Hyper-Personalization at Scale with AI

Convert your Reactive Apps into Proactive Hyper-Personalized Services
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TECHNOLOGIES

BizML

Combines Domain Expertise and Feature Engineering to create a feedback loop that increases accuracy (up to 20%) and reduces error rates (up to 50%).

Designed for Business Leaders.

Patent Pending
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Personalizer

Language-driven user targeting and content generation.

Integrates with BizML to increase precision and reliability (e.g. eliminate/reduce hallucinations) of interactions.

Massively scalable.

Patent Pending
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Agent

Integrates with BizML, Personalizer, Data Sources and Tools.

Can play a passive role – Responder. Also play an active and collaborative role – Assistant, Copilot, Autonomous Agent.

Accelerate development

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solutions

Investing & Wealth Management

Product Personalization

Asset Analytics

Analyze individual assets for investment performance(return, volatility, risk-adjusted return) and deliver personalized alerts and recommendations.

Portfolio Analytics

Analyze portfolio investment performance (return, volatility, risk-adjusted return) and make recommendations on investment strategies, portfolio rebalancing, etc.

Usage Recommendations

Advise users on current tool usage, make prescriptive recommendations (how to use the tool better) and predict outcomes.

Credit Cards & Loyalty

Personalizing Customer Engagement

Realtime Cardholder/Account Owner Buyer Behaviour Modification

Easily Influence Buyer Behaviour using Semantic Brain

Get in touch

Effortlessly optimize with Semantic Brain

Card Onboarding

Card Offers

Digital Activation

Travel

Benefits & Rewards

Cross-sell/Up-sell

Card Controls

Augment LLMs & Agents

Create your own foundation model

This is the most expensive and time-consuming solution. While state-of-the-art models often require $100M+ in investment, smaller models can be developed for much less.

This solution isn’t suitable for data changing over time.

Recommended use:

If you need highly specialized AI for proprietary tasks and can invest in data and compute.

Fine-tune a foundation model

Fine-tuning is the process of retraining a foundation model on new data. This is the second most expensive solution(but cheaper than much cheaper training models from scratch).

This solution isn’t suitable for data changing over time.

Recommended use:

When domain-specific performance is required, but you can leverage pre-trained models as a foundation.

Prompt Engineering

Quickly leverage pre-trained models without modifying them, using strategically crafted inputs to get the desired output.

Recommended use:

Ideal for maximizing pre-trained model output without modification, especially for less complex tasks.

Retrieval Augmented Generation(RAG)

Provide real-time, factually accurate information by combining a pre-trained language model with an external knowledge base.

Recommended use:

Is the go-to for combining real-time or proprietary knowledge with pre-trained models to enhance accuracy and context.

Semantic Brain’s Personalizer

Combine Analytics with Prompt Engineering and RAG to deliver personalization and optimization.

Recommended use:

For tasks requiring personalization or optimization.

risk mitigation

Why should I trust your solution?

Technical

Proof

BizML combines Domain Expertise with Feature Engineering to deliver increased accuracy(up to 20%) and reduced error rates(up to 50%).

BizML is used to steer LLM(i.e. reasoning handled by BizML instead of LLM).

Proven with 7 customers and over 10 projects

More precise steering

Eliminate/reduce hallucinations

Faster

Cheaper

Business

Confidence

20 years of experience in Interaction personalization for Finance and Telecom.

Customer owns data and continues to grow domain expertise.

Proven in finance and telecom

20+ years of expertise

Data ownership assured

Continuously evolving domain expertise