Pension AI

The problem

Retirement calculators come in two flavors: oversimplified single-slider toys, or spreadsheets with forty tabs that nobody maintains. Neither explains why your trajectory looks the way it does, and neither answers follow-up questions like 'what happens if I retire three years earlier?'

What I built

A web app combining Monte Carlo portfolio simulation (10,000 market paths) with an LLM advisor that interprets the distribution, stress-tests scenarios, and answers natural-language questions. Users input their current savings, contribution rate, target retirement age, and asset allocation — the engine returns a trajectory with a p10/p50/p90 confidence band and a plain-language narrative.

Architecture

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  Next.js frontend

  App Router, server components, charting via recharts

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  Simulation engine

  Python worker running Monte Carlo with configurable return distributions, inflation, sequence-of-returns risk, spending-shock scenarios

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  Scenario storage

  PostgreSQL persists every user scenario and simulation run for longitudinal comparison

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  RAG advisor

  Ollama-hosted model with injected context (user inputs, simulation results, asset-class priors)

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  Auth

  NextAuth v5 with Prisma adapter

Tech stack