Executive Summary
Company Positioning as AI Adoption Training Partners
White Noise, in strategic partnership with One Convergence, presents a comprehensive enterprise AI adoption training program designed to transform organizations from AI-curious to AI-empowered. Our partnership combines deep expertise in organizational change management with world-class technical infrastructure capabilities, delivering end-to-end AI enablement solutions that address the complete spectrum of enterprise AI adoption challenges.
As trusted AI adoption training partners, we position ourselves at the intersection of human capital development and technological transformation. We understand that successful AI implementation is not merely about deploying tools—it requires systematic upskilling, cultural adaptation, and strategic alignment across all organizational levels.
Vision Statement: Enterprise AI Transformation
"To empower every enterprise with the knowledge, skills, and infrastructure to harness artificial intelligence as a force multiplier—enabling 24/7 productivity, protecting intellectual property, and automating workflows while maintaining complete data sovereignty and security."
Our vision extends beyond technology deployment. We aim to create AI-literate organizations where human creativity and artificial intelligence work in concert to achieve unprecedented operational excellence.
Three-Tier Training Approach Overview
Our program is structured around a proven three-tier methodology that ensures systematic, scalable, and sustainable AI adoption:
| Tier | Focus Area | Key Components |
|---|---|---|
| Tier 1: Foundation | Organization-Wide Awareness | AI literacy, prompt engineering, security protocols, change management |
| Tier 2: Functional | Role-Specific Application | Department-specific use cases, workflow integration, advanced prompting |
| Tier 3: Advanced | Technical Integration & Infrastructure | On-premise deployment, custom models, API integration, security architecture |
Tier 1: Foundation Layer (Organization-Wide Awareness)
- AI literacy and awareness training for all employees
- Understanding AI capabilities, limitations, and ethical considerations
- Basic prompt engineering and AI tool familiarization
- Security protocols and data handling best practices
- Change management and mindset transformation
Tier 2: Functional Layer (Role-Specific Application)
- Department-specific AI use case identification and implementation
- Workflow integration and process optimization
- Advanced prompting techniques for domain-specific tasks
- Cross-functional collaboration and knowledge sharing
- Performance measurement and continuous improvement
Tier 3: Advanced Layer (Technical Integration & Infrastructure)
- On-premise AI deployment and infrastructure management
- Custom model development and fine-tuning
- API integration and automation pipeline creation
- Security architecture and data governance
- Scalable AI operations and maintenance
Key Value Propositions
24/7 Productivity Enablement
- Transform traditional 9-to-5 operations into continuous, AI-augmented workflows
- Enable asynchronous collaboration across time zones and departments
- Automate routine tasks to free human capital for strategic initiatives
- Reduce response times from hours to minutes through intelligent automation
- Achieve measurable productivity gains of 30-50% within 90 days of implementation
Intellectual Property Protection
- Deploy on-premise AI solutions ensuring complete data sovereignty
- Eliminate risks associated with cloud-based AI processing
- Maintain full audit trails and compliance documentation
- Protect proprietary algorithms, customer data, and trade secrets
- Meet regulatory requirements for data residency and privacy (GDPR, HIPAA, SOC 2)
Workflow Automation Excellence
- Identify and eliminate process bottlenecks through AI-driven analysis
- Implement intelligent document processing and data extraction
- Create self-optimizing workflows that improve over time
- Integrate AI seamlessly with existing enterprise systems
- Enable citizen developers to build AI-powered automation without coding
The AI Imperative for Enterprises
Current Market Landscape and Competitive Pressure
The enterprise AI market is experiencing unprecedented acceleration. According to recent industry analyses, organizations that have successfully implemented AI report:
- 40% reduction in operational costs through intelligent automation
- 35% improvement in decision-making speed and accuracy
- 50% faster time-to-market for new products and services
