Summary

AI-native Product Manager and Product Engineer with a passion for building and refining products that sit at the intersection of education and fintech. I lead with a deep focus on user experience, company growth, accountability and empathy, owning end-to-end product development from vision and roadmap through execution and go-to-market. With hands-on engineering experience, I bridge technical teams and business strategy to build products that deliver value to users and create impact for the business.

Experience

Selected Projects

• Shopify & e-commerce builds: online stores for publishers, a self-publishing storefront for an independent author, and a holiday/seasonal e-commerce site, built and iterated with Cursor and Claude Code
• AI agent / automation pipeline: Python application (Anthropic SDK, SQLite) running an agentic, multi-step workflow for small-business tasks
• Computer-vision accessibility tool: edge-ML app for real-time object identification; live AI demo at lyndsreed.com

Skills

Education

Certifications

The Problem

Retail traders had data but no insight

Most traders on funded platforms could see their raw numbers but couldn't extract meaning from them. They had no way to identify behavioral patterns driving losses, no psychological tracking, and no personalized coaching. The tools that existed were either too generic or too complex. The gap was clear: an AI-native performance layer built specifically for how traders actually think and fail.

Product Strategy

A dual-purpose platform with a clear market position

TradeSage was designed to function two ways simultaneously: as an embedded journal inside the funded trading dashboard for prop traders, and as a standalone product for retail traders, trading coaches, and trading groups. The mission was to empower traders to understand their performance, identify behavioral and technical patterns, and evolve into consistently profitable traders through clean data visualization and automated AI insights.

The competitive bet was on combining sophisticated AI analysis with social engagement features and seamless broker integrations. These were capabilities that point solutions in the market weren't offering together.

My Approach

End-to-end ownership from blank page to go-to-market

• Conducted discovery through competitive analysis, trader interviews, and support ticket research to define the core problem space
• Defined a 4-phase roadmap spanning rebrand through AI ecosystem expansion, balancing speed to market with product depth
• Prioritized 50+ features across 6 product areas using a Must-Have / Should-Have / Differentiator framework
• Partnered cross-functionally with engineering, design, data science, and marketing to align on scope and sequence
• Oversaw broker integrations, AI coaching layer, analytics pipelines, and psychological performance tracking
• Defined success metrics including 100K MAU target and 70% 90-day retention as north star KPIs

What I Defined

Six core product areas, fully scoped

Outcomes

A fully scoped 0-to-1 AI product

Key Learning

AI products require a fundamentally different validation process

You can't test a recommendation engine the same way you test a button. I invested heavily in defining what "good" looked like for each AI output before any model code was written. In fintech, the trust gap is the product problem. Users must understand and trust AI recommendations before acting on them. That insight shaped every UX and copy decision.

Sample Work

Product Roadmap: Full Strategic Plan

A comprehensive strategic plan for rebuilding, rebranding, and relaunching TradeSage as the leading AI-powered trading journal platform.

Sample Work

Feature Categorization Table

A full breakdown of 50+ features across six product areas, prioritized by Must-Have, Should-Have, and Differentiator tiers.

PM Artifacts

Planning & prioritization documents

Deep-dive documents from the TradeSage buildout.

Strategic Context

TradeSage needed to evolve from an embedded analytics layer into a standalone, market-leading AI trading journal. This roadmap outlines the full strategic arc from rebrand through ecosystem expansion, balancing speed to market with product depth.

Phase Overview

Success Metrics

Full Roadmap

Complete strategic plan

A full walkthrough of the TradeSage product strategy: architecture, phases, monetization, and long-term vision.

Overview

Problem statement

Funded and retail traders generate significant trade data but lack the tools to extract behavioral and performance insight from it. Existing journaling platforms offer raw data views without meaningful synthesis. TradeSage's Journal Dashboard solves this by combining a clean journaling interface with an AI performance layer that surfaces patterns, flags anomalies, and delivers personalized coaching, all in one place.

