AI compatibility

Messy contact deduplication across three sheets is a solid job for AI.

Good fit

AI can handle this.

Average across 1 submission.

avg / 100

The honest read

Deduplicating and consolidating contact records across three sheets is a well-structured data operations task that AI handles reliably — fuzzy email matching, conflict flagging, and CSV output are all within current capability. The main risk is edge-case matching logic (e.g., deciding whether 'johnsmith@' and 'john.smith@' are the same person when other fields conflict), which benefits from a human spot-check pass. With Google Sheets API access granted, this is a strong candidate for full automation.

Aggregated across 1 submission.

The five dimensions

Repeatability

High

The structure is consistent: read sheets, apply matching rules, merge, flag conflicts, export CSV. This pattern is the same every run and scales cleanly to more rows or sheets.

Ambiguity Tolerance

Medium

The core success criteria are clear (one master CSV, conflicts flagged), but the matching threshold for email variants and name fuzzy-matching requires a defined ruleset. Without explicit rules, the agent must make judgment calls that a human may later dispute.

Data & Tool Availability

High

Google Sheets has a well-documented API, and the agent needs only read access to three sheets plus write access to export a CSV. No external context or live credentials beyond OAuth are required.

Error Cost

Medium

A false merge (collapsing two real people into one) or a missed duplicate could corrupt the master list, but the output is a new CSV — the source sheets remain untouched, making errors reversible with a re-run or manual correction.

Human Judgment Required

Low

Conflict flagging is mechanical once rules are set, and the agent doesn't need to resolve conflicts — just surface them. A human only needs to review the flagged rows, not the full 8,500-record output.

What an agent would need

OAuth or service-account read access to all three Google Sheets
Defined matching rules: which email variants count as the same person, minimum name similarity threshold, and how to handle phone number conflicts
A code or data agent capable of fuzzy string matching (e.g., using libraries like dedupe, fuzzywuzzy, or recordlinkage)
A clear output spec: which fields to keep when merging, how to format the conflict-flag column, and the target CSV schema
Write access to a destination (local filesystem, Google Drive, or S3) to deliver the final CSV

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