval suite at handoff. Senior engineers, full source ownership, no framework lock-in.