Let Kosh AI Apply Configurable Matching Rules While Your Team Builds the Logic, Not the Queries
Most teams still match records between systems with a mix of spreadsheet formulas, one-off scripts, and manual review. Kosh AI replaces that patchwork. It pulls data from any source, applies configurable matching rules you control, and tells you exactly which records matched, which didn't, and why.
Kosh AI connects to databases, flat files, APIs, and common business systems, so your team isn't building a separate matching script for every data source.
Kosh AI lets your team define matching rules through a visual interface, so logic changes don't require a developer or a rewritten script every time.
Instead of forcing every match into a single-record pair, Kosh AI handles one-to-many and many-to-many relationships, the kind that break most spreadsheet-based matching.
Kosh AI is built to process large datasets quickly, so growing data volume doesn't mean growing matching time.
When records match, Kosh AI keeps the matching rule, the confidence score, and the fields compared attached to the result, so nothing is a black box.
You don't need to restructure your data or standardize every source first. Kosh AI reads what's already there and handles the mapping, the matching, and the reporting.
No two matching problems look the same. Kosh AI fits the data sources you connect, the industry you work in, and how your team is structured.
Bank, payment, and ledger records are matched for reconciliation, settlement verification, or fraud review.
Records from a legacy system are matched against a new system to confirm a migration completed without data loss.
Customer, vendor, or product records across systems are matched to identify and merge duplicates.
Records are matched against control lists or compliance datasets to confirm coverage and surface gaps.
Production and inventory records across plant systems are matched for consistency without custom scripting.
Usage, billing, and CRM records are matched to validate revenue recognition and customer data accuracy.
Order, inventory, and fulfillment records across systems are matched daily at retail transaction volume.
Patient, billing, and claims records are matched across systems with the accuracy healthcare data demands.
Kosh AI centralizes record matching for teams working across many systems, giving configurable rules and full transparency at enterprise volume.
Kosh AI gives growing finance and operations teams a matching engine without the custom development a manual process usually needs.
Matching records by hand or with one-off scripts ties up technical and finance staff every time the data changes shape. Kosh AI takes that load off your team and gives you matching logic you can actually maintain.
Datasets that used to take hours to compare manually are matched in minutes, freeing up time across every team that needs matching.
Business teams can configure and adjust matching rules themselves, without waiting on a developer for every change.
Instead of writing and maintaining matching scripts, your team spends its time reviewing the records that actually need judgment.
Low match rates on a data source often reveal an upstream data quality problem before it affects downstream reporting.
See match rates by source and by rule, without waiting for someone to run a report.
Every match keeps its logic and confidence score attached, so results can be explained and defended later.
Kosh AI connects directly with all major banks and ERPs, enabling real-time data sync for faster, more accurate reconciliations—no manual uploads, no delays.
A one-off script breaks the moment a data source changes format, and a spreadsheet can't handle many-to-many relationships at all. Kosh AI is built to be the matching layer your team configures instead of rebuilds.
Kosh AI lets you weight, tolerance, and combine fields precisely, not force every match through one rigid rule.
An unmatched record comes with its closest candidate and the reason it fell short, not just a blank result.
Manual corrections feed back into the matching logic, sharpening results for the next run.
From a simple exact match to a complex many-to-many split, Kosh AI handles the matching logic each scenario needs.
Getting consistently high match rates comes down to a few habits most teams know but struggle to apply systematically. Here's what Kosh AI helps enforce automatically.
A unique reference number beats a fuzzy text match every time one is available on both sides.
Amount and date tolerances should reflect real-world variation in your data, not an arbitrary round number.
A match made with low confidence deserves the same scrutiny as a record that didn't match at all.
Revisiting rules as data sources evolve keeps match rates high instead of slowly degrading over time.
Transaction matching software compares records held in two or more data sources and identifies which ones represent the same underlying event, using configurable rules rather than a one-off script or a manual spreadsheet comparison. It connects directly to databases, files, and APIs, so your team sees match results without writing and maintaining custom code for every new data source. Kosh AI takes it further by supporting one-to-many and many-to-many matching, letting business users configure rules through a visual interface, and keeping full transparency into why every match or non-match occurred. The result is a matching capability your team can reuse across use cases instead of rebuilding from scratch each time.
