VOAR

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VOAR Proposal

“We pioneer business transformation by merging brilliant technology with artificial intelligence excellence, backed by advanced research.”

Proven Experience

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Our Approach

 
 
18 Years of Experience
18 Years of Experience
600+ Successful Projects
600+ Successful Projects
 
98% Delivery Satisfaction
98% Delivery Satisfaction
 
 
 

Trusted by Leading Brands Across the Globe

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Testimonials

“Their quality of service and care for providing an excellent product made them stand out. They presented a number of fresh ideas and solutions that really appealed to us and what we were looking for in a partner.” - Megan R., Disney

Concepta MAGIC is Level 5 AI HUB

Level 0: Basic Awareness

Description: Foundational understanding of AI concepts and minimal engagement with AI models.
Requirements:
  • Awareness: Basic understanding of AI, including exposure to third-party AI models like OpenAI, Gemini, Claude, etc.
  • Training: Introductory AI training with an overview of AI models and their capabilities.
  • Model Interaction: Limited interaction with AI models.

Level 1: Initial Adoption

Description: Early stages of AI adoption with experimentation using third-party models.
Requirements:
  • Pilot Projects: Completion of at least one project using third-party AI models (e.g., GPT, Gemini) without modifications.
  • Basic Integration: Initial integration of third-party models into specific business functions.
  • Model Competency: Basic competency in deploying pre-built models via APIs.
  • AI Team Formation: Establishment of a basic AI team capable of implementing and managing third-party models.

Level 2: Structured Implementation

Description: Systematic use of AI, including the ability to tweak and customize third-party models.
Requirements:
  • Defined AI Strategy: A formal strategy that includes leveraging third-party models with custom tweaks to fit organizational needs.
  • Model Tuning: Basic competency in tweaking parameters of third-party models for better performance.
  • Intermediate Training: Employees receive advanced training on model tweaking and integration.
  • Standardized Processes: AI model implementation processes are standardized across multiple business functions.

Level 3: Advanced Integration

Description: Deep integration of AI with the capability to fine-tune and optimize third-party models for specific business use cases.
Requirements:
  • Widespread AI Adoption: Extensive use of fine-tuned models across various business functions.
  • Model Fine-Tuning: The organization possesses advanced skills in fine-tuning pre-trained models to meet specific business requirements.
  • Specialized AI Teams: Teams dedicated to different AI specialties, including model fine-tuning and performance optimization.
  • Advanced Data Utilization: Effective use of data for model fine-tuning and optimization.

Level 4: Industry Leadership

Description: Leadership in AI with the ability to train custom models and set industry benchmarks.
Requirements:
  • Innovative AI Solutions: Development of innovative AI solutions, including custom-trained models that set industry standards.
  • Model Training: The organization is proficient in training models from scratch or building on existing architectures.
  • AI Ecosystem: Active participation in the AI ecosystem, including collaboration with AI model providers, research institutions, and industry bodies.
  • Thought Leadership: Regular publication of AI research, including custom model development and case studies.

Level 5: Research & Advanced AI

Description: Operating at the forefront of AI research, with expertise in creating and advancing AI models.
Requirements:
  • R&D Excellence: Involvement in cutting-edge AI research, including the development of new AI models and architectures.
  • Custom AI Models: Expertise in creating highly sophisticated AI models tailored to specific advanced use cases, including autonomous systems and AGI.
  • AI Innovation Labs: Dedicated research labs focused on model innovation and the creation of next-generation AI technologies.
  • Global Recognition: Global recognition as a leader in AI model research and development, contributing to the advancement of AI knowledge and standards.
 