Goals & Non-Goals

What this PRD covers

User Personas

Who we are building for

User Stories

Core requirements expressed as stories

Success Metrics

How we measure done

Executive Summary

Business case for the relaunch

TradeSage exists as an embedded analytics feature within a funded trading platform. The opportunity is to transform it into a dual-purpose, standalone AI trading journal that competes directly in the trade journal market while retaining its embedded value for prop traders. The relaunch requires a full rebrand, UX overhaul, AI integration, and go-to-market strategy targeting four distinct customer segments.

Business Objectives

What success looks like at the business level

• Establish TradeSage as a recognized, standalone brand in the AI trading journal market
• Achieve 100,000 monthly active users within 12 months of relaunch
• Reach 70% 90-day user retention demonstrating genuine product value
• Generate recurring revenue through a freemium model with Standard ($24/mo) and Pro ($33/mo) tiers
• Capture enterprise and white-label revenue from prop firms and trading education companies
• Build an affiliate program that drives scalable, low-cost user acquisition

Market Opportunity

Why now and why this product

The trading journal market is growing alongside the rise of retail and prop trading. Existing tools are fragmented: some offer analytics, some offer journaling, none offer a unified AI coaching layer. TradeSage's competitive position is the combination of all three plus social engagement features, giving it a moat that point solutions cannot easily replicate.

Stakeholder Requirements

What each stakeholder needs

Constraints & Risks

Known limitations going in

• Phase 1 timeline is 1 to 2 months. Research, design, and rebrand must run in parallel.
• Baseline data access (support tickets, usage counts, revenue) required before KPIs can be formally set
• Broker API integrations are Phase 2. Phase 1 relies on manual data import via CSV.
• AI coaching layer (AI Coach) requires model training data that won't exist until users are active on the platform
• White-label and enterprise features are Phase 3. Early enterprise conversations must be managed against roadmap commitments.

What It Is

An adaptive AI coach that learns how each trader thinks

AI Coach is TradeSage's core AI differentiator: a conversational coaching engine that analyzes a trader's journal data, behavioral signals, and performance patterns to deliver personalized guidance. Unlike static rule engines or generic AI chat, AI Coach adapts its coaching style and recommendations to each user's specific setup, tendencies, and psychological profile over time.

Core Capabilities

What AI Coach does

Technical Approach

How it works under the hood

• LLM integration (GPT-4 / Claude) with structured prompting built on each user's journal data as context
• Behavioral signal pipeline aggregates emotional tags, discipline scores, time-of-day patterns, and setup performance into a user profile
• Anomaly detection layer flags statistical outliers in the user's own data, not generic benchmarks.
• Voice transcription via Whisper API; structured output routed to journal entry via post-processing pipeline
• Unlock gating controlled by a journal history threshold. Minimum 10 sessions before advanced coaching activates.

Edge Cases & Risks

What could go wrong

• New users have insufficient journal history for meaningful AI output. This is mitigated by generic onboarding insights until the threshold is reached.
• LLM hallucinations in financial context are high risk. All AI output is labeled as guidance, not financial advice, with clear disclaimer copy.
• Voice transcription accuracy degrades in noisy environments. Fallback to manual entry is always available.
• AI unlock gating may frustrate power users. The Pro tier bypasses the minimum session requirement.

Success Metrics

Sprint Goal

Establish the research and data foundation for the relaunch

This sprint focused on completing the discovery work required before any design or engineering could begin. The goal was to understand the current user base, define the competitive landscape, and establish baseline metrics so every subsequent decision would be grounded in real data rather than assumptions.

Team & Ceremonies

How the sprint ran

Sprint Planning: Day 1, 2 Hours

How the planning session was structured

The backlog was groomed in the week prior, so planning focused on commitment and clarity, not estimation or debate. Stories were reviewed, questions were answered, and the team left with a shared goal and a clear first action.

