― Managed AI Workflow Operations
The Full Operating Layer
A live workflow is not a finished product. The environment around it shifts: data patterns change, edge cases accumulate, upstream systems evolve, and the exceptions that seemed rare at launch become the dominant workload. Automation that looked complete in a pilot starts requiring more human attention, not less. Someone has to manage throughput, handle exceptions, keep queues moving, and improve the operation as patterns emerge.
Prosable Operations provides the operating layer: the human and AI structure that turns a live workflow into a reliable business process. The engagement comes with named roles, visible ownership, and accountability for operating results.
Choose the Right Operating Model
The two models deliver the same operating capabilities. What changes is where day-to-day execution sits and where long-term ownership lives.
Managed Operations: We run the workflow with SLA accountability.
- Prosable provides the operating team: exception queue management, L1/L2/L3 triage and resolution, integration and business context maintenance, and continuous improvement on a defined cadence
- Strategic oversight remains with the client organization
- SLA-backed delivery: exception resolution time targets, throughput commitments, and quarterly performance reporting with full transparency into operating metrics
- Committed outcomes, hybrid pricing: base retainer plus consumption-based resolution fees. Six-month initial term, then month-to-month with 60-day notice
Embedded Operations: We help client teams run the workflow and build the capability in-house.
- A forward-deployed Prosable team works alongside the organization’s staff: coaching exception handling, building playbooks together, setting up the Learning Loop, and transferring operating capability over a 3-6 month window
- This is a change management engagement as much as an operations engagement: role-specific training, coaching internal leads who drive adoption across their teams, tracking uptake, and structured knowledge transfer
- The fee declines as the team matures and takes on more of the operating load
- Maturity milestones trigger the transition; not calendar dates
Tiers Calibrated to Judgment
Both models use a tiered operating structure that distributes work across three levels, each calibrated to the judgment the case requires.
L1 AI workers handle triage and routine volume: classification, pattern matching, and routing within defined rules. L2 human specialists handle the exceptions that require judgment: ambiguous cases, incomplete context, policy interpretation, and edge cases that fall outside the playbook. L3 domain experts handle the highest-stakes decisions: strategic exceptions, process redesign, and cases where the business context demands senior operating judgment.
The goal extends beyond clearing the queue: reduce avoidable exceptions and shift more work to L1 resolution as the Learning Loop captures patterns and playbooks get sharper.
Day-to-Day Scope and Accountability
Operational delivery covers the full scope of production workflow support: intake quality and context validation, routing and queue management, exception resolution and escalation, backlog aging and priority management, and the continuous improvement cadence that drives exception rates down cycle over cycle.
This maps to the four components of the Prosable Operations Engine: Context Layer, Platform Ops, Exception Desk, and Learning Loop.
Named roles, regular cadence, visible queue ownership, and improving performance through each cycle. In Managed Operations, that means SLA accountability, weekly exception reviews, monthly playbook updates, and quarterly performance reporting. In Embedded Operations, it means hands-on coaching, capability transfer, and steadily increasing internal ownership as the team’s operating discipline strengthens.
