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Data Quality Gate

Before your AI agent acts on a dataset, check if it's trustworthy. Returns a deterministic verdict — RELIABLE / USABLE_WITH_CLEANING / UNRELIABLE — with exact facts: completeness,nulls,type errors,impossible values,duplicates, outliers.100% deterministic,no LLM, no hallucination. Any JSON dataset.

Pricing

from $0.01 / actor start

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Developer

Nicolas Dupuis

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Data Quality Gate for AI Agents

Call this before your agent acts on any dataset. It returns a deterministic quality verdict so your agent knows whether to trust the data — before making decisions on it.

When to call this tool

Before feeding a dataset to an LLM or agent
Right after a scraper run, to check if the output is usable
Before loading data into a RAG pipeline
Whenever an agent receives data from an untrusted or unknown source
As a quality gate step in any automated data pipeline

What it returns

A deterministic verdict in one call:

Verdict: RELIABLE / USABLE_WITH_CLEANING / UNRELIABLE
Score: 0-100, with transparent weights (recalculable by the agent)
Exact facts: completeness, nulls per field, type consistency, impossible values, exact & fuzzy duplicates, statistical outliers, structural homogeneity, cardinality

Why it's different

100% deterministic — same data in, same verdict out, always. No LLM, no hallucination, no randomness.
Facts separated from verdict — every number is traceable (method + threshold shown). The agent can trust the verdict OR recalculate from raw facts.
Hard gate on factual errors — any impossible value (negative price, out-of-range rating) caps the verdict, transparently.
Transversal — works on any tabular dataset, any domain (e-commerce, real estate, finance...). No domain assumptions.
Never crashes — malformed, empty, or huge datasets degrade to a valid verdict, never an error.

Benchmark (real datasets)

Token reduction 88–99.8%: turns a huge raw dataset into a compact, exploitable verdict — saving your agent tokens and preventing decisions on bad data.

Input

datasetId — an Apify dataset ID from a previous run, OR
rawJson — a raw JSON array of records

Output

One record: facts + score (with weights) + verdict (level, reasons, recommendation). Stable, predictable structure — safe to code against.

Deterministic, agent-first

Built for AI agents, not dashboards. No interface, no setup, one call, one verdict, exact.

MCP server

Ships as an MCP server for direct integration with Claude, Cursor, or any MCP-compatible agent.

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