- 25% increase in customer satisfaction scores
- Significant competitive advantages in talent acquisition and retention
The 9-to-5 Limitation vs 24/7 AI-Powered Operations
| Conventional Operations (9-to-5) | AI-Powered Operations (24/7) |
|---|---|
| Response delays during non-business hours | Continuous customer support without fatigue |
| Knowledge silos when key personnel unavailable | Real-time data analysis around the clock |
| Inconsistent service quality across shifts | Automated quality assurance monitoring |
| Burnout from manual, repetitive tasks | Instant document processing and workflow execution |
| Missed opportunities from delayed decisions | Global collaboration without time zone constraints |
Data Sovereignty and IP Protection Challenges
As enterprises explore AI adoption, critical security and compliance concerns emerge:
Cloud-Based AI Risks:
- Data exposure to third-party AI providers and their infrastructure
- Unclear data retention and processing policies
- Potential for proprietary information to train public models
- Regulatory non-compliance with data residency requirements
- Limited visibility into how sensitive data is handled
Compliance and Regulatory Pressure:
- GDPR requirements for data processing within jurisdictional boundaries
- Industry-specific regulations (HIPAA for healthcare, PCI-DSS for finance)
- Customer contractual obligations regarding data handling
- Board and shareholder expectations for data protection
- Increasing regulatory scrutiny of AI systems and their training data
Complete control over data processing and storage, full compliance with regulatory and contractual requirements, protection of intellectual property and competitive advantages, transparent audit trails and governance frameworks, and scalable architecture that grows with organizational needs.
Why Companies Need Structured AI Adoption Training
Ad-hoc AI adoption leads to fragmented implementations, security gaps, and missed opportunities. Structured training programs deliver:
Strategic Alignment:
- Clear connection between AI initiatives and business objectives
- Coordinated implementation across departments and functions
- Executive sponsorship and organizational buy-in
- Defined success metrics and ROI measurement frameworks
Risk Mitigation:
- Standardized security protocols and compliance procedures
- Consistent data handling practices across the organization
- Reduced likelihood of shadow AI usage and data leakage
- Documented governance frameworks and audit readiness
Accelerated Value Realization:
- Faster time-to-value through proven methodologies
- Reduced trial-and-error and implementation failures
- Leverage of best practices from successful deployments
- Continuous improvement through feedback and iteration
Partnership Overview
White Noise Expertise: Training & Change Management
White Noise brings deep expertise in organizational transformation and human capital development:
Training Excellence:
- Proven curriculum development for enterprise AI literacy
- Experienced facilitators with enterprise transformation backgrounds
- Customized training programs tailored to organizational culture
- Blended learning approaches (in-person, virtual, self-paced)
- Continuous learning platforms and knowledge management systems
Change Management Capabilities:
- Organizational readiness assessment and gap analysis
- Stakeholder engagement and communication strategies
- Resistance management and adoption acceleration
- Culture transformation and mindset shift programs
- Executive coaching and leadership development
One Convergence Expertise: Technical Infrastructure
One Convergence delivers world-class technical infrastructure and deployment capabilities:
On-Premise AI Solutions:
- Enterprise-grade AI infrastructure design and deployment
- Secure, air-gapped AI systems for maximum data protection
- Scalable architecture supporting from hundreds to millions of users
- Integration with existing enterprise systems and databases
- High-availability configurations with disaster recovery
Combined Value Proposition
End-to-End Solution: Single partnership covering training, implementation, and support with aligned methodologies ensuring seamless transition from learning to doing.
Reduced Complexity: One contract, one point of contact, one integrated team—elimination of gaps between training and technical implementation.
Accelerated Outcomes: Parallel workstreams for training and infrastructure deployment with faster time-to-value through coordinated execution.
