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Advisory Engagement

Fintech Advisory: a data-first internal tool that surfaces opportunities

An investment advisory with five data sources, a growing book, and a team spending days each week assembling spreadsheets by hand. I built an internal tool that unified the data and did the assembly for them.

What changed

Manual Data Work
0%
Reduction (in hours spent per week)
Automated Data Coverage
0%
Of All Systems Data Now Automated
Data Transformed
0M
Rows of data transformed on-demand
Executive Adoption
0%
Team using internal tooling

The Problem

"We waste hours per week manually assembling data, performing analysis, and building proposals. We need a solution that automates the data work and provides us what we need to make decisions and sell more"

Pain Points:

  • •5 different data sources
  • •Hours to transform data and build reports
  • •All opportunities are manually identified
  • •Not a system that can scale with the business

The Solution

Proposal & Opportunity Workbench

Core Features:

  • •Unified portfolio data model
  • •Automated opportunity detection
  • •One-click analysis and reporting
  • •MVP tool that saves time and can scale with the team

4-Week Build Sprint

Week 1: Data Modelling

Consolidated data sources and formats into consistent schema covering positions, cost basis, risk profiles, and strategy preferences. Established single source of truth through well-defined data models and transformations.

Week 2: Opportunity Engine

Built detection algorithms for rebalance triggers, strategy compliance, and intelligent recommendations.

Week 3: MVP & Analytics

Shipped the internal tool: automated data assembly, analysis views, and the opportunity cards the team uses to decide what to propose

Week 4: Adoption & Training

Created advisor runbook for structured note entry. Trained team on using the system. Outlined next work.

Data quality, before and after

Portfolio Data Accuracy95%
Proposal Automation Rate85%
Opportunity Detection Coverage92%
Dashboard Adoption (Exec Team)100%

Tech Stack

Data Pipeline

  • File ingests → Postgres
  • Lightweight Supabase backend with auth, storage and transformation functions
  • Unified models and reporting frameworks

Front End

  • Unified front end for data analysis and reporting
  • Auth and security for the team
  • Interactive charts, tables and opportunity cards

Engagement Model

Signals DiagnosticSpine Build SprintFractional Steward

Started with diagnostic to map opportunities, built the spine in 4 weeks, then moved to monthly build work for ongoing stewardship.

Have a similar problem?

A short fit call first. If a diagnostic is the right next step, it ends with a written 90-day plan.