Good AI Task

AI compatibility

Deduplicating 2,800 lead records across six sources is a clean win for a data agent.

Good fit

AI can handle this.

Average across 1 submission.

82
avg / 100

The honest read

This is a well-scoped data cleaning and deduplication task with clear success criteria: one clean CSV, deduplicated by email, with standardized fields and merge notes. The main risk is edge cases in fuzzy company name matching and handling malformed or missing emails, but these are manageable with a well-prompted agent and a human spot-check before HubSpot import. The output is reversible — the original source files remain intact — so error cost is low.

Aggregated across 1 submission.

The five dimensions

Repeatability

High

The logic is structurally identical every time: ingest CSVs and email exports, normalize fields, deduplicate on email, flag merges. This is a deterministic pipeline that can be scripted and rerun as new batches arrive.

Ambiguity Tolerance

Medium

Deduplication by exact email is crisp, but company name standardization requires fuzzy matching rules that aren't fully specified — 'Acme Corp' vs 'Acme Corporation' vs 'ACME' all need a judgment call. Success is mostly measurable but a few edge cases will need human review.

Data & Tool Availability

High

The user has all source data (CSVs and Gmail exports) and the output target is a simple CSV — no live API access or special permissions required. A code agent can work entirely with local files.

Error Cost

Low

The original source files are untouched, so any mistakes in the output CSV are fully reversible before HubSpot import. A human spot-check of the deduplication notes before import is a natural safety gate.

Human Judgment Required

Low

The task is almost entirely rule-based: email normalization, phone formatting, fuzzy string matching for company names. The only genuine judgment calls are ambiguous company name variants, which can be flagged for human review rather than silently resolved.

What an agent would need

  • All 6 source files delivered as CSVs (or Gmail export in a parseable format like CSV/JSON) with consistent or documented column headers
  • Explicit rules or examples for company name standardization (e.g., preferred canonical forms, handling of Inc/LLC/Corp suffixes)
  • A defined deduplication priority rule when records conflict — e.g., which source wins on phone number or company name when emails match
  • A code execution environment (Python with pandas/fuzzywuzzy or similar) to run the deduplication and normalization pipeline
  • A defined output schema matching HubSpot's import template so the cleaned CSV maps correctly to CRM fields

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