Target Audiences & Use Cases
Non-Technical Teams: Productivity and Workflow Efficiency
Target Audiences: Human Resources, Marketing, Operations, Finance, Legal
Key Use Cases:
- AI-Powered Content Creation: Generate marketing copy, internal communications, and documentation with consistent brand voice
- Intelligent Document Processing: Extract information from contracts, invoices, and forms with 95%+ accuracy
- Meeting Intelligence: Automated transcription, action item extraction, and follow-up scheduling
- Research Acceleration: Rapid synthesis of market data, competitor analysis, and industry trends
- Workflow Automation: Eliminate manual data entry and routine administrative tasks
Outcomes:
- 40-60% reduction in time spent on routine documentation tasks
- Improved consistency and quality of communications
- Faster turnaround on research and analysis projects
- Enhanced employee satisfaction through elimination of tedious work
Customer Support, Product, R&D, Logistics: Process Optimization
Target Audiences: Customer Support, Product Management, R&D, Supply Chain, Quality Assurance
Key Use Cases:
- Intelligent Customer Support: AI-assisted ticket routing, response suggestions, and knowledge base automation
- Product Intelligence: Automated user feedback analysis, competitive benchmarking, and feature prioritization
- R&D Acceleration: Literature synthesis, hypothesis generation, and experimental design optimization
- Supply Chain Optimization: Predictive demand forecasting, inventory optimization, and logistics planning
- Quality Management: Automated test generation, defect pattern recognition, and compliance monitoring
Technical Teams: Deep Integration & Secure Infrastructure
Target Audiences: Software Engineering, Data Engineering, IT Operations, Data Science, Security Teams
Key Use Cases:
- AI-Assisted Development: Code generation, automated testing, documentation, and code review assistance
- Infrastructure Automation: Intelligent monitoring, predictive maintenance, and automated scaling
- Data Pipeline Optimization: Automated ETL development, data quality monitoring, and anomaly detection
- Secure AI Deployment: On-premise model serving, API development, and access control
Cross-Functional Benefits
- Unified AI Literacy: Common language and understanding across departments
- Collaborative Innovation: Cross-pollination of ideas and best practices
- Scalable Knowledge Management: Institutional knowledge capture and accessibility
- Future-Ready Workforce: Skills that remain relevant as AI technology evolves
Training Curriculum
Track 1: Velocity Fundamentals
Day 1: AI Fundamentals & Claude Mastery (8 hours)
Module 1.1: Introduction to AI & Enterprise Applications (2 hours)
Learning Objectives:
- Understand fundamental AI concepts (LLMs, generative AI, prompt engineering)
- Recognize AI capabilities and limitations in enterprise contexts
- Identify high-value use cases for AI in daily workflows
- Develop AI literacy for responsible and effective usage
Module 1.2: Claude Fundamentals & Interface Mastery (3 hours)
Learning Objectives:
- Navigate Claude interface confidently
- Master basic to advanced prompting techniques
- Understand Claude's capabilities for business tasks
- Apply effective communication patterns with AI
Module 1.3: Artifacts, Dashboards & Advanced Features (3 hours)
Learning Objectives:
- Create and manage Claude Artifacts effectively
- Build interactive dashboards and visualizations
- Leverage Claude for data presentation and reporting
- Integrate Claude outputs into business workflows
Day 2: Workflow Automation & Digital Twins (8 hours)
Module 2.1: Introduction to No-Code Automation (2.5 hours)
Tools Covered: Zapier (Starter), Make (Basic), Power Automate (Basic)
Module 2.2: Digital Twins for Business Processes (2.5 hours)
Create AI-powered process simulations, build decision-support systems, and design virtual assistants for routine tasks.
Module 2.3: AI-Augmented Document & Email Workflows (3 hours)
Intelligent document processing, smart email management, and approval workflow automation.
Day 3: Content Creation, Data Analysis & Decision Support (8 hours)
Module 3.1: AI-Powered Content Creation (2.5 hours)
Brand voice configuration, marketing content production, and content adaptation.
Module 3.2: Data Analysis with AI Assistance (2.5 hours)
Tools Covered: Claude, Google Sheets/Excel, Tableau Public, Looker Studio
Module 3.3: AI for Decision Support (3 hours)
Decision framework building, business case development, and stakeholder presentation.
