If you're experiencing declining market share, inefficiencies in your operations, here's how I can help:

Marketing & Client Experience

GraphRAG: Models customer-product relationship networks for next-best-action predictions
DSPy: Optimizes cross-sell/upsell prompt variations through A/B testing

Risk & Audit

GraphRAG: Maps transactional relationships into dynamic knowledge graphs to detect multi-layered fraud patterns
Tool Use: Integrates fraud detection APIs, anomaly scoring models, and regulatory compliance checkers
DSPy: Optimizes fraud explanation prompts for regulatory reporting
Explainable AI: Intuitive visualization to help stakeholder understand model risk and flaw

Objective: Persona-Driven Financial Product Recommendations: Unlock Competitive Advantage & Feature Innovation

Retrieval: Public Product Data using Tavily Search
Recommend: Competition Product

benefits

remove friction in research, saving labour time
improve insight quality by identify competitor

Verbatim

Product

Examples

Companies in competitive industries are constantly under pressure to innovate—but often face the same challenge:

📉 Pain points:

Unable to identify gaps or opportunities in competitor products in real-time.
Lack of insight into customer feedback on competitor features.
Difficulty in predicting how new features will be received in the market.

🧩 The real question:

How can your product stay ahead of the competition without a clear understanding of what features your competitors are developing, and how they’re performing with customers?

🎯 The customer need:

What businesses really need is a data-driven approach to competitor product research, one that can identify trends, uncover feature gaps, and provide actionable insights to drive innovation in product development.

✅ Solution: Competitor Product Research for Feature Development

By leveraging AI, market intelligence, and competitive analysis tools, you can track competitor launches, analyze user sentiment, and evaluate feature performance across the board. This insight helps shape strategic product decisions—ensuring your team isn't building in the dark.

📌 Real-world use cases:

Spotify tracks competitor music features, leveraging insights from users and music trends to introduce features like playlist sharing and collaborative playlists—leading to increased user engagement.
Apple regularly conducts competitor analysis to anticipate and outpace trends, such as implementing health tracking features before they became mainstream in wearables.
Slack uses competitor research to build features that cater to the evolving needs of remote teams, like advanced search functionality and integrations with other tools.

💡 Business benefits:

Informed product decisions: Develop features that fill gaps and add value in ways competitors aren’t addressing.
Faster time-to-market: Avoid reinventing the wheel by learning from competitors’ successes and mistakes.
Market positioning: Stay one step ahead of competitors, ensuring your product remains the best solution for your target audience.

With the right competitive research, you don’t just react to the market—you anticipate it.

Objective: Develop a Targeted Marketing Plan Aligned with Customer Personas

Reasoning from context, answering the question

Context

A business model is not merely a static description but a dynamic ecosystem defined by five interdependent pillars:

Value Creation (What you sell): The core offering must solve a critical pain point or unlock untapped demand. This is the foundation of your value proposition—quantifiable (e.g., cost efficiency) or qualitative (e.g., exceptional user experience)—that differentiates you in the market.

Delivery Infrastructure (How you deliver): Channels and partnerships must align to ensure seamless access to your offering. For instance, a SaaS company might leverage cloud platforms for instant scalability, while a luxury brand prioritizes exclusive retail partnerships.

Customer Lifecycle Dynamics:

Acquisition: How do users discover you? Channels like organic search (SEO), targeted ads, or influencer partnerships must map to your customer segments’ behaviors.

Activation: Do first-time users experience immediate value? A fitness app, for example, might use onboarding tutorials to convert sign-ups into active users.

Retention: Is engagement sustained? Metrics like churn rate and CLV reveal whether your model fosters loyalty through features like personalized content or subscription perks.

Referral: Do users become advocates? Incentivize sharing through referral programs or viral loops (e.g., Dropbox’s storage rewards).

Revenue Architecture (How you monetize): Align pricing models (subscriptions, freemium tiers) with customer willingness-to-pay. For instance, a niche market might sustain premium pricing, while a mass-market product prioritizes volume.

Cost Symmetry: Every activity—from R&D to customer support—must balance against revenue streams. A low-cost airline, for example, optimizes for operational efficiency to maintain profitability.

Strategic Imperatives for Modern Business Models

Systemic Integration: Ohmae’s “3C’s” (Customer, Competitor, Company) remind us that acquisition channels and value propositions must adapt to shifting market realities. For instance, a retailer might pivot from brick-and-mortar to hybrid models post-pandemic.

Data-Driven Iteration: Use AARRR metrics to identify leaks in the funnel. If activation rates lag, refine onboarding; if referrals stagnate, enhance shareability.

Scalability through Partnerships: Key partners (e.g., tech vendors, logistics providers) can reduce overhead while expanding reach—critical for transitioning from niche to mass markets.