Our Approach

 
Launchpad
Launchpad
Mission Control
Mission Control
 
Rockets
Rockets
 

Launchpad - Discovery

01. Define Business Roadmap

02. Document System and Data Architecture

03. Data Pipeline

04. AI Roadmap - Moving Forward Plan

Launchpad - Discovery

 
01. Define Business Roadmap
01. Define Business Roadmap
02. Document System and Data Archictecture
02. Document System and Data Archictecture
 
03. Data Pipeline
03. Data Pipeline
 
04. AI Roadmap - Moving Forward Plan
04. AI Roadmap - Moving Forward Plan
 

MAGIC ASCEND

ASCEND: A Dynamic and Actionable AI Readiness & Maturity Assessment Framework

Executive Summary

The next two years represent a critical window of opportunity. Organizations that embrace AI will unlock unprecedented value and gain a decisive competitive edge. Those that hesitate risk falling irrevocably behind. ASCEND is an AI readiness and maturity assessment framework to help organizations unlock the potential of AI by evaluating and enhancing their AI capabilities through a dynamic, data-driven approach.

Mission

To empower organizations to navigate the complexities of AI adoption and unlock transformative potential of GenAI by providing tailored, actionable insights and strategies.

Why is AI not just an option, but a necessity?

In today's dynamic market, adaptability is paramount. AI is no longer a futuristic vision; it's the key to unlocking agility, driving intelligent decisions, and responding to evolving customer needs in real-time.

Market Data

According to Gartner, only 53% of AI projects make it from prototype to production, underscoring the critical need for structured, actionable guidance in AI adoption.

The Challenge: Navigating the Path to AI Value

Complexity. While the need for AI is clear, the journey towards successful adoption is complex. Legacy systems, fragmented data landscapes, and a shortage of specialized talent present significant roadblocks.
Competition. Adding to the urgency, competitors are rapidly integrating AI, widening the gap between early adopters and those slow to adapt. This rush to implement AI often leads to misaligned strategies, wasted resources, and ultimately, unrealized potential. The consequence? A staggering number of AI initiatives fail to deliver tangible business value.
Data Readiness. Organizations face significant hurdles in adopting and scaling AI, including inadequate data readiness, insufficient IT infrastructure, and lack of skilled talent.
Value. Companies struggle with identifying the most impactful areas for investment, managing cultural resistance, and maintaining momentum in AI initiatives.

Solution: ASCEND Features and Benefits

The MAGIC AI Readiness and Maturity Assessment Framework, ASCEND (”Actionable Solutions for Comprehensive Evaluation of Needs and Development”) addresses these challenges through a phased approach:

1. Comprehensive Assessment and Analysis: Seven Critical Dimensions

ASCEND combines quantitative metrics, qualitative assessments, and expert judgment to deliver a personalized AI maturity roadmap, enabling organizations to achieve sustained AI growth and innovation.
1.1. AI Strategy & Alignment: Evaluates the existence and quality of a dedicated AI strategy, executive sponsorship, and alignment with business goals.
1.2. Data Readiness: Assesses data quality, accessibility, governance, and overall preparedness to support AI initiatives.
1.3. IT Infrastructure: Analyzes the capability of existing IT infrastructure to handle AI workloads, including cloud adoption and specialized hardware.
1.4. Code Practices & MLOps: Reviews the maturity of MLOps practices, code quality, and efficiency of model development and deployment pipelines.
1.5. Team & Resources: Evaluates the availability and expertise of AI talent, budget allocation, and resource utilization.
1.6. AI Culture & Change Management: Measures organizational risk tolerance, AI literacy, adaptability, and readiness to embrace AI-driven change.
1.7. AI Governance: Assesses the maturity of ethical considerations, responsible AI practices, and governance frameworks.

2. AI Maturity Index (AIMI)

Dynamic AI Maturity Index (AIMI):
Realized AI Value (Xₙ): This is assessed through a combination of data-driven estimations, historical data analysis, market research, and expert input. Initially, it represents the potential value AI can bring to the organization. In subsequent assessments, it tracks the actual value generated.
Constraints Analysis (1-Xₙ): Identifies the barriers preventing the organization from reaching its full AI potential across seven critical dimensions, such as AI strategy, data readiness, and IT infrastructure. Each dimension is scored and weighted based on its impact on AI growth.
Mathematical Foundations:
The 'r' value, calculated using the formula r = Xₙ / (Xₙ₋₁ * (1 - Xₙ₋₁)), captures the AI growth rate and overall maturity. This dynamic measure helps track progress and forecast future potential.