Day 4: Integration with Enterprise Tools (8 hours)
Module 4.1: Slack & Communication Platform Integration (2 hours)
Tools Covered: Slack, Claude for Slack, Zapier, Workflow Builder
Module 4.2: Google Workspace & Microsoft 365 Integration (2.5 hours)
Tools Covered: Google Workspace, Gemini (Google), Claude, Apps Script (basic)
Module 4.3: CRM & Business Application Integration (2 hours)
Tools Covered: Salesforce Einstein, HubSpot AI, Zapier, Claude
Module 4.4: Capstone Project & Integration Challenge (1.5 hours)
Build end-to-end workflow using 3+ tools, integrate Claude with enterprise applications.
Format: Comprehensive practical examination | Duration: 2 hours
Components: Written (30%), Practical (50%), Portfolio (20%)
Passing Score: 80% overall | Certification valid 2 years
Track 2: Operational Excellence
Day 1: Customer Support Automation & Quality (8 hours)
Module 1.1: AI-Enhanced Customer Support (3 hours)
Tools Covered: Zendesk AI, Freshdesk AI, Intercom Fin, Claude
Module 1.2: Chatbot Development & Deployment (3 hours)
Knowledge base integration, conversation flow design, chatbot testing & deployment.
Day 2: Product Management with AI (8 hours)
Module 2.1: AI-Powered Market Research (2.5 hours)
Tools Covered: Crayon, Klue, Productboard, Claude, Tableau
Module 2.2: AI-Assisted Roadmapping & Prioritization (2.5 hours)
Tools Covered: Aha!, Productboard, Jira, Claude, Google Sheets
Day 3: R&D Acceleration (8 hours)
Module 3.1: AI-Assisted Research & Literature Review (2.5 hours)
Tools Covered: Elicit, ResearchRabbit, Semantic Scholar, Claude, Zotero
Module 3.2: Technical Documentation & Knowledge Management (2.5 hours)
Tools Covered: GitHub Copilot, Claude, Notion, Confluence, ReadMe
Day 4: Logistics & Operations Optimization (8 hours)
Module 4.1: Supply Chain AI & Predictive Analytics (2.5 hours)
Tools Covered: Tableau, Power BI, Python (basic), Claude, ERP systems
Module 4.2: Process Optimization & Automation (2.5 hours)
Tools Covered: ProcessMaker, Camunda, Power Automate, Claude, Tableau
Format: Practical examination with real-world scenario | Duration: 3 hours
Components: Scenario-based (40%), System design (35%), Presentation (25%)
Passing Score: 80% overall | Certification valid 2 years
Track 3: Deep Velocity
Week 1-2: On-Premise LLM Architecture & Deployment (40 hours)
Module 1.1: LLM Fundamentals & Architecture (8 hours)
Transformer architecture, tokenization, attention mechanisms, model evaluation.
Module 1.2: On-Premise LLM Deployment (12 hours)
llama.cpp, Ollama, vLLM, quantization techniques, hardware optimization.
Module 1.3: Model Fine-Tuning & Customization (10 hours)
Dataset preparation, LoRA/QLoRA fine-tuning, model evaluation, custom deployment.
Module 1.4: Production Infrastructure & Operations (10 hours)
High-availability architecture, monitoring, auto-scaling, model versioning, disaster recovery.
Week 3: Agent Frameworks & Docker Deployment (40 hours)
Module 2.1: AI Agent Architecture & Development (12 hours)
ReAct agents, tool use, memory systems, multi-agent coordination, evaluation.
Module 2.2: LangChain & Agent Frameworks (12 hours)
LangChain fundamentals, chains & pipelines, tool integration, RAG, deployment.
Module 2.3: Docker Containerization (8 hours)
Dockerfile creation, multi-stage builds, Docker Compose, production deployment.
Module 2.4: Kubernetes Orchestration (8 hours)
Deployments, HPA & auto-scaling, ConfigMaps & Secrets, Helm Charts.
Week 4: API Integration, Security & Governance (40 hours)
Module 3.1: API Development & Integration (10 hours)
FastAPI, LLM service APIs, GraphQL, API documentation.
