AI system assessment
We review what you've already shipped (architecture, evaluation gaps, where it holds up, where the seams are showing) and tell you what we'd change.
The gap between an AI feature that works in a demo and one that holds up for thousands of real users is wider than most teams expect. Closing it is engineering work: architecture, evaluation, and careful tradeoffs between what the model handles and what traditional code handles. We work with teams who've moved past “can we use an LLM here” and are facing the harder question: “how do we ship this so it actually holds up?”
What we build
Most of what we build sits on top of hybrid system architectures: probabilistic LLMs paired with deterministic code, each doing what it's best at. Models handle natural language and reasoning; traditional code handles precise operations, state, and the parts where you can't afford a hallucination.
On top of that we build retrieval pipelines that surface the right context, autonomous agents with bounded responsibilities, MCP integrations, and short- and long-term memory layers tuned to your product's actual usage patterns. We replace rigid multi-step forms and workflows with conversational interfaces, built on modular prompt engineering, separating extraction, question generation, and validation so every part is testable and tunable.
And underneath all of it: evaluation pipelines. The systematic frameworks that let you catch regressions, measure improvements, and tell whether a prompt change actually helped or just felt better. The bridge between “works in demo” and “works in production.”
How we navigate the tradeoffs
Production AI is a four-way tradeoff between cost, latency, security, and quality. We help teams find the right point on that curve for their use case, and re-find it when models, prices, or requirements shift. This is the work we spend most of our time on, and it's where most “we built it ourselves” projects struggle.
What we don't pretend
We won't tell you AI can do something it can't. We won't ship a feature without a way to measure whether it's working. And we won't treat your production users as the test set.
How we plug in
Strategy workshops
Working sessions with your product and engineering teams to map where AI fits, where it doesn't, and the right architectural shape.
Experimentation cycles
Short build-and-evaluate loops where every change ships behind evaluation infrastructure, so we know what's working before it reaches your users.
Recent thinking on AI engineering
7 min read
Artificial Intelligence
AI Engineering Explained: Building the Future of Software
An overview of AI Engineering as a discipline, covering foundation model integration, tradeoffs in AI systems, evaluation pipelines, and emerging architectural patterns.
Jorge BejarMay 12, 2025
3 min read
Artificial Intelligence
Embrace Uncertainty to Ship Better AI
AI-powered features introduce a new kind of uncertainty — not about when we'll ship, but about what the AI can actually achieve. Here's how we handle it.
Jorge BejarOct 27, 2025
Let's Build Together.
Ready to ship AI features that hold up past the demo and under real production load?
The gap between an AI feature that works in a demo and one that holds up for thousands of real users is wider than most teams expect. Closing it is engineering work: architecture, evaluation, and careful tradeoffs between what the model handles and what traditional code handles. We work with teams who've moved past “can we use an LLM here” and are facing the harder question: “how do we ship this so it actually holds up?”
What we build
Most of what we build sits on top of hybrid system architectures: probabilistic LLMs paired with deterministic code, each doing what it's best at. Models handle natural language and reasoning; traditional code handles precise operations, state, and the parts where you can't afford a hallucination.
On top of that we build retrieval pipelines that surface the right context, autonomous agents with bounded responsibilities, MCP integrations, and short- and long-term memory layers tuned to your product's actual usage patterns. We replace rigid multi-step forms and workflows with conversational interfaces, built on modular prompt engineering, separating extraction, question generation, and validation so every part is testable and tunable.
And underneath all of it: evaluation pipelines. The systematic frameworks that let you catch regressions, measure improvements, and tell whether a prompt change actually helped or just felt better. The bridge between “works in demo” and “works in production.”
How we navigate the tradeoffs
Production AI is a four-way tradeoff between cost, latency, security, and quality. We help teams find the right point on that curve for their use case, and re-find it when models, prices, or requirements shift. This is the work we spend most of our time on, and it's where most “we built it ourselves” projects struggle.
What we don't pretend
We won't tell you AI can do something it can't. We won't ship a feature without a way to measure whether it's working. And we won't treat your production users as the test set.
How we plug in
AI system assessment
We review what you've already shipped (architecture, evaluation gaps, where it holds up, where the seams are showing) and tell you what we'd change.
Strategy workshops
Working sessions with your product and engineering teams to map where AI fits, where it doesn't, and the right architectural shape.
Experimentation cycles
Short build-and-evaluate loops where every change ships behind evaluation infrastructure, so we know what's working before it reaches your users.
Recent thinking on AI engineering
7 min read
Artificial Intelligence
AI Engineering Explained: Building the Future of Software
An overview of AI Engineering as a discipline, covering foundation model integration, tradeoffs in AI systems, evaluation pipelines, and emerging architectural patterns.
Jorge BejarMay 12, 2025
3 min read
Artificial Intelligence
Embrace Uncertainty to Ship Better AI
AI-powered features introduce a new kind of uncertainty — not about when we'll ship, but about what the AI can actually achieve. Here's how we handle it.
Jorge BejarOct 27, 2025
Let's Build Together.
Ready to ship AI features that hold up past the demo and under real production load?