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 VECTORSProduction 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 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.
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
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-Framework01
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.
[RELATED]
Worth a look next.
APPLIED K-FRAMEWORKBring the problem.
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.