3. Personalized Roadmap Development

Prioritization of Constraints: Identifies and prioritizes the most significant barriers to AI adoption, guiding organizations to focus on high-impact areas first.
Dynamic Weight Adjustment: Continuously adjusts the importance of each dimension based on evolving organizational needs and external factors, ensuring the roadmap remains relevant and effective.

4. AI Best Practices

The ASCEND framework promotes best practices in AI implementation, including strategic planning aligned with business objectives, executive support, robust data management, MLOps integration, GenAIOps, continuous skill development, and cross-functional collaboration. It emphasizes ethical AI practices, such as bias mitigation and transparency, along with comprehensive risk management covering security, privacy, and regulatory compliance.

5. Research Centered

ASCEND is rooted in continuous research and innovation, leveraging partnerships with academic and industry leaders to stay ahead in AI advancements. It fosters knowledge sharing through conferences and publications and applies insights from pilot projects and prototyping to refine AI strategies and solutions.

6. Actionable Guidance & Tools

ASCEND provides practical tools and resources to streamline AI adoption. It includes pre-configured AI modules for quick deployment and industry-specific best practices to guide implementation. Access to AI experts offers personalized insights, helping organizations effectively address challenges and optimize their AI strategies for maximum impact. This guidance ensures efficient decision-making and successful AI integration.

7. Expert Insights

The ASCEND framework connects organizations with a network of industry experts, AI consultants, and ethics advisors, offering tailored insights and guidance. It provides comparative analysis, best practices, and custom recommendations to support AI governance, data management, and long-term growth.

8. Ongoing Learning, Education, and Adaptation:

The ASCEND framework promotes a culture of continuous learning and adaptation, offering ongoing support and guidance. This ensures organizations stay ahead in the rapidly evolving AI landscape by keeping up with the latest developments and best practices.

9. Integration with Established IT Practices (Additional)

Alignment with IT Management Frameworks: Maps assessment findings and recommendations to specific processes within ITIL, COBIT, and proprietary AI management frameworks, ensuring seamless integration and compliance.
Behavioral Science Insights: Applies behavioral economics principles to motivate AI adoption, highlighting the benefits of action and the potential losses of inaction.

10. Data Governance and Privacy Best Practices (Additional)

Data governance and privacy are critical components of the ASCEND framework, ensuring that AI initiatives are built on a foundation of trust, transparency, and compliance.
Data Governance Framework:
  • Policy Development: Assists organizations in developing comprehensive data governance policies that define data ownership, access controls, and accountability structures.
  • Data Stewardship: Establishes roles and responsibilities for data stewardship, ensuring that data is managed effectively and ethically throughout its lifecycle.
  • Quality Assurance: Implements data quality assurance processes to ensure the accuracy, consistency, and reliability of data used in AI projects.
Privacy and Security:
  • Compliance Standards: Ensures compliance with relevant data protection regulations, such as GDPR, CCPA, and HIPAA, by providing guidance on legal and ethical considerations.
  • Data Anonymization: Employs data anonymization techniques to protect sensitive information while enabling valuable insights from data analysis.
  • Security Protocols: Recommends robust security protocols, including encryption, access controls, and incident response plans, to safeguard data against breaches and unauthorized access.
Ethical AI Practices:
  • Bias Mitigation: Implements strategies to identify and mitigate biases in AI models, promoting fairness and equity in AI outcomes.
  • Transparency and Accountability: Encourages transparency in AI decision-making processes, providing clear explanations and documentation for stakeholders.
  • Stakeholder Engagement: Engages stakeholders, including customers, employees, and regulators, in discussions about data usage and AI ethics to build trust and accountability.

Impact Scenario Examples

High 'r' - The AI Front-runner: A fintech company invests in robust data infrastructure and a data-driven culture, resulting in rapid growth of realized AI value and a high 'r' value, positioning them as an industry leader.
Low 'r' - The Laggard: A manufacturing company hesitates to invest in AI talent and infrastructure, leading to low realized AI value and a low 'r' value, indicating stagnation and competitive disadvantage.

