ProMom AI-empowered
Capabilities Ideas

Product

Text Analytics Engine

Sentiment Analysis

Topic Modeling and
Fine-Tuning

Personalized Product Recommendation

BellySafe

Risk Assessment and
Health Condition Awareness

Recipe Recommendations

Alternative Suggestions

Cultural Awareness

Nutrient Breakdown

User Feedback Loop

Personalized Recommendations
(foods rich )

Trimester-Specific Needs

Morning Sickness Relief

Craving Management

Dietary Restrictions & Aversions

Pregnancy-Safe Consumption

Caffeine and Alcohol Intake
(Moderation or alternatives)

Hydration Support
(weather, activity level, and stage)

Nutritional Guidance and Education

Balanced Meal Planning
(all necessary nutrients for mom and baby)

Supplement Recommendations

Engagement and Support

Calendar

Menstrual Cycle and Ovulation Prediction

Fertility Window Identification

Mood Tracking and Symptom Prediction

Anomaly detection and Recommendation

Exercise

Lifestyle

Expert

Text Analytics Engine

Expert Recommendation

User -Expert Matching(Rec Algorithms)

User Feedback , Rating and Sentiment Analysis

Language Preference ,communication method

Automatic appointment setting

AI-powered chatbots

Sentiment Analysis

Tracker

In case of potential food risks
Offer alternative options with similar nutritional benefits

e.g. ...in case of gestational diabetes suggest monitoring
sugar intake and considering portion control

Related to the menstrual cycle or general recom

e.g. Craving sweets: Recommend fruit smoothies or yogurt parfaits.

Depends on Crawling Tools and quality and size of the crawled data:
- Min 3 weeks, Max 3 months in case of bad data
- 1 Data Analyst
- Storage: ?

5 wdays

Suggest foods and drinks known to alleviate nausea and morning sickness

- Min 4 weeks, Max 2 months
- 1-2 Data Scientist
- RAM 64 GB to 128 GB
- 200 GB to 500 GB for model checkpoints and
datasets(1 TB or more for storing fine-tuning
datasets and model checkpoints)
- GPU A100

Essential nutrients for fetal development

Pre-existing allergies, intolerances,
and pregnancy-induced food aversions

Pregnancy-safe smoothies, banana bread with whole
wheat flour (better for blood sugar control), or frozen banana "nice cream"

Product

Criteria
Generation

Research/Base Model with Sample Data (Min 10 days)

Setting and Set up ML Modeling Environment

Data Preparation
Product descriptions, reviews,
and metadata from Amazon

Crawling

Cleaning and Preprocessing

Modeling

Unsupervised

LLM

Criteria Extraction

Criteria Representation

Fine-Tunning & Evaluation

Traditional

Semi supervised

Integration, deployment, scalability

Test

e.g. In some cultures, bananas are eaten unripe or cooked,
system can offer information on safe preparation methods for pregnant women.

Determine the most fertile days.
Recs for increase the chances of conception

- ~ 10 wdays
- 2 Researcher/Data scientist

Prediction and personalized notifications

Integration with Expert,
Personalized Recommendations
(foods rich ) , Balanced Meal Planning,
and Pregnancy-Safe Consumption

PEFT Methods

Additive
Fine-Tunning

Adapter-based

Adapter Design

Multi-task Adaptation

Soft Prompt-based

Soft Prompt Design

Training Speedup

Reparameterized
Fine-Tuning

Low-rank
Decomposition

LoRA Derivatives

Dynamic Rank

LoRA Improvement

Multiple LoRA

Selective
Fine-Tunning

Unstructural Masking

Structural Masking

Product

Topic Modeling

Research/Base Model with Sample Data

Setting and Set up ML Modeling Environment

Data Preparation
Product descriptions, reviews,
and metadata from Amazon

Crawling

Cleaning and Preprocessing

Modeling

Unsupervised

LLM

Traditional

Semi supervised

Integration, deployment, scalability

Test

Plan

Subtopic

Litterateur Review
R&D

Data Gathering

Crawling Feasibility

Elastic

Data

Amazon Ready to Use

Amazon Adver API

Amazon Website

LLM Modeling and Benchmark

Models and Code

BERTTopic

GCP VM Instance

Deployment

LLMs Tasks

Language Modeling

Text Generation

DataOps

Data Pipeline Orchestration
Managing the flow from data ingestion
to storage and processing.

Data Sorces

1. Base data: User, Expert, ...tabular , text , geospatial

Product

2. Product Meta , Details, Categories

3. User Reviews

4. User clickstream data, system log, user preference

5. QA interactions (Bellysafe, Expert, ...) Text-based data.

6. Calendar Tracking: Time series data (appointments, events, etc.).

7. External, Knowledge_Base

Ingestion Layer

Batch ingestion: large data dumps like historical product data or user logs

Real-time ingestion: For handling QA, user chats , calendar event updates

Data Storage / Data Lake

AWS S3: suitable for building a data lake.

OpenSearch: If search and retrieval are key functions, OpenSearch (for analytics) can be used in combination with a data lake.

Version Control & Collaboration: Managing different versions of data, changes in ML models, and collaboration among team members.

Monitoring and Quality Assurance: Ensure data quality, detect anomalies, and apply corrective actions.

Security & Compliance: Ensuring data privacy (important for health-related apps) and compliance with regulations like HIPAA.

Product Use Cases

Product Similarity/relevancy

Rrecommendation

Category Rec

Popularity_based

Personalized

Product Basket

Customers Say

Summarization

Sentiment Analysis

Attributes/Tag generation

AI Criteria Tags

Search + Result Page

Filter + Result Page

Search

Semantic

FullText