Delivered engagementFintech · $25k – $35k
Anar

AI financial assistant for NRIs

Delivered engagement. Coralsoft designed and built this product end to end; figures below retain their evidence level.

WebBackendNext.js 15 App RouterReact 19Tailwind CSS v4
anar-ai-financial-assistant-nri.example / dashboard
Anar chat interface with conversation history and task tracker sidebar
The situation

What was in place before we started.

A production AI financial assistant for Non-Resident Indians navigating NRE/NRO banking, FEMA rules, tax-treaty questions, mutual funds and long-term planning — built as a full-stack platform with a streaming chatbot, RAG knowledge pipeline, personalisation, task tracking, billing and admin.

Anar is a production AI financial assistant created for Non-Resident Indians who need clearer guidance across cross-border financial decisions. The platform supports users dealing with NRE/NRO banking, FEMA-related questions, tax-treaty considerations, mutual funds, and long-term investment planning — providing a conversational interface that delivers contextual answers and turns recommendations into practical next steps.

The client needed a scalable AI product that could support complex financial conversations while remaining secure, fast, and production-grade. The challenge was unifying multiple product layers into one coherent system: a streaming AI chatbot, a retrieval-augmented knowledge base, user personalisation, task tracking, onboarding, billing, and admin workflows — on a data architecture with protected user access, vector search, and headroom to grow as the corpus and user base expand.

Constraint

The platform handles sensitive cross-border financial data — NRE/NRO accounts, FEMA rules, tax-treaty implications — so every product surface had to ship with Row-Level Security, personalisation, and audit-grade access control from day one, not as a hardening pass later.

Timeline
6 months reconstructed
Team
5 people reconstructed
Budget
$25k – $35k reconstructed
Platforms
2
Stackrecorded
Next.js 15 App RouterReact 19Tailwind CSS v4TanStack Query v5Next.js Route HandlersNode.js runtime (chat pipeline)Edge runtime (KB retrieval)Supabase Postgres + RLSpgvector (cosine similarity)Supabase AuthTypeScriptOpenAI GPT-4oRAG pipeline + vector search
Challenges

Each constraint, and what we did about it.

Constraint

The platform handles sensitive cross-border financial data — NRE/NRO accounts, FEMA rules, tax-treaty implications — so every product surface had to ship with Row-Level Security, personalisation, and audit-grade access control from day one, not as a hardening pass later.

What we did

The frontend is built with Next.js 15 App Router, React 19 and TanStack Query v5 for client-side fetching, caching and optimistic UI. The API layer uses Next.js Route Handlers split across runtimes — Node.js for the chat pipeline (longer-running tool-rich requests) and Edge for fast knowledge-base retrieval close to the user.

Results

Outcome, with the source of every figure.

25+
Supabase tables with Row-Level Security across user-owned data
self-reportedCoralsoft's own figure for its own delivery, stated in cases.ts. No third-party attestation in the sources.
6
Product surfaces unified — chat, knowledge base, personalisation, tasks, billing, admin
self-reportedCoralsoft's own figure for its own delivery, stated in cases.ts. No third-party attestation in the sources.
0→1
Greenfield full-stack AI delivery — secure, scalable, production-ready
self-reportedCoralsoft's own figure for its own delivery, stated in cases.ts. No third-party attestation in the sources.
Showcase

How the system fits together.

4 of 9 views
anar-ai-financial-assistant-nri.example
Anar admin Knowledge Base management view with versions and uploads
anar-ai-financial-assistant-nri.example
Anar admin guardrail rules controlling AI safety and behaviour
anar-ai-financial-assistant-nri.example
Anar — product screenshot
anar-ai-financial-assistant-nri.example
Anar — product screenshot
Roadmap

How it was sequenced.

6 monthsreconstructed delivery
Weeks 1–4

Discovery & architecture

Real-time conversational assistant for complex cross-border financial questions, returning contextual answers grounded in retrieved knowledge — not generic LLM output.

  • Streaming AI chatbot
Weeks 5–13

Core build

GPT-4o connected to a structured financial knowledge base via pgvector cosine similarity search — every answer is anchored to retrievable, citable source material.

  • RAG knowledge pipeline
Weeks 14–18

Integrations & data

User context (residency, life stage, account types, investment goals) is woven into retrieval and prompting so recommendations are relevant to each individual situation rather than generic.

  • Personalisation layer
Weeks 19–23

Hardening & QA

AI-generated recommendations are converted into concrete action items the user can work through over time — bridging the gap between financial advice and real-world execution.

  • Task tracker
Weeks 24–26

Launch & handover

Internal management layer for platform operations, knowledge-base content, user-related workflows and onboarding configuration — content updates ship without engineering involvement.

  • Operator admin panel

More work like this.

Every case study is a Coralsoft delivery story, with the evidence behind each figure kept visible.