We bring experience, judgment, and deep understanding to every problem—building AI systems that actually hold up in production.
We don't sell experiments, prototypes, or hype-driven AI initiatives. We build systems that work.
Let's understandWe help teams build AI systems they can actually own
After a decade in software and ML engineering—building products, shipping systems, and watching AI projects succeed and fail—one pattern became clear: the failures almost always start with complexity, not capability.
We work across the full spectrum: greenfield projects, rescuing failed initiatives, taking prototypes to production, and augmenting existing teams. What stays constant is the approach—start simple, prove value, then scale.
Understand the problem before proposing solutions—most AI projects fail at the framing stage.
If you're rescuing a failed initiative, we diagnose before we rebuild.
Small tests that prove or disprove assumptions quickly—not to impress.
Your team should be able to own and evolve what we build together.
Choosing the right level of complexity for your problem
Retrieval-augmented generation works well when you have existing knowledge that needs to be surfaced accurately—not generated.
Good fit: Internal knowledge bases, document Q&A, decision-support with citations.
Often overkill: Simple lookup tables, well-structured databases, cases where traditional search suffices.
Agents add value when tasks require coordination, tool use, or autonomous decision-making—but they also add complexity.
Good fit: Multi-step processes, human-in-the-loop workflows, operations that benefit from autonomy.
Often overkill: Single-step tasks, predictable workflows, cases where a simple API call suffices.
Simpler architectures often win. We help you find the right level of complexity.
Not every project is a good fit. We turn down work when:
When we work together, you walk away with more than code
Initial conversations are exploratory and obligation-free.
Book a discovery call