Module 3.2: Authentication & Authorization (10 hours)
JWT, RBAC, OAuth 2.0, API key management.
Module 3.3: AI Governance & Ethics (10 hours)
Governance frameworks, bias detection, audit & explainability, compliance.
Module 3.4: Security Best Practices (10 hours)
Prompt injection defense, endpoint security, data protection, security testing.
Week 5-6: Advanced Automation, CI/CD & Production Scaling (80 hours)
Module 4.1: CI/CD Pipelines for AI (16 hours)
GitHub Actions, model CI/CD, automated testing, deployment automation.
Module 4.2: Infrastructure as Code (12 hours)
Terraform fundamentals, LLM infrastructure, multi-environment, infrastructure testing.
Module 4.3: Performance Optimization (12 hours)
Inference optimization, batching strategies, GPU optimization, caching layer.
Module 4.4: Monitoring & Observability (12 hours)
Prometheus metrics, logging & tracing, Grafana dashboards, incident response.
Module 4.5: Capstone Project (28 hours)
Deploy on-premise LLM, build agent application, implement security & governance, set up CI/CD and monitoring.
Deliverables: Working production system (40%), Technical documentation (20%), Architecture diagrams (15%), Operations runbook (15%), Final presentation (10%)
Certification Specializations
| Specialization | Focus Areas | Additional Requirements |
|---|---|---|
| LLM Architecture Specialist | Model selection, deployment, optimization | Deploy 3+ models, performance benchmarking |
| AI Agent Engineer | Agent development, multi-agent systems | Build 2+ agent applications, coordination demo |
| MLOps & Production Systems | CI/CD, monitoring, scaling | Production deployment with monitoring |
| AI Security & Governance | Security, compliance, ethics | Security audit report, governance framework |
Format: Comprehensive technical examination | Duration: 4 hours
Components: Written (25%), Practical (50%), Capstone (25%)
Passing Score: 80% overall | Certification valid 2 years with optional specializations
Deployment Architecture & Security
On-Premise Solutions for Data/IP-Sensitive Environments
On-premise deployment remains the gold standard for organizations handling sensitive data across finance, healthcare, R&D, and legal sectors.
C-Based LLM Deployment on x86 Architecture
- 3-10x faster inference compared to Python-based implementations
- Lower memory footprint enabling larger models on same hardware
- Better CPU utilization through optimized SIMD instructions (AVX-512, AMX)
- Reduced latency for real-time applications
Key C-Based Frameworks
| Framework | Best For | Key Features |
|---|---|---|
| llama.cpp | General-purpose on-premise | GGUF format, extensive quantization, CPU/GPU, 50K+ stars |
| Ollama | Rapid prototyping | User-friendly CLI, REST API, Docker support |
| MLC-LLM | Multi-platform | TVM compiler, various hardware targets |
| llamafile | Portable single-file | Single executable, cross-platform, no dependencies |
Recommended Local Models
| Model | Parameters | VRAM (Q4) | Best For |
|---|---|---|---|
| Mistral 7B Instruct | 7B | ~4GB | General-purpose, coding, reasoning |
| Llama 3 8B Instruct | 8B | ~5GB | General chat, multilingual |
| Llama 3 70B Instruct | 70B | ~40GB | Complex reasoning, enterprise tasks |
| Code Llama 34B | 34B | ~20GB | Software development, code generation |
| Mixtral 8x7B (MoE) | 47B active | ~28GB | High-quality, diverse tasks |
Docker Containerization
# Example Dockerfile for llama.cpp deployment
FROM ubuntu:22.04
RUN apt-get update && apt-get install -y \
build-essential cmake git \
&& rm -rf /var/lib/apt/lists/*
WORKDIR /app
RUN git clone https://github.com/ggerganov/llama.cpp.git
WORKDIR /app/llama.cpp
RUN cmake -B build && cmake --build build --config Release
EXPOSE 8080
CMD ["./build/bin/server", "-m", "/models/model.gguf", \
"--host", "0.0.0.0", "--port", "8080"]
