AI architecture and integration
Choose the right application pattern, connect models and business systems, and define clear boundaries for data and tool access.
TECHMOUNT / AI ENGINEERING
A compelling demo is only the beginning. We help engineering teams design the infrastructure and controls behind AI applications, from model integration and retrieval to evaluation, observability, and deployment.
Discuss your projectWHAT THIS COVERS
Architecture, evaluation, integrations, and operations for AI systems that need to perform reliably.
Choose the right application pattern, connect models and business systems, and define clear boundaries for data and tool access.
Test output quality, groundedness, safety, and task completion against scenarios that reflect your users and domain.
Track latency, cost, failures, and response quality so teams can investigate issues and improve after launch.
Plan rollout, human review, change management, and ongoing improvements as requirements and models evolve.
OUR APPROACH
We assess the current design, define measurable quality and operational targets, and prioritize the engineering work needed to move from pilot to dependable service.
COMMON QUESTIONS
AI development focuses on the application and user workflow. AI engineering focuses on the architecture, integrations, evaluation, deployment, and operations that support it.
We begin with representative tasks and expected outcomes, then assess dimensions such as groundedness, task success, safety, latency, and cost. Human review remains important for nuanced results.
The approach begins with your current systems and constraints. The architecture can be designed around the platforms and models your organization has approved.
START A CONVERSATION
Tell us about your challenge, the systems involved, and what success would look like.