User Stories

Sprint backlog

Definition of Done

Acceptance criteria applied to all stories

• All research artifacts documented and shared in Notion before sprint review
• User interview insights synthesized into a findings summary with direct quotes
• Baseline metrics documented in a shared data dashboard accessible to all stakeholders
• Competitive audit delivered as a comparison matrix with clear opportunity callouts
• Brand moodboard reviewed and one direction selected by sprint close

Sprint Summary

Strategic Context

With a broad product surface spanning analytics, AI coaching, psychology, and education, prioritization was critical. This framework organized 50+ features across six areas into three tiers: what had to ship, what should ship, and what would differentiate TradeSage in a competitive market.

Priority Framework

Full Feature Table

All categories and priorities

A complete breakdown of features across Advanced Analytics, Psychology, Education, Replay & Backtesting, AI Coaching, and Prop Readiness.

The Problem

A fast-scaling fintech with no scalable way to train its people

Apex was growing rapidly but training wasn't keeping pace. Knowledge was siloed, onboarding was inconsistent, and there was no systematic way to measure whether training was actually working. New hires took too long to ramp and the company lacked infrastructure to support a growing, distributed team.

My Approach

Treating the LMS as a product, not a project

• Ran discovery across departments to map knowledge gaps and friction points
• Defined platform requirements, managed vendor relationships, drove adoption
• Ran agile ceremonies with instructional designers, trainers, and SMEs
• Built adaptive assessments and interactive modules using TalentLMS, H5P, and Articulate
• Applied learning analytics continuously to identify drop-off points and iterate

Outcomes

Measurable impact across the organization

Key Learning

Learning design is product design

The principles of good product design and good learning design are nearly identical. Both require understanding the user's goal, removing friction, and measuring whether the experience worked. My background in linguistics and instructional design wasn't separate from the PM work, it was the PM work.

Sample Work

AI Knowledge Assistant: Full Case Study

A detailed walkthrough of an AI support training initiative, covering architecture, implementation challenges, and measurable outcomes.

Sample Work

Leadership Development Pathway

A comprehensive approach to building consistent leadership capabilities from front-line supervisors to executive leadership, including program structure, blended learning methodology, and business impact.

Sample Work

Onboarding Redesign: A Success Story

A comprehensive approach to standardizing and optimizing an employee onboarding experience, including AI-powered personalization, cross-functional collaboration, and measurable reduction in onboarding time.

Sample Work

Department-Specific Learning Pathway Audit & Redesign

A strategic audit spanning multiple departments, eliminating redundant content, creating role-based learning journeys, and reducing time-to-proficiency.

PM Artifacts

Planning & Execution Documents

Process artifacts from the Apex LMS buildout: the roadmap, OKRs, and sprint documentation that drove execution.

Strategic Context

Apex was scaling rapidly with no centralized training infrastructure. This roadmap defined the full buildout from platform selection through optimization, structured in four phases to balance speed of delivery with quality of output.

Roadmap Overview

Outcomes by Phase End

Context

These OKRs were set at the start of FY 2024 to align the learning platform's development directly to business outcomes. Each key result was designed to be measurable, time-bound, and tied to a departmental KPI. Progress was reviewed monthly with department heads and reported to leadership quarterly.

Objective 1

Build a scalable, world-class learning infrastructure for a rapidly growing fintech organization

Objective 2

Improve measurable knowledge quality and competency outcomes across all departments

Objective 3

Align organizational learning directly to business performance and measurable outcomes

Sprint Goal

Team & Ceremonies

User Stories

Definition of Done

• All content reviewed and approved by subject matter expert
• Modules built, tested, and validated in TalentLMS staging environment
• Knowledge checks built in H5P, pass threshold confirmed
• Pilot group enrolled in production; notifications confirmed
• Feedback survey live and trigger tested end-to-end
• Sprint review demo completed with department head stakeholders

Sprint Summary

The Brief

An independent author needed a home for her debut novel

Camille Pandian Milner was preparing to launch her debut novel, Climbing Up the Walls, a character-driven story set in London's mid-2000s music scene. She needed more than a basic website. She needed a platform that could tell her story as an author, build anticipation ahead of publication, sell directly to readers, and capture an audience through email, all in one cohesive experience.