Hardware Requirements
| Size | CPU | RAM | Storage | GPU |
|---|---|---|---|---|
| Small (1-10 users) | 8 cores | 32 GB | 500 GB SSD | RTX 4090 (24GB) |
| Medium (10-50) | 16 cores | 64 GB | 1 TB SSD | A100 (40GB) |
| Large (50-200) | 32+ cores | 128 GB | 2 TB SSD | 2x A100 (80GB) |
| Enterprise (200+) | 64+ cores | 256+ GB | 5+ TB NVMe | Multi-GPU cluster |
Security & Governance Framework
Role-Based Access Control (RBAC)
| Role | Permissions | Typical Users |
|---|---|---|
| Admin | Full system access, user management | IT Administrators, Security Officers |
| Developer | API access, model deployment, testing | Software Engineers, Data Scientists |
| Analyst | Query access, report generation, read-only | Business Analysts |
| End User | Chat interface, limited queries | General employees |
Compliance Frameworks
| Framework | Focus | Implementation |
|---|---|---|
| SOC 2 | Security, availability, confidentiality | Annual audits, continuous monitoring |
| ISO 27001 | Information security management | Certified ISMS, regular assessments |
| GDPR | Data protection and privacy | Data processing agreements, DPO |
| HIPAA | Healthcare data protection | BAA, encryption, access controls |
Legal Framework & Compliance
IMPORTANT LEGAL DISCLAIMER
This document constitutes a legal framework template for informational purposes only. It does not constitute legal advice.
White Noise, One Convergence, and all parties are advised to consult qualified legal counsel before executing any agreements based on this framework.
No Attorney-Client Relationship: Review of this document does not create an attorney-client relationship.
Data Protection & IP Security
| Data Classification | Handling | Access Controls | Encryption |
|---|---|---|---|
| Public | Standard handling | Basic authentication | In transit (TLS) |
| Internal | Role-based access | RBAC required | At rest + in transit |
| Confidential | Need-to-know basis | Strict authorization | AES-256 minimum |
| Restricted | Executive approval | Multi-factor auth | HSM-protected keys |
IP Protection Guarantees
- No Training on Client Data: Client data shall not be used to train any AI models without express written consent
- Isolated Environments: All work involving proprietary information occurs in isolated, dedicated environments
- Output Ownership: All outputs generated from Client's proprietary information remain Client's exclusive property
- No Reverse Engineering: Service Providers shall not attempt to reverse engineer Client systems or data
Service Level Agreements
| Metric | Target | Measurement | Remedy |
|---|---|---|---|
| System Uptime | 99.9% | Monthly | Service credits |
| Response Time (Critical) | 4 hours | Per incident | Escalation |
| Resolution Time (Critical) | 24 hours | Per incident | Service credits |
| Training Delivery | 100% on schedule | Per session | Reschedule + credit |
Intellectual Property Ownership
| Category | Ownership | License |
|---|---|---|
| Pre-existing IP (White Noise) | White Noise | Limited license for engagement |
| Pre-existing IP (One Convergence) | One Convergence | Limited license for engagement |
| Custom Training Materials | Client (work-for-hire) | Perpetual, worldwide, royalty-free |
| Custom Code/Configurations | Client (work-for-hire) | Perpetual, worldwide, royalty-free |
| Joint Improvements | Joint ownership | Cross-license for future use |
Liability & Risk Management
Limitation of Liability
- Cap on Liability: Aggregate liability shall not exceed the greater of (a) total fees paid in 12 months preceding the claim, or (b) $500,000 USD
- Excluded Damages: Neither party liable for indirect, incidental, consequential, special, or punitive damages
- Exceptions: Limitations do not apply to breach of confidentiality, IP infringement, gross negligence, or regulatory violations
