Centralised client and portfolio management
Client financial products, household relationships and portfolio records are managed in one platform.
Confidential digital product case study
Sugamta
A unified financial advisory platform for client portfolios, payment schedules, alerts and AI assisted document extraction.
A financial advisory and insurance services organization required a unified digital platform to manage client financial products, including insurance policies, investment plans, and post office schemes. The organization was handling multiple clients, each with diverse financial instruments such as LIC policies, recurring deposits, savings schemes, and other investments.
Project classification: This project showcases a unified financial advisory platform for portfolios, documents, payment schedules and client follow up.
Reduced repetitive document data entry
AI assisted extraction reduces repeated manual entry from policy and financial documents.
More reliable payment and maturity tracking
Generated payment and maturity schedules support more reliable due date tracking.
01 / Business context
Who needed the solution
A financial advisory and insurance services organization required a unified digital platform to manage client financial products, including insurance policies, investment plans, and post office schemes. The organization was handling multiple clients, each with diverse financial instruments such as LIC policies, recurring deposits, savings schemes, and other investments. These were being tracked manually or through fragmented systems, leading to inefficiencies in monitoring payments, managing client data, and delivering timely services. Advisors needed a centralized solution that could: • Manage multiple clients and their financial portfolios • Track upcoming EMIs, premiums, and maturity timelines • Handle complex scenarios like joint holders and nominee structures • Provide quick insights and alerts for actionable decisions
02 / Challenge
What needed to change
The key challenge was the absence of a structured and scalable system to manage financial plans and payment schedules efficiently. The organization faced multiple operational issues: • Lack of centralized visibility of client financial data • Difficulty in tracking upcoming EMIs and payment deadlines • Manual errors in maintaining policy and account information • Inefficient handling of joint holders and secondary clients • No automated alerting mechanism for due payments • Poor user experience due to scattered workflows • Limited scalability for handling growing client data These issues led to delayed actions, reduced operational efficiency, and suboptimal client servicing.
Client financial information was spread across manual recordsLack of centralized visibility of client financial data.
Upcoming EMIs and payment deadlines were difficult to trackDifficulty in tracking upcoming EMIs and payment deadlines.
Policy and account details were prone to entry errorsManual errors in maintaining policy and account information.
Joint holders and nominees created data complexityInefficient handling of joint holders and secondary clients.
Advisors lacked proactive alerting and one portfolio viewNo automated alerting mechanism for due payments.
03 / Workflow transformation
Before and after
Previous workflow
- Client financial information was spread across manual records
- Upcoming EMIs and payment deadlines were difficult to track
- Policy and account details were prone to entry errors
- Joint holders and nominees created data complexity
- Advisors lacked proactive alerting and one portfolio view
Structured workflow
- Extract document data
- Model client portfolios
- Generate payment schedules
- Trigger alerts and dashboards
- Review structured outputs
04 / Solution
How we approached it
While Sugamta is primarily a structured financial management platform, the system incorporates intelligent automation and AI powered data extraction to improve operational efficiency and reduce manual effort. The approach includes: • AI Based Document Data Extraction Integration of Google Gemini via Google Cloud Vertex AI to extract structured information from uploaded documents such as insurance policies and financial records.
Extract document data
Use Gemini to capture structured information from policies and financial documents.
Model client portfolios
Represent instruments, holders, nominees and plan relationships in a normalised schema.
Generate payment schedules
Calculate EMI, premium and maturity timelines from plan configurations.
Trigger alerts and dashboards
Surface upcoming actions and portfolio information to advisors.
05 / Example workflow
From input to reviewable output
1Extract document data
2Model client portfolios
3Generate payment schedules
4Trigger alerts and dashboards
5Review extracted and scheduled data
6Advisor follow up
06 / Delivery scope
What the delivery covered
Advisory workflow
- Client and household onboarding
- Portfolio and policy management
- Payment and maturity scheduling
- Advisor alerts and follow up
AI and data automation
- Gemini assisted document extraction
- Normalised financial product data model
- Schedule computation and workflow rules
- Notification and alert orchestration
Application and cloud engineering
- React interface
- Python and FastAPI services
- PostgreSQL and SQLAlchemy
- AWS and Google Cloud integrations
Scope boundary
The case study focuses on portfolio, document, schedule and alert workflows. It does not claim quantified error reduction or regulated financial advice outcomes.
07 / Architecture and controls
How the system is organised
Google Gemini
React
Python
FastAPI
PostgreSQL
AWS
Google Cloud
Vertex AI
Role and client separation
Advisor and client records are separated through authenticated product workflows.
Document extraction review
Gemini assisted extraction remains reviewable before financial product data is accepted.
Financial decision boundary
The platform supports administration and follow up; it does not replace regulated financial advice.
Alert traceability
Payment and maturity reminders are generated from stored schedules and remain visible to advisors.
08 / Business value
Expected operational value
1Projected
Centralised client and portfolio management
Client financial products, household relationships and portfolio records are managed in one platform.
2Projected
Reduced repetitive document data entry
AI assisted extraction reduces repeated manual entry from policy and financial documents.
3Projected
More reliable payment and maturity tracking
Generated payment and maturity schedules support more reliable due date tracking.
4Projected
Proactive advisor follow up through alerts
Alerts give advisors a structured way to follow up on upcoming payments and events.
5Projected
A scalable foundation for growing advisory operations
The service and data architecture supports additional clients, products and advisory workflows.
09 / Technology
Every component, with its role in the delivery.
Technology choices from the supplied project brief, mapped to the workflow each component supports.
Google Gemini
Structured extraction, reasoning or generation
React
Reviewer or product user interface
Python
AI, data processing and backend logic
FastAPI
Backend APIs and workflow orchestration
PostgreSQL
Relational operational and analytics data
AWS
Cloud infrastructure and managed services
Google Cloud
Cloud hosting and managed AI services
Vertex AI
Managed Gemini and AI model integration
Amazon SES
Transactional email and alert delivery
SQLAlchemy
Database access and data models
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