service · AI
AI & vibecoding development
LLM-driven product features and AI-assisted (vibecoding) delivery — put to real, safe work.
AI-driven product features and AI-assisted (vibecoding) delivery — LLMs put to real, safe work. (Agentic operations are their own offering below.)
- Claude / LLMs
- MCP
- pgvector / RAG
- Python
overview
What it is
Large language models are useful inside a product when they are scoped, grounded, and supervised: a feature that answers from your own documents, extracts structure from messy text, drafts something a person approves. We build those features with Claude and other LLMs, grounding them in your data with RAG (retrieval over pgvector) and keeping a human in the loop wherever the output matters.
We also work AI-assisted ourselves — vibecoding — with the model accelerating delivery while a human engineer stays in control of every decision. This offering is about product features and the way we build them; standing, multi-step agents that run an operation are their own offering, AI agents for operations.
what's included
Capabilities
LLM integration & AI features
Claude and other LLMs built into your product as concrete features — chat, drafting, classification, extraction — rather than a bolt-on demo.
AI-assisted delivery (vibecoding)
We build with AI in the loop to move fast, while a human engineer stays in control of every decision and review.
Document & data intelligence (RAG)
Retrieval-augmented generation over your own documents and data with pgvector, so answers are grounded in your reality and cite their source.
Human-in-the-loop safety rails
Review and approval steps wherever output matters, so a person stays in control of anything the model produces that has consequences.
Prompt & context engineering
The prompts, context, and tool wiring designed and tested so the feature behaves predictably instead of improvising.
Tooling via MCP
LLM features connected to your data and systems through typed tools and MCP, so the model works with your real context.
why it matters
Benefits
- LLM features that are scoped and grounded, instead of an open-ended chatbot that guesses.
- Answers grounded in your own data with citations, so they reflect your reality.
- A human stays in control wherever output matters, keeping the feature safe to ship.
- AI-assisted delivery lets us move quickly without giving up engineering judgement.
in practice
Use cases
how we work
The engagement
Discovery
We pick a concrete feature where an LLM genuinely helps, with clear inputs and a clear definition of good.
Grounding & design
We design the prompts, context, and RAG grounding so the feature reasons over your data, not generic knowledge.
Build with safety rails
We build the feature with human-in-the-loop review wherever the output has consequences.
Ship & iterate
We deploy, watch how it behaves on real inputs, and refine prompts and grounding from there.
get in touch
Let's talk
Have an operation you'd like an agent to run, or want to see how AI & vibecoding development could fit your work? Tell us about it and we'll come back with a tailored plan.