Custom AI Workflows & Pipelines
AI Build
Prompt-and-pray isn’t anAI strategy.
Design and deploy agent workflows, orchestration, and integrations across your stack—with before-and-after metrics leadership can review.
The problem
AI prototypes that never reach production systems
Teams can demo an agent in a sandbox or wire a one-off API call—but connecting models to ERP, CRM, ticketing, and approval paths is where most builds stall. Without orchestration patterns and success metrics, custom AI stays a side project.
Disconnected tools and APIs
Models, agents, and automations live in separate consoles. Nobody owns the end-to-end flow from trigger to system-of-record update—so pilots never graduate to daily operations.
Orchestration without standards
Power Automate, Logic Apps, Copilot Studio, and custom runtimes each solve a slice. Without shared patterns for logging, retries, and human handoff, every workflow is a one-off maintenance burden.
Approvals and audit gaps
Workflows that change customer records, financial data, or regulated content need explicit approval paths. Ad-hoc integrations bypass AI governance and stall security review.
No before-and-after metric
Leadership funded a build sprint but cannot compare cycle time, error rates, or throughput before and after the workflow shipped—so expansion requests lack evidence.
Our approach
From workflow map to measured production
We design orchestration that fits your stack—not a generic “AI platform” rip-and-replace. Data-heavy ingestion and classification patterns live on our AI-augmented data pipelines page; here we focus on business-process automation and multi-system integration.
Outcomes
What custom workflow engagements deliver
Production orchestration
End-to-end flows from trigger through model or agent step to system update—with retries, timeouts, and escalation paths operators can run without vendor support tickets.
Integration patterns
Reusable connector and auth patterns across CRM, ERP, ticketing, and document stores—so the second workflow ships faster than the first.
Before-and-after metrics
Cycle time, throughput, and error-rate comparisons leadership can review in budget discussions—not anecdotal “time saved” stories.
Low-code telehealth platform case study — workflow automation on a lean delivery team. Need governed agents specifically? See Agents & automation.
Turn AI prototypes into production workflows
Start with an AI Activation Assessment to map workflow opportunities and readiness—or contact us if you already know the integration pattern you need to ship.