What I Built

A fully functioning author platform and e-commerce store

I designed and developed the complete site on Shopify, building a custom experience that goes well beyond a standard storefront. The platform includes:

• A live e-commerce store with multiple product formats, paperback, ebook, and signed edition, each with waitlist capture ahead of the official launch
• An immersive photo journal section showcasing archival images from the London music scene that inspired the novel, creating emotional connection with the book before it ships
• An email capture and mailing list system for early chapters, bonus content, and tour updates
• An author bio and media page for press and interview requests
• A blog and news section for ongoing reader engagement
• Clean, editorial design that reflects the book's atmosphere, London, music, and late nights

Discovery

Starting with the world inside the book

Before touching Shopify, I sat down with Camille to understand what she was actually trying to create. We talked about the book, the themes, the atmosphere, and the specific world of London's music scene in the early 2000s. Late nights, underground venues, the energy of that era. She wasn't just launching a product. She was inviting people into a world she'd spent years building.

From that conversation, the brief became clear: the website needed to feel like the book. It had to carry the same atmosphere. And it needed to function as a real business tool: a store, an email list, a press page, not just a pretty landing page.

Design Decisions

Translating a world into a visual language

The dark background wasn't a trend choice. It was a deliberate reference to the late-night world the book inhabits. The cyan and coral accent palette evokes the neon and warmth of that music era without being literal about it. Serif headlines (Newsreader) give the site literary weight; the clean sans-serif body text (Archivo) keeps it modern and readable.

The photo journal section was a key decision. Rather than just showing the book cover, I built an immersive, zoomable gallery of archival images from the London music scene that inspired the novel. This wasn't decorative. It was designed to make a visitor feel something before they ever clicked "buy."

Product Thinking

Treating an author website as a product, not a brochure

Most author websites are static. They exist to confirm that a book exists. I approached this differently. The site was designed with a clear user journey in mind: arrive, get emotionally invested in the story, join the waitlist, and stay connected through launch and beyond.

Every section serves a conversion goal. The photo journal builds atmosphere and keeps people on the page. The multiple format options let readers self-select by preference. The waitlist captures intent before inventory exists. The mailing list turns one-time visitors into a community the author owns, not one that lives inside an algorithm.

Process

Iterating until it was right

The project ran over about a month, with multiple rounds of revisions. Camille reviewed, gave feedback, and I adjusted. This back-and-forth wasn't friction. It was part of the process. A website for a debut novel needs to feel personal, and getting there required real collaboration. We kept iterating until the site matched the vision she'd carried through years of writing the book.

Live Site

See it in context

The finished platform, live at unboundbooks.net.

Outcomes

A platform that matches the world of the book

What Accendra Does

Your business runs. Your AI works.

Accendra is my current business — IT consulting focused on custom AI agents and workflow automation for service businesses. I build agents that qualify leads at 2am, book appointments, reactivate old clients, and handle intake, so small teams can focus on delivering the actual service instead of the busywork around it. No tech knowledge required on the client's end. I handle everything, from discovery through build to handoff.

The Business

A real, operating consultancy — not a concept

Unlike the case studies elsewhere on this site, Accendra isn't a past role or a portfolio concept. It's live, it has real clients, and I run it end to end: positioning, sales, discovery calls, technical build, and delivery. It's the clearest example on this site of how I operate without a team around me — product strategy and hands-on engineering, done by the same person, on a deadline that's real because it's my own business.

Live Site

See it in context

The current homepage, live at accendra.io.

Outcomes

Real numbers from a real business