By framing each component as a strategic variable rather than a fixed element, businesses can continuously adapt to disruptions—a necessity in Ohmae’s vision of fluid, customer-first strategy.

Persona

Plan

Examples

Benefits of a Marketing Campaign Generator

Accelerated Campaign Launches Quickly generates tailored campaigns, reducing go-to-market time from weeks to hours.
Improved Targeting & Personalization Uses customer data and behavior to craft messages that resonate with specific segments.

Objective: Transform Personal Pain Points into Actionable Insights with a Dynamic Knowledge Graph Framework

Identify what channel customer prefer

Question

Knowledge Graph

Examples

Example of Customer Profile in Graph

Customer Needs and Pain Points

https://i.postimg.cc/D03Sstqd/knowledge-graph1.png

Accumulated Interaction for the same Customer Needs and Pain Points

https://i.postimg.cc/9ffZQ5pD/knowledge-graph2.png

Benefits of a Knowledge Graph

Smarter Data Relationships Connects siloed data across domains to create a holistic, contextual view.
Improved Search & Discovery Enables semantic search—understanding meaning, not just keywords.
Enhanced Decision-Making Surfaces hidden patterns and relationships for better analytics and insights.
Data Reusability Once created, knowledge graphs can be repurposed across multiple use cases (e.g., search, recommendation, fraud detection).

Objective: Automated PII Data Removal: Proactive Compliance & Risk Mitigation

Peronal Info

PII

Examples

Benefits of Entity Removal

Data Privacy & Compliance Ensures sensitive information (names, emails, phone numbers, etc.) is anonymized to comply with GDPR, HIPAA, or other regulations.
Improved Data Quality Removes noise (e.g., irrelevant names or addresses) to make datasets cleaner and more usable for modeling or analysis.
Enhanced Focus for NLP Models Allows downstream tasks (like sentiment analysis or topic modeling) to focus on content rather than personal identifiers.

Objective: Streamline Customer Insights: Auto-Classify Feedback for Product Optimization

multi class classification, could have multiple label for 1 feedback
fix classification in this use case: online banking, card, auto finance, mortgage, insurance
LLM Judge to evaluate relevancy

Business use case: customer segmentation for ab testing

Acquisition: Behavior cluster, we can predict not only who is likely to click—but who is likely to retain
Activation: segmenting users based on behavioral signals—like browsing activity, time since last engagement, or declining open/click rates.

Customer Feedback separate by ;

Classification & Evaluation

Examples

Benefits of Multi Class Classification

Precision Decision-Making Automates complex categorization tasks (e.g., loan risk tiers, transaction types) with >90% accuracy, reducing human bias.
Operational Efficiency Processes 10,000+ transactions/cases per minute vs. hours manually (e.g., JP Morgan’s COiN platform reduced 360k loan doc hours to seconds).
Risk Mitigation Proactively flags 5+ fraud types (identity theft, money laundering) with 40% fewer false positives than rule-based systems.
Regulatory Compliance Auto-classifies documents for FINRA/SEC audits (e.g., Morgan Stanley uses NLP to categorize 3M+ annual communications into 50+ compliance buckets).

Objective: Proactive Entity Mapping: Clarifying Critical Elements in Complex Call Analysis for Strategic Insight

Graph relationship between entity
summary of the interaction

Content

Entity Resolution

Examples

Example of Call Resolution

Resolution for Clear Picture about Customer Issue https://i.postimg.cc/J4qsDYtZ/entity.png

Companies like RBC, Comcast, or BMO often face a recurring challenge: long, complex customer service calls filled with vague product references, overlapping account details, and unstructured issue descriptions. This makes it difficult for support teams and analytics engines to extract clear insights or resolve recurring pain points across accounts and products.

How can teams automatically stitch together fragmented mentions of the same customer, product, or issue—across call transcripts, CRM records, and support tickets—to form a unified view of the actual problem?

That's where Entity Resolution comes in. By linking related entities hidden across data silos and messy text (like "my internet box" = "ARRIS TG1682G" or "John Smith, J. Smith, and js456@gmail.com"), teams gain a clearer, contextual understanding of customer frustration in real-time.

For example, Comcast reduced repeat service calls by 17% after deploying entity resolution models on long call transcripts—turning messy feedback into actionable product insights and faster resolutions.

The result? Less agent time lost, higher customer satisfaction, and data pipelines that actually speak human.

Objective: Leveraging Human Feedback to Deliver Personalized Content that Proactively Solves Customer Pain Points

replace human with reward/penalty function, you will get RLHF by ranking the solutions

Persona

campaign

Your Personalized Email Campaign Bot

Examples

Persona	campaign

Human Feedback

Which campaign you want to approve?

approved rejected

campaign

Human Feedback for Personalized Content enables brands like Mr. Cooper to analyze customer preferences and pain points, then deliver tailored solutions. By embedding real-time feedback loops, they created personalized mortgage-refinancing videos showcasing individual home equity data and financial goals, resulting in 18% higher engagement and 12% lower churn.

