LLM & Generative AI · San Francisco, California
LLM development, grounded in your data.
We build custom LLM applications — RAG pipelines, AI agents, and fine-tuned models — with the evals and guardrails to run them in production without surprises.
What it means
Generative AI that is fast, grounded, and cheap to run.
Most LLM demos fall apart in production: they hallucinate, they cost too much, and nobody can tell when they regress. We build generative AI the other way — grounded in your data with retrieval, bounded by guardrails, and measured by evals so you know when a change makes it better or worse.
We build LLM products ourselves: CodeMouse (consensus AI code review on every pull request), Forever (persistent memory for Claude), and Promptside (local prompt management). That experience goes straight into your engagement.
Need it wired into your existing stack? That is our AI integration work; need it operated long-term? That is MLOps.
Scope
What LLM development covers.
RAG Pipelines
Retrieval-augmented generation grounded in your documents and data, with citations and freshness.
AI Agents
Tool-using agents that take real actions, with the guardrails and traces to run them safely.
Fine-tuning
Fine-tuned and adapted models when prompting is not enough — on the data that makes you different.
Evals & Guardrails
Evaluation harnesses and guardrails so quality is measured, not guessed, and regressions get caught.
Context Engineering
Prompt and context design that gets the most out of frontier models like Claude, reliably.
Cost Optimization
Routing, caching, and model selection that cut token spend without giving up quality.
How we work
Three steps, no theatre.
Call
A short scoping call. You describe the problem and constraints; we tell you honestly whether and how AI helps — and whether we are the right team.
Scope
A concrete plan: what we build, how we measure it, the timeline, and the path to production. No open-ended retainers dressed up as strategy.
Ship
We build, evaluate, and deploy — then hand over a running system with the monitoring and docs to operate it. We can keep running it if you want us to.
FAQ
Questions, answered.
What does LLM development involve?
It is building applications on top of large language models — RAG pipelines, agents, and fine-tuned models — plus the evals, guardrails, and infrastructure to run them reliably and cost-effectively in production.
Do you build RAG pipelines and AI agents?
Yes. Retrieval-augmented generation and tool-using agents are core to what we build, including the retrieval, evaluation, and guardrail layers that make them production-ready.
Which models do you work with?
We work with the leading frontier models, including the Claude family, and choose per use case based on quality, latency, and cost rather than loyalty to one vendor.
Should we fine-tune or use RAG?
Usually RAG first — it is cheaper and easier to keep current — with fine-tuning reserved for when you need behaviour or format that prompting and retrieval cannot reach. We help you decide.
How do we get started?
Email info@squidcode.com with what you want to build. We will reply with next steps and set up a scoping call.
More services
Related AI consulting services.
Ready to build with generative AI?
Tell us what you are building. You will hear back from an engineer, not a funnel.
info@squidcode.com