Agentic AI Development
Agent systems with tool use, retrieval, memory, and evaluation frameworks.
Agent architecture design and implementation. Tool calling with structured input and output schemas. Retrieval-augmented generation across multiple knowledge stores. Persistent memory systems with intentional retention policies. Evaluation harnesses for regression testing and quality tracing. Streaming interfaces for real-time delivery. Guardrails that keep the system reliable without making it rigid. Everything ships production-grade with observability built in.
Most teams come in with a strong idea of what AI should do for their users and a much weaker sense of what it takes to build it. The tooling landscape changes every few months. The right model, the right orchestration framework, the right retrieval strategy today may need to be re-evaluated when the next generation of models ships. Teams tend to pick an approach early and hold onto it too long because it worked once.
We spend a lot of time early in an engagement making sure we're building the right system for the problem, with the right tools, at the right time. That means sometimes recommending a simpler approach than the client expected, and sometimes explaining why the thing they thought was simple actually requires serious infrastructure.
Once you build an AI system and put it in front of users, you've made a commitment. The system needs to be maintained, evaluated, tuned, and evolved. Models get updated. User behavior shifts. New capabilities become available. If you stop investing in it, the experience degrades and users notice.
We build with that reality in mind from day one. The architecture is designed to evolve. The evaluation framework is designed to catch drift early. The system is built to be a living part of the product, because that's what it's going to be whether you plan for it or not.
More services.
From zero to shipped