Outcome:

Dynamic content adaptation based on behavioral data (e.g., Hilton Honors’ app reduced booking friction by 40% via predictive analytics)
Proactive problem-solving (e.g., Orangetheory Fitness used workout metrics to boost class attendance to 97%)
52% faster ROI through AI-driven personalization scaling

Ready to turn customer frustrations into loyalty drivers with content that feels personally crafted?

This approach aligns with best-in-class use cases where feedback-driven personalization drives measurable business growth

Objective: Dynamic RBC Product Recommender: Personalize Offers Using Customer Persona Insights

Retrieval: Public RBC Product Data, other massive dataset: customers data
Recommend: RBC Product

free tier hosting system limitation for this use case

cannot use any workable embedding model due to big size
this is not functioning correctly since I just replace embedding with a random matrix.
it will work under normal environment.

Potential Optimization

BM25 reranking using keyword

Verbatim

Product

Examples

Companies pour millions into product catalogs, marketing funnels, and user acquisition—yet many still face the same challenge:

📉 Pain points:

High bounce rates and low conversion despite heavy traffic
Customers struggle to find relevant products on their own
One-size-fits-all promotions result in wasted ad spend and poor ROI

🧩 The real question:

What if your product catalog could adapt itself to each user in real time—just like your best salesperson would?

🎯 The customer need:

Businesses need a way to dynamically personalize product discovery, so every customer sees the most relevant items—without manually configuring hundreds of rules.

✅ Enter: Product Recommender Systems

By analyzing behavioral data, preferences, and historical purchases, a recommender engine surfaces what each user is most likely to want—boosting engagement and revenue.

📌 Real-world use cases:

Amazon attributes up to 35% of its revenue to its recommender system, which tailors the home page, emails, and checkout cross-sells per user.
Netflix leverages personalized content recommendations to reduce churn and increase watch time—saving the company over $1B annually in retention value.
Stitch Fix uses machine learning-powered recommendations to curate clothing boxes tailored to individual style profiles—scaling personal styling.

💡 Business benefits:

Higher conversion rates through relevant discovery
Increased average order value (AOV) via cross-sell and upsell
Improved retention and lower customer acquisition cost (CAC)

If your product discovery experience isn’t working as hard as your marketing budget, it’s time to make your catalog intelligent—with recommendations that convert.

If you're experiencing declining market share, inefficiencies in your operations, here's how I can help:

Marketing & Client Experience

Risk & Audit

Other Links:

Objective: Persona-Driven Financial Product Recommendations: Unlock Competitive Advantage & Feature Innovation

benefits

Companies in competitive industries are constantly under pressure to innovate—but often face the same challenge:

📉 Pain points:

🧩 The real question:

🎯 The customer need:

✅ Solution: Competitor Product Research for Feature Development

📌 Real-world use cases:

💡 Business benefits:

Objective: Develop a Targeted Marketing Plan Aligned with Customer Personas

Benefits of a Marketing Campaign Generator

Objective: Transform Personal Pain Points into Actionable Insights with a Dynamic Knowledge Graph Framework

Example of Customer Profile in Graph

Customer Needs and Pain Points

Accumulated Interaction for the same Customer Needs and Pain Points

Benefits of a Knowledge Graph

Objective: Automated PII Data Removal: Proactive Compliance & Risk Mitigation

Benefits of Entity Removal

Objective: Streamline Customer Insights: Auto-Classify Feedback for Product Optimization

Business use case: customer segmentation for ab testing

Benefits of Multi Class Classification

Objective: Proactive Entity Mapping: Clarifying Critical Elements in Complex Call Analysis for Strategic Insight

Example of Call Resolution

How can teams automatically stitch together fragmented mentions of the same customer, product, or issue—across call transcripts, CRM records, and support tickets—to form a unified view of the actual problem?

The result? Less agent time lost, higher customer satisfaction, and data pipelines that actually speak human.

Objective: Leveraging Human Feedback to Deliver Personalized Content that Proactively Solves Customer Pain Points

Outcome:

Ready to turn customer frustrations into loyalty drivers with content that feels personally crafted?

Objective: Dynamic RBC Product Recommender: Personalize Offers Using Customer Persona Insights

free tier hosting system limitation for this use case

Potential Optimization

Companies pour millions into product catalogs, marketing funnels, and user acquisition—yet many still face the same challenge:

📉 Pain points:

🧩 The real question:

🎯 The customer need:

✅ Enter: Product Recommender Systems

📌 Real-world use cases:

💡 Business benefits: