Five AI Agents Across Four Departments for a Field Services Company
.png)
When Tech 24 approached Streamline, they weren’t looking for another tool. They were looking for a way to fundamentally change how their operations ran. Growth had outpaced their manual processes, and the patchwork of disconnected systems across dispatch, finance, procurement, and customer service was creating bottlenecks that couldn’t be solved by adding more headcount.
On the surface, things looked manageable—calls were answered, invoices got processed, technicians were dispatched. But under the surface, every handoff between teams was a potential failure point. Scheduling was reactive. Vendor follow-ups fell through cracks. Customer issues were tracked informally across inboxes and spreadsheets.
It wasn’t a single broken process. It was a systemic one.
We started with a thorough operational audit, reviewing process documentation, onboarding materials, and recorded interactions across departments. The goal was to understand not just what each team did, but how they described their own work—and where those descriptions diverged from reality.
What we found was telling. Each department had its own vocabulary for the same problems. Dispatch called it “scheduling gaps.” Finance called it “reconciliation delays.” Customer service called it “escalation overload.” They were all describing the same upstream issue: a lack of intelligent routing and automated handoffs between systems.
The discovery also revealed something less technical but equally important: there was no shared framework for deciding what should be automated versus what needed a human. Every team had different instincts about where AI could help, and the lack of a unified perspective was blocking progress.
Streamline designed a phased engagement built on n8n workflow orchestration and purpose-built AI agents. Rather than attempting a single massive deployment, we structured the rollout in sequential 60-day phases—each one delivering a production-ready agent while the next phase entered discovery.
This approach gave Tech 24 immediate operational value while maintaining the flexibility to adjust scope and priorities as early results came in.
The solution is anchored by n8n as the central workflow orchestrator, connecting all agents to Tech 24’s existing enterprise systems:
All traffic is encrypted in transit and at rest. Tech 24 maintains full ownership of all data and knowledge-base content, with no data used for third-party model training.
Each phase follows a consistent 60-day delivery window with overlapping discovery and development:
The following metrics represent projected targets based on the solution design and comparable deployments. These targets will be validated with production data as the rollout continues.
Based on Tech 24’s transaction volume and a target deflection rate in the 50–70% range, the solution is projected to deliver meaningful cost savings within the first month of full deployment—driven by reduced manual handling and faster resolution times across all four departments.
A core requirement was building a structured escalation system from scratch. Streamline implemented a comprehensive framework:
With the first phases live and additional agents rolling out on 60-day cycles, Tech 24 is building toward a fully AI-augmented operations model. Upcoming milestones include voice channel integration, enhanced analytics dashboards, and expansion of the customer-facing AI chatbot to additional service lines.
Every phase generates production data that will validate and refine the target metrics outlined in this case study—transforming projections into proven outcomes.
