AI Development & Engineering

AI engineering is the work of turning AI models into dependable business software. It covers building AI applications, sharing AI capabilities across teams, connecting AI to existing systems, and testing, deploying, and monitoring it in production.

They cover three parts: building the AI application, creating enterprise AI capabilities that teams share, and integrating AI with existing systems. Many projects need all three. A document review project, for example, may need a new application, capabilities shared across departments, and connections to existing records. We scope them together so the parts fit, then deliver them in the order that gives you value soonest. To see how the layers fit in one system, read AI Voice Agent Architecture: How Modern Voice AI Systems Work.

AI development builds the AI application itself, from requirements and model selection through evaluation and deployment. AI integration connects an AI capability to the systems your teams already use, such as ERP, CRM, and workflow tools. Enterprise AI engineering projects often need both, so we scope them together.

We integrate AI into existing enterprise systems by connecting it to ERP, CRM, document repositories, databases, APIs, and workflow tools. Each integration follows your existing permissions and approval rules, and we test it against real data flows before release.

Start with a clear business problem, the workflow you want to improve, and the result you want to measure. Then, identify the available data, required systems, and access rules. We review these inputs to define a practical project scope.

We test output quality, system connections, permissions, response times, and failure handling against agreed acceptance criteria. In addition, we check human review and approval flows where needed. A controlled pilot helps validate the workflow before wider rollout. For a structured way to think about AI risk and evaluation, see the NIST AI Risk Management Framework.

Cost depends on the scope of the application, the number of systems it connects to, the condition of your data, security and compliance requirements, and whether the capability is shared across teams. A focused pilot costs less than a shared enterprise platform. After scoping your use case, data, and systems, we provide an estimate for each phase.

Timelines depend on the same factors: scope, data readiness, the number of integrations, and testing and approval requirements. A focused use case can reach a controlled pilot sooner, while shared enterprise capabilities and multi-system integrations take longer. We agree on milestones for each phase when we define the scope.

Yes. We review the application, data flows, integrations, and production issues to identify what needs improvement. Depending on the findings, the work may include better evaluation, stronger access controls, faster responses, or more reliable deployment. For hosting options, read AI Voice Agent Deployment Models: SaaS, VPC and Self-Hosted. Cloud documentation such as the AWS Well-Architected Machine Learning Lens covers the architecture and security practices to plan for.