AI compatibility

Normalizing messy supplier spreadsheets into one clean CSV is a solid job for AI.

Good fit

AI can handle this.

Average across 1 submission.

avg / 100

The honest read

This is a well-scoped data normalization and deduplication task with clear inputs, defined outputs, and low error cost since the result is a reviewable CSV. The main challenge is schema mapping across 6 different supplier formats, but that's exactly the kind of structured variation a code or data agent handles well. A human spot-check on deduplication logic and pricing flags is advisable but not strictly required.

Aggregated across 1 submission.

The five dimensions

Repeatability

High

The task is structurally identical each month: ingest files, map schemas, deduplicate, flag anomalies, output CSV. Once the schema mappings are established for each supplier, the pipeline is highly repeatable with minimal new judgment required.

Ambiguity Tolerance

Medium

The output format (canonical schema, master CSV with source and date) is well-defined, but deduplication logic for SKUs with different supplier codes requires a matching heuristic that may need human validation on edge cases. Pricing inconsistency thresholds also need a defined rule.

Data & Tool Availability

High

All 18 months of Excel files are available as static inputs, and the agent only needs standard data processing tools (Python/pandas or similar). No live APIs, credentials, or external systems are required to complete the task.

Error Cost

Low

The output is a CSV that a human can review before acting on it. Errors in deduplication or schema mapping are visible and correctable; no irreversible downstream action is triggered automatically by the agent's output.

Human Judgment Required

Low

Most decisions here are rule-based: column mapping, unit conversion, date normalization, and exact or fuzzy SKU matching. The only genuine judgment call is resolving ambiguous duplicate SKUs, which can be flagged for human review rather than auto-resolved.

What an agent would need

Access to all 18 months of supplier Excel files, ideally uploaded to a shared location or passed directly to the agent
A defined canonical schema specifying target column names, units, and date format for the output CSV
A deduplication matching rule (e.g., fuzzy name match + unit match) or willingness to flag ambiguous cases for human review
A pricing inconsistency threshold (e.g., flag if same SKU differs by more than X% across suppliers)
A code execution environment (Python with pandas/openpyxl or equivalent) to process and transform the files

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