Case study · GTM data

Amazon brand lead engine

A pipeline that turns marketplace brand and product exports into qualified leads with a verified domain, the right decision-maker and a deliverable email, with every count reconciled back to the source.

Built for WorkinX DigitalClient engagementGTM data
Engineering facts
831,765 brand and 3,732,077 product records loaded and reconciled
8-stage pipeline with a tie-out gate at every step
Every email lookup logged, so none is paid for twice
  1. 1The problem

    The sales team had hundreds of raw marketplace exports and no reliable way to tell which brands fit, who to contact or whether a number on a dashboard could be trusted. Manual research did not scale and paid lookups were being repeated.

  2. 2Our contribution

    Designed and built end to end by Kumar Sahil.

  3. 3What we built

    An 8-stage pipeline: load and de-duplicate (with a tie-out gate that must reconcile before anything moves on) → map brands to products → score against the ICP revenue band → find and verify the company domain → size the company → find decision-makers through a strict title gate → find emails, with every attempt logged so no lookup is ever paid for twice → clean. A 13-command CLI runs each stage, and a CRM dashboard shows a tie-out panel next to every number.

  4. 4The result

    In production. Results shared on request.

Technology
Python
PostgreSQL
FastAPI
Tavily
Trykitt
LLM verification
More work

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