LLM Application Development
Custom assistants, copilots, and AI features built for production. Streaming, tool use, structured outputs, observability — all wired up correctly.
- Chat & copilot UX
- Tool use & function calling
- Structured outputs
We partner with teams to design, build, and ship reliable AI products — from RAG systems and AI agents to fine-tuned models and evaluation pipelines.
From the first prototype to the on-call rotation. We work across the entire lifecycle of an AI product.
Custom assistants, copilots, and AI features built for production. Streaming, tool use, structured outputs, observability — all wired up correctly.
Retrieval-augmented systems that ground answers in your data. Hybrid search, smart chunking, and re-ranking that holds up under real query distributions.
Autonomous and human-in-the-loop agents that take action across your tools. Designed with guardrails, recovery, and the right level of autonomy.
Eval-driven development. We build the test sets, scorers, and dashboards that let you ship changes with confidence and catch regressions early.
When prompting plateaus, we tune. Smaller, faster, cheaper models that match or beat frontier performance on your specific tasks.
The boring parts that make AI work in production: gateways, caching, fallbacks, cost controls, and CI/CD for prompts and models.
A few of the patterns we ship most often. Every engagement is shaped around the specifics of your business.
Agents that resolve tickets end-to-end — pulling from your knowledge base, taking actions in your CRM, and escalating gracefully when needed.
Q&A over policies, runbooks, and decks. Permissions-aware retrieval, citation-backed answers, and a chat surface your team will actually use.
Extraction, classification, and summarization at scale — for contracts, invoices, claims, research, and unstructured archives.
Lead enrichment, account research, personalized outbound, and content generation grounded in your brand voice and product reality.
Internal copilots for codebases, code review assistants, and automated runbook execution — built for the way your engineering org actually works.
Policy review, audit prep, and risk scoring — with eval suites that surface drift and an audit trail your legal team can defend.
AI projects fail in different ways than software projects. Our process is built for the things that actually go wrong: ambiguous specs, eval gaps, and quiet regressions.
We define the use case in concrete terms: what success looks like, what failure looks like, and which constraints matter — latency, cost, accuracy, privacy.
Architecture, model choice, and the eval plan up front. We pick the simplest design that can pass the bar — and write the tests before the code.
Prototype to production. Iteration is driven by eval scores, not vibes. Observability, guardrails, and cost controls land before launch — not after.
Models drift, distributions shift, prompts rot. We stay on for ongoing evals, model upgrades, and the small refinements that compound into big wins.
We're senior engineers and applied researchers who've shipped LLM products at scale. We don't chase trends — we ship things that work, measure them honestly, and stay on the hook when something breaks.
Every engagement is led by people who've built and operated AI systems in production.
We invest in measurement on day one so you can ship changes — and upgrades — without holding your breath.
Closed, open, fine-tuned, distilled — we pick the right model for the job, not the loudest one.
Code, weights, evals, prompts. No vendor lock-in, no black-box handoffs.
Tell us about it. We'll get back within one business day with a candid take and next steps.
contact@yiaistuido.com
www.yiaistuido.com