Insurance Requirements
| Coverage Type | Minimum Amount |
|---|---|
| General Liability | $2,000,000 per occurrence |
| Professional Liability (E&O) | $5,000,000 aggregate |
| Cyber Liability | $5,000,000 aggregate |
| Workers Compensation | Statutory limits |
Business Case & ROI
Return on Investment Analysis
Time Savings by Employee Category
| Employee Category | Weekly Hours Saved | Annual Hours | Annual Value/Employee |
|---|---|---|---|
| Administrative Staff | 11.5 hours | 598 hours | $44,850 |
| Marketing Professionals | 15.2 hours | 790 hours | $59,250 |
| Customer Support | 18.8 hours | 978 hours | $73,350 |
| Product Managers | 16.5 hours | 858 hours | $64,350 |
| R&D Engineers | 22.3 hours | 1,160 hours | $87,000 |
| Software Developers | 25.6 hours | 1,331 hours | $99,825 |
| Data Scientists | 21.4 hours | 1,113 hours | $83,475 |
ROI by Organization Size
| Org Size | Investment | Year 1 ROI | 3-Year ROI | Payback | NPV (3-Year) |
|---|---|---|---|---|---|
| 25 Employees | $312,500 | 2,956% | 8,692% | 2 weeks | $11.2M |
| 100 Employees | $1,250,000 | 3,296% | 9,865% | 11 days | $45.8M |
| 500 Employees | $6,250,000 | 3,829% | 11,532% | 9 days | $234.5M |
| 1,000 Employees | $12,500,000 | 4,125% | 12,485% | 7 days | $489.2M |
| 5,000 Employees | $62,500,000 | 4,650% | 14,125% | 5 days | $2.48B |
Implementation Roadmap
Phase 1: Discovery & Pilot (Weeks 1-2)
- Current state workflow audit and documentation
- Stakeholder alignment and executive sponsorship
- Pilot team selection and preparation
- Technical infrastructure assessment
Phase 2: Foundation Training (Weeks 3-4)
- Track 1 delivery to pilot groups
- Tool setup and basic automation implementation
- Quick win identification and celebration
Phase 3: Advanced Deployment (Weeks 5-8)
- Track 2 and Track 3 delivery
- Infrastructure deployment for technical teams
- Integration with enterprise systems
Phase 4: Scale & Optimize (Ongoing)
- Continuous improvement and optimization
- Expansion to additional teams and use cases
- ROI measurement and reporting
Pricing Structure
Training Track Pricing
| Training Track | Pricing Model | Base Price | Volume Discounts |
|---|---|---|---|
| Track 1: Velocity Fundamentals 3-4 Days | Per participant | $2,500/participant | 10-24: 10% off | 25-49: 20% off | 50+: 30% off |
| Track 2: Operational Excellence 3-4 Days | Per team (up to 10) | $35,000/team | 2-3 teams: 15% off | 4+: 25% off |
| Track 3: Deep Velocity 4-6 Weeks | Enterprise engagement | Starting at $250,000 | Custom enterprise pricing |
Enterprise Package Pricing
| Package | Includes | Price |
|---|---|---|
| Starter Up to 50 employees | Track 1 for all + Track 2 for 2 teams + Basic support (6 months) | $175,000 |
| Professional Up to 200 employees | Track 1 for all + Track 2 for 5 teams + Track 3 for 10 engineers + Support (1 year) | $650,000 |
| Enterprise Up to 1,000 employees | All tracks + Custom curriculum + Dedicated success manager + Support (2 years) | $2,500,000 |
| Transformation Unlimited | Everything in Enterprise + On-premise infrastructure + Custom AI models + 3-year partnership | Custom pricing |
If you don't see measurable improvements within 90 days of completing the training, we will provide additional training at no cost until your targets are met.
Conditions: Participants must complete all modules, implement recommended practices, and provide access to usage data for measurement.
Payment Terms
- Standard Terms: Net 30 days from invoice date
- Payment Schedule: 30% upon contract execution, 40% at midpoint, 30% upon completion
- Early Payment Discount: 2% discount for payment within 10 days
- Accepted Methods: Wire transfer, ACH, corporate credit card (3% fee)
Ready to Transform Your Organization?
Let's discuss how White Noise and One Convergence can accelerate your AI adoption journey.