Difficult AI engineering, from prototype to production.

Six capability areas, all reinforcing the same thing: AI product engineering, done at production quality.

AI Applications

What we do: LLM-powered applications, agents, RAG, and orchestration built for production, not demos.
Typical problem: Prototypes that work in a sandbox but fall apart under real users, real data volume, or real latency requirements.
Example technology: RAG pipelines on vector search infrastructure, multi-agent orchestration, prompt versioning and LLM evaluation frameworks.
Typical engagement: Project delivery or an embedded engineering pod, depending on scope.

What we do: Voice, conversational AI, streaming, realtime interaction, and AI avatars.
Typical problem: Realtime systems have different failure modes than request/response APIs — latency budgets, streaming state, and GPU-bound inference all compound.
Example technology: Realtime avatar rendering, voice pipelines, GPU inference serving, low-latency orchestration between conversation, voice, and video layers.
Typical engagement: Specialist capacity brought in for the realtime component of a larger project.

What we do: GPU infrastructure, model serving, inference optimization, scaling, and cost control.
Typical problem: AI features that work in a demo but cost too much or scale too poorly to ship.
Example technology: Model serving infrastructure, inference optimization, GPU capacity planning and cost management.
Typical engagement: Specialist capacity or an infrastructure-focused engineering pod.

What we do: EHR integrations, healthcare workflows, interoperability, and AI systems for regulated environments.
Typical problem: Fragmented EHR ecosystems and regulatory constraints make integration the hard part, not the AI itself.
Example technology: EHR/EMR integration layers, healthcare data interoperability, AI systems designed for regulated, audit-relevant environments.
Typical engagement: Project delivery for a defined integration, or ongoing engineering for a healthcare AI product.

What we do: AI products and infrastructure for lending, payments, financial services, and regulated financial workflows.
Typical problem: Financial services AI has to work under audit, compliance, and reliability constraints most consumer AI products never face.
Example technology: Lending and credit workflow systems, regulated data handling, production reliability engineering for financial infrastructure.
Typical engagement: Overflow capacity or a specialist engineering pod for the regulated component of a project.

What we do: Taking an AI prototype or proof of concept into a reliable production system.
Typical problem: The gap between 'it works in the demo' and 'it works in production' — latency, reliability, observability, security, and cost.
Example technology: Production hardening across latency, reliability, cost, observability, security, and scalability.
Typical engagement: Project delivery to productionize a specific system, or ongoing engineering.