Case Study: Famous Food Retail

Problem: lA leading food retailer struggled with fragmented data infrastructure and lack of AI talent.
Solution: Implemented a data lake solution and upskilled the workforce through hands-on AI training and strategic hires.
Results: Achieved a 15% increase in e-commerce sales and a 30% increase in engagement on customized marketing campaigns.

Quantifiable Metrics

AI Maturity Improvement: Across 10 mid-sized companies, an average increase of 45% in AI maturity scores within the first year.
Cost Savings: Organizations reported an average cost savings of 30% in areas where AI solutions were deployed.

Conclusion

ASCEND’s Impact: By providing a comprehensive, dynamic, and actionable framework, ASCEND enables organizations to confidently navigate their AI transformation journeys, achieving sustained growth and innovation in an AI-driven world.

Project Overview

Project Timeline

Project Investment

We’ve all heard the phrase, “measure twice, cut once.” We believe every successful project begins with a dedicated phase to de-risk, define, and align on a blueprint for success. During the AI Discovery process, we partner with your team through in-person and virtual strategy sessions, documentation, estimation, and defining the technical architecture and major milestones of your roadmap. When this phase is complete, we create an executable plan and can begin your AI journey with confidence that success will be on the other side.

FanHero Creator

FanHero CREATOR is an AI-powered content generation platform designed to automate the creation of courses and other digital content, streamlining the entire content production process. The platform leverages advanced AI models to generate, review, and optimize content according to specific workflows tailored to your needs.
Cost Structure:
  • Implementation: $10,000 per month for 6 months, totaling $60,000.
  • Licensing: $5,000 per month after the initial implementation period.
Included Infrastructure:
  • Customers and Subscribers: 10,000
  • Registered Users: 500,000
  • Digital Channel(s): 50
  • Data Transfer: 100TB
  • Storage: 100TB
  • Encoding: 1,000 hours
  • Transaction Fee: N/A
  • Servers: Included
  • AI GPUs: Not included.
AI Infrastructure Options:
  • Managed Infrastructure: Includes 4 AI GPUs at $2,500 per month, featuring a 1GB link in a data center.
  • Cloud or On-Premises Options: Available based on your specific requirements and preferences.
Additional Infrastructure (Overages):
  • Customers and Subscribers: $0.50/Subscriber
  • Registered Users: $0.01/Registered User
  • Digital Channel(s): N/A
  • Data Transfer: $0.05/GB
  • Storage: $0.05/GB
  • Encoding: $5.00/hour
  • Transaction Fee: N/A
  • Servers: N/A
  • AI GPUs: TBD

Concepta MAGIC ASCEND

ASCEND (Actionable Solutions for Comprehensive Evaluation of Needs and Development) is an AI readiness and maturity assessment framework designed to evaluate and enhance your organization’s capabilities in adopting and leveraging AI technologies. ASCEND provides a comprehensive analysis of your current AI infrastructure, processes, and talent, offering actionable insights and a clear roadmap to achieve your AI goals.
Cost Structure:
  • Initial Discovery Phase: $50,000 for a detailed assessment and recommendations.
Key Features:
  • AI Readiness Assessment: Evaluates the current state of AI within your organization, including existing capabilities and gaps.
  • Maturity Model: Categorizes your AI maturity across six levels, from basic adoption to advanced AI research and development.
  • Customized Roadmap: Provides a strategic plan to enhance AI capabilities, including actionable steps for improvement.
  • Expert Consultation: Ongoing support and expert advice to ensure successful implementation of the roadmap.
ASCEND is designed to empower your organization with a clear understanding of where you stand in your AI journey and how to accelerate your progress, ensuring a strategic and successful AI adoption.

Discovery Deliverables

Define Business Roadmap

A strategic path to align data with future AI initiatives, ensuring sustainable growth and competitive advantage.

Data Architecture

A robust data infrastructure to support seamless integration and efficient data flow across departments.

Data Pipeline

Establishing a streamlined process for data selection, collection, migration, and transformation, for Reporting and AI

AI Roadmap

Outlining a clear, actionable strategy for AI adoption and evolution, driving innovation and operational excellence.

Timeline & Investment

Discovery Acceptance

 
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