Engagement Model

At Numanic, we specialize in Advanced Data Analytics and AI solutions that empower organizations to make intelligent, data-driven decisions. Understanding that every business has unique challenges and objectives, we design and implement customized end-to-end AI and analytics solutions that drive efficiency, enhance decision-making, and create new opportunities for growth.

Key Elements of Our Engagement Model

Strategic & Consultative Approach

Our engagements begin with a thorough assessment of our client’s business objectives, data ecosystem, and AI readiness. We work collaboratively with stakeholders to define key performance indicators (KPIs) and develop AI and data strategies that align with business goals and deliver measurable impact.

Typical AI & Data Analytics Engagement Stages

1. Discovery, Needs Assessment, and Proof of Concept (PoC)

Identify key business challenges, opportunities, and objectives for AI and data-driven decision-making.
Assess existing data infrastructure, availability, and AI readiness.
Engage with key stakeholders across the organization to align AI and analytics initiatives with business needs.
The PoC engagement typically lasts between 4 to 6 weeks, during which we conduct an in-depth evaluation and provide a comprehensive assessment and detailed implementation roadmap at the conclusion of the phase.

2. Data Preparation & Engineering

Collect, clean, and structure data from diverse sources to ensure quality, accuracy, and usability.
Develop scalable data pipelines and integrate various data sources to facilitate seamless analytics and AI model development.

3. AI Model Development & Advanced Data Analytics Solution

Apply cutting-edge machine learning (ML), deep learning, and advanced statistical and optimization techniques to extract meaningful insights.
Build predictive models, recommendation systems, and AI-driven automation solutions tailored to business needs.

4. Visualization & Business Intelligence Reporting

Develop interactive dashboards and comprehensive reports to transform complex data into actionable insights.
Enable intuitive data storytelling with visualizations customized for key business users and decision-makers.

5. Deployment & Integration

Implement AI and analytics solutions into production environments, ensuring seamless operation and scalability.
Integrate AI models and analytics tools with existing business applications, such as ERP, CRM, and cloud platforms.

6. Monitoring, Optimization & Continuous Improvement

Track AI model performance, refine algorithms, and optimize analytical models based on real-world usage.
Provide ongoing support, continuous model retraining, and adaptation to evolving business and market conditions.

Tailored AI & Data Analytics Services

Numanic offers a comprehensive suite of Data Analytics and AI consulting services. Please look at our detailed service offerings here.

Flexible Engagement Models

We offer flexible engagement models tailored to different business needs, levels of AI maturity, and budget considerations:

1. Project-Based Engagement

A structured AI or analytics project with clearly defined objectives, deliverables, and timelines.
Best suited for proof-of-concept (PoC) initiatives, AI pilot programs, and targeted analytics projects.

2. Retainer Model

Ongoing access to AI and data analytics expertise for continuous support, optimization, and advisory services.
Ideal for businesses with evolving AI needs that require recurring analysis, AI model enhancements, and strategic consulting.

3. AI & Analytics-as-a-Service (AIaaS / DaaS)

Subscription-based access to pre-built AI models, dashboards, and automated analytics reports.
A cost-effective solution for companies seeking scalable AI and analytics capabilities without heavy infrastructure investments.

4. Managed AI & Data Services

Fully outsourced AI and data analytics function, including data management, AI model deployment, monitoring, and compliance.
Designed for organizations seeking a comprehensive AI and analytics solution without the need for in-house expertise.

Key Considerations for Choosing an Engagement Model

When selecting the right engagement model, businesses should evaluate:

AI & Data Maturity – The current state of AI adoption and data infrastructure within the organization.
Project Scope & Complexity – Whether the initiative is a one-time project, a long-term strategic AI deployment, or requires continuous evolution.
Budget & Resource Allocation – Financial constraints and the level of in-house AI and data analytics expertise.
Integration & Scalability Needs – How AI and analytics solutions will integrate with existing business processes and scale over time.
Regulatory & Ethical Considerations – Compliance with AI ethics, data privacy laws, and governance frameworks.

Example of a Hybrid AI & Data Analytics Engagement Model

Phase 1: Project-Based AI & Analytics Implementation

A retail company engages Numanic to develop a customer segmentation model using advanced machine learning and analytics. The project delivers:

A detailed report with key customer segments, buying patterns, and predictive insights using AI models.
An interactive BI (Business Intelligence) dashboard for real-time data exploration and decision-making.

Phase 2: Ongoing Retainer for Optimization & Expansion

Following the initial implementation, the company transitions to a retainer-based engagement, enabling:

Continuous model refinement based on new customer behavior data.
Ongoing data analysis to uncover emerging trends.
Strategic advisory services to help scale AI-driven marketing and customer engagement strategies.

Please look at a reference implementation here.