Case Studies
Case Study
Cybersecurity Risk Quantification (CRQ)
Context
Security Insights and Threat Analytics (SITA) is a B2B platform to empower CISO with tools & insights in enterprise security posture, risk quantification & return on security investments.
Need
- CISOs struggle to connect with Org's Board on security program.
- Organizations lack agility to detect signals early & act swiftly.
- CISO's seek consolidated data & actionable insights.
Data Engg & Analytics
- ETLs to extract events from security products.
- Data Analytic tools to provide actionable insights.
- IP developed based on the data from industry frameworks like NIST / MITRE to quantify security risks.
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Solution
Consulting:
Phase I is about understanding the user needs.
Product Development:
In phase II, product roadmap was planned & MVP1 was released.
Risk Quantification & RoSI:
In phase III, risk quantification and RoSI models were developed.
Case Study
Voice & Accent Neutralization
Context
A leading transportation outsourcing service provider in US had customer support based in Asia. The company’s clients in US often call the support teams with enquiries related to their cargo.
Need
- Support staff from different countries had strong influence of native accents in English.
- This is true for the Customers too as accent changed with geo.
- Our client wanted to reduce the impact through neutralization.
AI Model Training
- After model selection, models were tested for the requirements.
- Of the selected models, we zeroed down on a single model that nearly addresses accent neutralization.
- FSL training is used to further train and optimize the model for accuracy.

Solution
Advisory:
Discussion with our client and their customers to firm up the requirements.
Model Selection:
Identify the models, preferably pre-trained that can address the needs.
POCs:
Build POCs to address the need.
Case Study
Language Translation
Context
A leading Cybersecurity company in Colombia, Latin America with a global client base and operations in Colombia and India was assisting clients from different geographies.
Need
- Its client base includes large Banking, Financial service, and Retail conglomerates in their respective geographies.
- Often, customer support is needed in the local language.
AI Model Training
- Pre-trained models were identified for English to Latin conversions.
- Of the selected models, we zeroed down on a single model that nearly addresses language translation.
- FSL training is used to further train and optimize the model for accuracy.

Solution
Consulting:
Phase I is about understanding the user needs.
Product Development:
In phase II, POC of the product was tested.
Integration:
In phase III, Integration of the product with Teams was tested.
Data, Gen AI & ML – Industry Use Cases
Fintech
Opening savings/current bank account using vernacular language both voice and text-based promptsFintech
Enabling KYC and related checks for user verification and validation required for account operations.Proptech
Real estate price prediction engine based on the historical market prices and the current macro and micro indicators.BPO – Contact Center
Accent conversion (English – US and UK accents) in real time to provide high quality offshore support.BPO – Contact Center
Language conversion in near real time to empower agents serve their overseas clients in local/vernacular language.Product Engineering
Three of our key use cases under product engineering:Case Study (Product Engineering)
B2B SaaS Optimization Platform
Context
CloudNuro.AI is an intelligent SaaS management platform for preventing SaaS sprawl, bringing observability and actionable insights into SaaS applications.
Need
- Organizations usually end up buying excessive SaaS licenses.
- It does have number of unsanctioned SaaS apps and redundant services.
Tech Stack
- Python, Fast API
- MS-SQL
- Google Cloud

Solution
Cloudnuro.ai:
An AI enabled platform is designed for SaaS optimization and observability.
AWS integration:
Samta developed AWS integration module for Cloudnuro.ai.
Case Study (Product Engineering)
B2B SaaS Pricing Prediction Engine
Context
Real Estate Asset Lifecycle Management (REALM) is a cross border investment & management platform to address needs of Non-resident property investors
Need
- Remote property transactions trigger pain and anxiety among investors.
- Leasing and managing property can be time consuming.
Tech Stack
- Python, Django
- React Native & React Js
- PostgreSQL
- NgineX, Gunicorn
- AWS
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Solution
REALM:
Developed a technology platform (mobile and app) to make property transactions transparent, seamless, and cost efficient by leveraging data, technology.