How does DataReconIQ handle datasets with different schemas?

DataReconIQ uses AI-powered column mapping to suggest matches between datasets with different column names. It analyzes data types, values, and patterns to map columns intelligently, even when schemas don't line up. Users can also override any suggested mapping.

What happens when primary keys don't match between datasets?

DataReconIQ suggests primary keys but allows users to override them. It uses fuzzy matching to find likely matches even when IDs don't align exactly. Low-confidence matches are flagged for review rather than forced, gracefully handling broken keys and missing IDs.

How does conflict resolution work in DataReconIQ?

When matched records have conflicting field values, DataReconIQ flags them for review. Users can choose values from Dataset A, Dataset B, or enter custom values. Bulk actions are available for common conflicts, and every decision is logged in an audit trail.

Can I export reconciled data, and in what formats?

Yes, DataReconIQ exports clean, normalized datasets to CSV, XLSX, or JSON formats. Column names are editable before export, and all match decisions are logged in an audit trail for compliance and traceability.

What is human-in-the-loop review?

Human-in-the-loop means DataReconIQ flags uncertain matches and conflicts for your review. The tool never forces matches when confidence is low, ensuring no hallucinations or forced matches. Users decide which records match and how to resolve differences, providing full control and traceability.

How does DataReconIQ handle duplicates and near-duplicates?

DataReconIQ identifies duplicate records within each dataset and groups them, which helps improve matching accuracy and data quality. Near-duplicates are also flagged for review, preventing false matches from erroneous entries.

How many rows can I reconcile per dataset?

All DataReconIQ plans support reconciling up to 50,000 rows per dataset. Each reconciliation compares 2 datasets.

How many files can I reconcile with DataReconIQ?

Each reconciliation compares 2 datasets (files). The Free tier includes 2 lifetime reconciliations. Pay-as-you-go includes 1 reconciliation per purchase ($12 each). Subscription plans offer 12 (Starter), 40 (Collaborative), or 90 (Operational) reconciliations per month. Reruns count as additional reconciliations.

How long are reconciled results retained?

For Pay-as-you-go purchases, results are retained for 30 days. For subscription plans (Starter, Collaborative, Operational), results are retained for the duration of your subscription, with higher tiers offering longer history.

Is my data private and secure with DataReconIQ?

Yes, all data is private to your workspace. DataReconIQ uses encryption in transit and at rest and does not share your data with third parties. Full audit trails are available for compliance, and the platform adheres to SOC2-ready security practices.

DataReconIQ: AI Data Reconciliation for Mismatched Schemas & Messy Data

DataReconIQ Overview

DataReconIQ is an advanced AI-assisted tool specifically engineered to simplify and automate the complex process of reconciling two disparate datasets. It excels in scenarios where traditional methods like Excel VLOOKUP or fragile scripts fail, such as when schemas don't match, keys are broken, or data contains duplicates and near-duplicates. By combining intelligent fuzzy matching, confidence scoring, and a human-in-the-loop review process, DataReconIQ ensures accurate and verifiable results, transforming messy exports into clean, trusted data.

How to use DataReconIQ

Using DataReconIQ involves a straightforward three-step process. First, users upload two CSV or Excel files, allowing DataReconIQ to inspect their structure, data types, and potential keys. Second, the AI suggests primary keys and column mappings, utilizing fuzzy matching to find likely record matches even when exact IDs are absent. Low-confidence matches are automatically flagged for review. Finally, users review these flagged conflicts and field-level differences, resolving them by selecting values from either dataset or providing custom inputs. The clean, normalized dataset can then be exported to CSV, Excel, or JSON formats, complete with a comprehensive audit trail detailing every decision.

Core Features of DataReconIQ

Smart Column Mapping: AI-suggested column mappings with confidence scores using exact, similarity, and semantic matching.
Primary Key Suggestion & Override: AI proposes primary keys, with the option for manual override and fuzzy matching for broken/missing IDs.
Fuzzy Matching for Data Reconciliation: Intelligently finds likely matches even with inexact values, handling duplicates and near-duplicates.
Confidence Scoring & Match Classification: Weighted 0–100 scoring to classify matches as auto-match, review, or no-match.
Conflict Resolution: Field-level conflict resolution with Dataset A / Dataset B / custom value selection, including bulk actions.
Human-in-the-Loop Review: Side-by-side comparison with detailed match breakdowns for confident decision-making.
Real-Time Progress Tracking: Monitor reconciliation progress live with statistics and match counts.
Duplicate Detection: Identify and group duplicate records within each dataset.
Multiple Export Formats: Export clean, normalized data to CSV, XLSX, or JSON with user-editable column names.
Audit Trail & Explainable Matching: Every match decision, conflict resolution, and override is logged for full traceability and compliance.

Use Cases for DataReconIQ

DataReconIQ is ideal for a wide range of reconciliation scenarios across various business functions. It can be used for invoice reconciliation (matching invoices against payments with differing vendor schemas), bank reconciliation (matching bank transactions with accounting records), shipment reconciliation (matching shipping manifests with order records), and inventory reconciliation (aligning warehouse counts with system records). Furthermore, it's highly effective for CRM vs. ERP reconciliation (matching customer data between systems) and critical month-end close processes, where auditability and accuracy are paramount.

Advantages of DataReconIQ

The primary advantages of DataReconIQ lie in its ability to intelligently handle messy, real-world data without relying on fragile scripts or manual Excel gymnastics. Its AI-powered fuzzy matching and schema-aware capabilities significantly reduce reconciliation time and errors. The human-in-the-loop approach ensures that users retain control over critical decisions, preventing forced matches or 'hallucinations' and building trust in the output. With scalable processing for up to 50,000 rows per dataset, comprehensive audit trails, and robust data privacy (private to workspace, encryption, SOC2-ready practices), DataReconIQ provides a reliable, efficient, and auditable solution for complex data reconciliation challenges.

Pricing and Plans

DataReconIQ offers flexible pricing based on usage limits and workspace size, with all plans supporting up to 50,000 rows per dataset for each reconciliation (comparing 2 datasets). Reruns count as additional reconciliations. Available plans include:

FREE: $0, includes 2 lifetime reconciliations, all features. Upgrade required after free usage.
Pay as you go: $12 per reconciliation, no monthly commitment. Results retained for 30 days.
Starter: $79 / month, for solo analysts, 1 user, 12 reconciliations/month. Results retained while subscribed.
Collaborative: $149 / month, for small teams, up to 5 users, 40 reconciliations/month, shared workspace + longer history.
Operational: $249 / month, for ongoing operational reconciliation, up to 15 users, 90 reconciliations/month, priority processing + longest history.

Add-ons are available for additional users ($20 per user/month) and additional reconciliations ($12 each). Usage is shared across workspace members, and failed reconciliations due to system errors do not count towards limits.

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