Manual prospecting found only a small number of companies each day, with the reasoning scattered across browser tabs and notes. Fully automatic outreach would create a different problem: nobody could see why a company qualified, and an inaccurate or poorly timed message could still reach a real person.
05 / Internal product · AI workflows · B2B
Internal AI Lead Qualification Platform
Built for Shree Export
An internal tool that finds export prospects, gathers public evidence, scores fit, and drafts an opening message, then requires a person to approve it before anything is sent.
Let AI gather evidence, help score fit, and prepare a draft. Keep the decision to contact a company with a person who can review the evidence and change or reject the message.
A working internal dashboard with role-based access, company and lead records, qualification fields, status and assignment, filtering, bulk actions, CSV export, background-job visibility, and an approval step before outreach.
The workflow is built, but lead volume, research time saved, and email deliverability still need to be measured during regular use. Private company, contact, and lead data are not shown.
01
User and problem
Who needed this product, and why.
A small marketing team that researches and qualifies prospects, with sales and management able to view progress, assignments, and lead quality without changing the working data.
The original request was to find more export leads. I turned it into four steps a small marketing team could manage: discover companies from public sources, collect the evidence behind each lead, assess whether the company fits, and prepare a draft that a person can approve, edit, or reject.
02
Important decisions
Decision 01
Give every step a visible state
- Why it mattered
- One automated score would hide whether a company was only discovered, researched with evidence, judged as a fit, reviewed by a person, or contacted.
- What I did
- Keep companies, contacts, source evidence, qualification, assignment, status, and outreach approval as separate records and steps.
- What it required
- The team has to make a few explicit decisions instead of pressing one automation button, but each decision can be checked and corrected.
- The result
- Anyone with access can see where a lead is, why it qualified, who owns the next action, and which person approved the outreach.
Decision 02
Build the team workspace before heavier AI
- Why it mattered
- The long-term concept included continuous research, monitoring, reporting, and campaigns, which was too much to validate in one release.
- What I did
- Start with login, role-based access, companies, leads, job status, filtering, assignment, bulk actions, and export. Add deeper AI research only after the team can manage the resulting work.
- What it required
- The first release automates less, but gives the team one reliable place to organize and review prospecting work sooner.
- The result
- The working dashboard supports the daily process now, while the roadmap keeps later research-agent capabilities separate from features already built.
Decision 03
Require approval before outreach
- Why it mattered
- AI-generated personalization can be inaccurate, intrusive, or poorly timed when it is sent without review.
- What I did
- Place every draft in a review queue where a user must approve, edit, or reject it before it can become outreach.
- What it required
- The workflow sends fewer messages than a fully autonomous system, and every approved draft requires human attention.
- The result
- A wrong or badly timed message cannot reach a real company unless someone has read it and chosen to approve it.
03
What I built
What the product includes and how it works.
- A role-based internal dashboard for marketing, sales, and management
- Company and lead records with status, assignment, filtering, bulk actions, and CSV export
- Structured source evidence and qualification fields that show why a lead was accepted or rejected
- Visible background-job status for longer research and enrichment tasks
- A review queue where a person approves, edits, or rejects every proposed outreach message
- A PRD and phased roadmap that keep future AI-agent work separate from the working MVP
How it works. The web dashboard stores companies, contacts, public source evidence, qualification decisions, assignments, and outreach drafts as related records with access controlled by role. Longer research and enrichment tasks run in the background with visible status. AI-generated drafts enter a review queue, not an outbox, so nothing is sent without a person's decision.
04
What exists today
The manual process found about 5 to 10 leads per day
This recalled working estimate was the starting point for the product, not an instrumented result.Goal: build a list of 500 qualified leads per month
This is a product goal, not a result achieved yet.Goal: reduce research time and improve email deliverability
Time saved and the share of messages reaching an inbox still need to be measured during regular use.Private names, contact details, and lead data are not shown
The case explains the product without exposing business or personal data.05
Reflection
What I learned, and what I would test next.
- An AI workflow is easier to inspect and improve when each step records a visible decision instead of hiding activity inside an agent.
- Product goals must stay clearly separate from results that have actually been measured.
- When AI can affect an external person, human review can be a permanent product safeguard rather than a temporary limitation.
Let’s discuss the product problem.