PRODUCT / INTERNAL BUILD

FolioLytics — AI-Powered Portfolio Analysis

An AI-powered portfolio analysis platform integrating market-data feeds and brokerage APIs to provide portfolio analysis through a modern Next.js and Python architecture.

PythonNext.jsMarket data APIsKite / Groww integrationsPostgreSQL

Confidential client names are omitted. Interfaces are representative product UIs and architecture from systems designed in this practice.

FolioLytics portfolio analysis dashboard with performance and holdings

Project overview

FolioLytics turns brokerage and market feeds into a portfolio the user can read: value, allocation, performance and an AI layer that comments on the book — not on a random ticker.

Business challenge

Finance side-projects often stop at a chart. The useful system is identity, holdings sync, and insights that respect the actual book.

Solution architecture

Python for ingest and analysis. Next.js for the portfolio UI. Broker APIs for holdings. A constrained AI summary over computed metrics.

Ingest → normalize holdings → compute performance → optional AI narrative. The model does not invent positions.

FolioLytics — AI-Powered Portfolio Analysis
Broker APIsKite / Groww
Python servicesNormalize · compute
Portfolio UINext.js
AI explains the book. It does not become the book.

Technology stack

PythonNext.jsMarket data APIsKite / Groww integrationsPostgreSQL

Key features

Holdings sync

Broker-linked positions.

Performance & allocation

Value and mix the user can audit.

AI commentary

Narrative on computed metrics.

Modern web UI

Next.js, not a desktop terminal only.

My role

Product designPython servicesNext.js UIBroker APIsAI insightsData model

Implementation

Shows financial-data + AI + full-stack range next to the telecom portfolio. See also the FolioLytics blog note on this site.

Results / capabilities

  • Feed-backed holdings
  • Performance the user can check
  • AI as commentary, not a black box book

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