A VLOOKUP or a quick Python script can handle matching two small, clean datasets well enough. The trouble starts when a match isn't strictly one-to-one, when a data source changes its format without notice, or when the volume grows past what a spreadsheet can process without crawling to a halt. At that point, every matching problem becomes a small engineering project, and whoever owns the script becomes a bottleneck every time the business needs a new comparison run.
This gets expensive in ways that aren't always obvious. A script written for one data migration doesn't transfer cleanly to the next reconciliation problem, so teams end up rebuilding similar logic again and again instead of reusing it. And because the matching logic usually lives in someone's personal script rather than a documented, auditable system, it's hard for anyone else to trust the results or explain exactly why a particular match was made. Transaction matching software exists to turn that one-off, person-dependent process into a reusable, transparent capability the whole team can rely on.
Here's what actually changes when transaction matching moves off scripts and spreadsheets and onto a dedicated matching engine.
| What You're Comparing | Automated Transaction Matching | Doing It Manually |
|---|---|---|
| Who Can Build or Change Matching Logic | Business users configure rules through a visual interface. | Changes usually require a developer to rewrite a script. |
| Handling Many-to-Many Matches | Built in as a core capability of the matching engine. | Usually requires custom logic that's hard to maintain. |
| Scaling to More Data | Performance holds steady as record volume grows. | Scripts and spreadsheets slow down or fail at high volume. |
| Understanding Why a Match Was Made | Every match keeps its logic and confidence score attached. | Matching logic often lives only in the script author's head. |
| Adapting to New Data Sources | New sources are connected and configured without new code. | Each new source usually means a new script to write and maintain. |
| Cost Over Time | Reusable rules reduce the ongoing cost of each new matching project. | Each matching project starts largely from scratch. |
Not every matching tool is built to handle complex, high-volume data relationships. Here's what to walk through before picking one.
Set up Kosh AI, start matching your highest-volume data sources automatically, and stop rebuilding matching logic from scratch.
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Transaction matching software compares records held in two or more data sources and identifies which represent the same underlying event, using configurable rules instead of a one-off script or manual spreadsheet comparison. Kosh AI does this automatically, applying your matching logic and flagging anything that doesn't match.
It connects to your data sources, applies matching rules your team configures through a visual interface, and compares records field by field. Records that match cleanly are confirmed automatically, and anything that doesn't gets flagged with the closest candidate and a reason.
Matching with scripts and spreadsheets gets slower and more fragile as data volume and complexity grow, and matching logic often lives only in one person's head. Transaction matching software keeps the process documented, reusable, and scalable regardless of who's maintaining it.
It supports one-to-one, one-to-many, and many-to-many matching, along with exact, fuzzy, and tolerance-based rules. Kosh AI handles all of these relationship types within the same matching engine.
Transaction reconciliation software is typically built around a specific finance workflow, like matching bank activity to the ledger. Transaction matching software is the more general-purpose engine underneath, usable for finance reconciliation as well as data migration validation, deduplication, and other matching problems. Kosh AI provides both.
Most transaction matching software is built to connect broadly, including databases, flat files, and APIs. Kosh AI connects to common business systems and ERPs like SAP, Oracle, NetSuite, Sage, QuickBooks, and Xero, as well as custom data sources, so matching isn't limited to a fixed set of integrations.
No, that's one of the core advantages of dedicated matching software. Kosh AI lets business users build and adjust matching rules through a visual interface, without writing or maintaining custom code.
They're flagged with the closest candidate found and the reason the match didn't meet your configured threshold. From there, a reviewer can confirm, reject, or adjust the match manually, and that correction feeds back into future matching runs.
Most teams can have a first matching scenario configured and running within a few weeks, since the software connects to data sources you already have. Kosh AI follows a similar timeline, with more complex, multi-source rollouts typically taking 1 to 3 months to fully configure.
Start with your data sources, the types of matching relationships you need, and your expected record volume. From there, compare options on rule configuration flexibility, performance at scale, and how transparent match results are. Kosh AI is built around exactly that checklist, but it applies to any software you're comparing.
Book a demo and watch Kosh AI match your own transaction data across systems.
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