Managed AI for Workforce Execution
Deploy AI-Powered Workforce Intelligence Without Writing a Single Line of Code
Turbine is the only end-to-end, managed AI platform built for workforce systems. Designed for training providers, employers, apprenticeship programs, and intermediaries, Turbine delivers pre-integrated AI assistants, compliance automation, cultural capture, and learning orchestration—no dev team required.
You bring the workforce. Turbine brings the intelligence.
Ship AI outcomes without hiring an AI team.
Deployable Assistants
Pre-built role- and task-based assistants for SOPs, learning, and compliance. Zero-code.
Governed Backend
Secure inference, governed metadata, and real-time orchestration. SOC2-ready.
Culture Capture
KnowledgeOps + VELA embed cultural norms and evidence capture directly into work.
What you get out of the box
Assistants, orchestration, and governance shipped day one.
Fractional AI Team-in-a-Box
- Voice-enabled OJT capture (VELA)
- Real-time guidance and task logging (Otto)
- Automated RAPIDS / PIRL compliance (ComplianceOps)
- Role-based pathway generation (LearningOps)
- Cultural pulse and knowledge preservation (KnowledgeOps + VELA)
AI-Native Workforce Stack
- Structured content governance (KnowledgeOps)
- Learning orchestration and LMS sync (LearningOps)
- Predictive reporting + ROI dashboards (ReportingOps)
- Secure, compliant audit trail (SecurityOps)
No engineers? No problem.
- SOC2-compliant infrastructure
- Pre-configured assistants by role, task, and compliance
- Built-in governance, version control, and explainability (MCP-powered)
From discovery to ROI in weeks
Guided implementation—no internal AI/ML team required.
Blueprint
Translate roles and SOPs into governed pathways, define compliance targets, and identify highest-ROI assistant use cases.
Assistants & Workflows
Enable VELA logging, Otto task automations, and PIRL/RAPIDS compliance exports mapped to policy.
Governance & LMS Sync
Connect content sources, set governance, and sync with LMS/HRIS for accurate, live reporting.
ROI & Optimization
Track proficiency, retention, and cost savings; expand assistant coverage where impact is highest.
Designed For
- Registered Apprenticeship Programs (via Apprentage)
- CTE and technical education institutions
- Employers scaling internal L&D or onboarding
- Workforce boards and WIOA-funded organizations
- Organizations looking to preserve culture, expertise, and institutional memory
Outcomes You Can Expect
- 3× faster onboarding of apprentices and new hires
- 99.9% compliance accuracy (federal/state ready)
- $48K–$180K in savings from reduced manual admin
- Deployment in under 30 days with 90%+ user adoption
- Increased cultural consistency and retention of tribal knowledge
Cost Comparison vs Hiring an AI/ML Team
Building similar capability internally typically requires a minimum 5-person cross-functional team.
Typical team (5 FTE)
- Lead AI/ML Engineer: $220K–$280K loaded
- ML Engineer (2): $180K–$240K each
- Data Engineer: $170K–$220K
- MLOps/Platform Engineer: $170K–$220K
Estimated annual cost
- Cloud + LLM usage: $100K–$300K+
- Tools/compliance: $50K–$150K+
Estimate Your Annual Savings
Adjust assumptions to project impact from Managed RAG, Deployable Assistants, and Compliance Automations.
Assumptions
Projected Annual Impact
Digital Infrastructure Addendum (MOU / IFA)
Preview and copy this addendum to align your MOU/IFA with a unified digital workforce infrastructure.
Digital Infrastructure Addendum to the Memorandum of Understanding (MOU) and Infrastructure Funding Agreement (IFA)
Effective Date: December 08, 2025
Addendum to: Memorandum of Understanding and Infrastructure Funding Agreement (IFA)
Between: The [Local Workforce Development Board], [Chief Elected Official(s)], and Required Partners under WIOA §121(b)
1. Purpose
This Addendum establishes the shared cost structure, responsibilities, and benefits associated with implementing a unified Digital Workforce Infrastructure System, encompassing Apprentage or Turbine Managed AI Platform features that support compliance automation, document management, and data sharing among required partners of the One-Stop Delivery System. This Addendum is incorporated into the MOU and IFA pursuant to WIOA Section 121(c) and (h) and 20 CFR 678.700–745.
2. Definition of the System
- Compliance automation for PIRL, RAPIDS, and ETA-9173/9179 reporting
- Secure document routing, electronic signature capture, and partner coordination
- AI-powered task logging and time savings for staff and administrators
- Data integration with LMS, HRIS, and state reporting systems
- Participant journey tracking, including apprenticeship and pre-apprenticeship pathways
3. Cost Classification
- Subscription and managed services: Infrastructure cost (Non-Personnel) under 2 CFR 200.94 and WIOA §121(h)(4).
- Administrative time savings: Allocable administrative cost avoidance under 2 CFR 200.405(d) and 2 CFR 200.404; documented in IFA narratives (TEGL 17-16).
- Configuration/integration services: Allowable, reasonable, and allocable costs supporting the shared system (2 CFR 200.404, 200.413).
4. Cost Allocation Methodology
Partner contributions to the System shall be proportionate to their use and benefit received, using one of the following approved allocation bases: Partner Staff FTEs, Active Participant Volume, Digital Licenses (Seats), or Weighted Usage Index.
5. Benefit–Cost Ratio and Administrative Cost Avoidance
Each partner may calculate the Benefit–Cost Ratio (BCR) using the formula: BCR = Administrative and Infrastructure Cost Savings ÷ Partner Contribution to the System. Per TEGL 17-16 and OMB Circular A-94, a BCR greater than 1.0 indicates a positive return.
10. Signatures
- [Local Workforce Development Board Representative] — Date: __________
- [Chief Elected Official(s)] — Date: __________
- [Required Partners (WIOA §121(b))] — Date: __________