AI 应用工程。从基础模型,到生产系统。
Foundation models are a new kind of raw material.
Most teams treat them like APIs — send a prompt, collect a response, ship a feature. That works for demos. It breaks at production.
Between a foundation model and a reliable application lies everything that matters: context engineering, retrieval architecture, evaluation, tool orchestration, failure modes, latency budgets, cost control.
UEIC is the team that builds those layers.
Foundation models have entered the same phase cloud infrastructure entered around 2010: the primitives are public, but the engineering that turns primitives into reliable systems is still rare, expensive, and mostly tacit.
This is the window where the application layer gets built —
not by the model labs, who are focused on the frontier,
and not by prompt-engineering startups, who do not own the stack.
We are positioned in this window.
Working directly with frontier foundation models — understanding their capabilities, failure modes, and the operating envelope at scale. We select, route, and combine models based on what the application actually needs — not based on brand preference or benchmark scores.
The layer where foundation models become products. Context engineering, retrieval systems, evaluation pipelines, tool orchestration, agentic workflows — the engineering that determines whether an AI feature survives contact with real users.
What runs underneath: inference optimization, cost accounting, observability, deployment patterns, the guardrails and telemetry that turn a prototype into a system your organization can depend on.
Each layer compounds the next. We build all three.
Every decision passes through three filters:
What would we build if the field did not yet exist?
Depend on foundations we can inspect and replace — and be clear about what we cannot own.
Optimize for the system's shape in five years, not five weeks.
Not scheduled, not marketing, not for SEO.
If you are building AI applications at production scale and want to compare notes, write to us.