Turning Manual Lead Research into a Working Tool

01 Status

02 What I Built

OptiGTM is an internal MVP for researching and evaluating ICP (ideal customer profile)-fit e-commerce brands at Optifeed.

The problem was manual lead research: finding relevant companies, understanding fit, and preparing enough context for a first outreach took too much repeated effort.

I worked from marketing problem discovery through UX flow, CTO scoping, and Rails MVP build with Anthropic API integration.

The tool is an actively evolving internal MVP; production adoption and usage metrics are not claimed.

03 Problem

Optifeed needed a better way to discover and evaluate ICP-fit e-commerce brands instead of relying only on inbound, founder-led sales, or manual research.

The marketing/sales workflow needed a repeatable way to create a research project, run a search, receive explainable company candidates, and review the result before any outreach decision.

04 Key Constraint

05 My Role

• Discovery and hypothesis
  Marketing problem discovery, hypothesis formation, and UX flow in Miro.
• Technical scoping
  Scoped the direction with CTO context before committing to the MVP shape.
• MVP build
  Rails MVP work at practical prototype/MVP level with Anthropic API integration understanding.
• AI-assisted implementation
  UI/view updates, result saving/display flow, and build support with Claude Code and Codex.

06 MVP Flow

• Create
  Create a project or campaign.
• Search
  Run a lead research search.
• Generate
  Claude / Anthropic returns explainable company candidates.
• Review
  Save the result, show it on the project page, and let marketing review before deciding what to do next.

07 What I Built / Worked On

• Product structure
  Project/Search model understanding and project-based lead research flow.
• Result flow
  Search result saving, project page output display, and simple UI-level Rails views in ERB/HTML.
• Anthropic flow
  Anthropic integration flow for explainable company candidates.

08 Key Product Decisions

• Project-based structure
  A repeatable project/campaign structure instead of one-off search.
• Explainable output
  Explainable candidate output instead of a raw company list.
• Saved results
  Saved results instead of temporary AI response.
• Human review
  Marketing review step before any automated action.

09 Visual Story

• Rails Product Structure
  The Project and Search structure shows how campaign inputs move into saved AI research results. A real screenshot is not shown to avoid exposing internal or customer data.

Real screenshots require permission and must avoid exposing internal or customer data.

10 Current Status

Any real screenshot requires permission and must avoid exposing internal or customer data, which is why this section uses a structural description instead of a live capture.

11 What This Proves

• Workflow to product loop
  I can translate a business workflow problem into an internal product loop.
• Practical Rails fluency
  I can work with Rails at practical MVP-building level.
• AI-assisted internal tool build
  I can use AI coding agents to move from product direction to working internal tool.
• Scoped collaboration
  I can collaborate with marketing and CTO context without overbuilding.

If a manual internal workflow is eating your team’s time and nobody has turned it into a tool yet.