Reconova: A Private Studio for Cleaning Messy Business Data

Most data problems do not announce themselves. An export opens cleanly, the columns look right, and the file goes into a CRM, a finance system, or a migration script. The damage shows up later: duplicate customers after an import, invoice references that lost their leading zeros somewhere between two tools, a status column that quietly contains four different spellings of the same value.

Reconova is the Motifuse product built for that category of problem. It is a data quality, transformation, and reconciliation studio: a workspace where you can profile a messy CSV, see exactly what is wrong with it, build a cleaning pipeline one step at a time, preview every change before it happens, and export a corrected file along with a record of what was done.

This article explains what Reconova does today, how the free browser workspace and the subscription cloud tier differ, how files and data are handled, and where the product's current limits are. The goal is that you can judge whether it fits your workflow before you ever open it — and use it well if you do.

What Reconova is

Reconova lives at motifuse.com/reconova and has two modes that share one interface.

The browser workspace is free and needs no account. You open the page, add a CSV, TSV, or JSON file, and everything — parsing, profiling, transformation, export — happens locally on your device. Files up to 25 MB and around 100,000 rows are supported, subject to your browser's memory.

The secure cloud workspace is a subscription tier for work that outgrows a browser tab. Signed-in subscribers can upload larger files — CSV, TSV, JSON, NDJSON, XLSX, and Parquet up to 100 MB — to private, encrypted object storage, run profiling as tracked server-side jobs, and download results through short-lived links. Cloud processing is explicit: nothing is uploaded unless you choose Secure upload while signed in.

Within Motifuse, Reconova sits in the premium products line: the free tier is a real, complete tool rather than a demo, and the subscription adds capacity and continuity rather than unlocking basic features.

Why a data-quality studio, and not another converter

There is no shortage of one-shot utilities that convert a CSV to JSON or split a file in half. Motifuse publishes several itself, including a CSV to JSON converter, and for a quick format change they are exactly the right tool.

The gap appears when the job is not conversion but judgment. A file with 40,000 customer rows does not need reformatting; it needs someone to discover that 6 percent of email addresses are blank, that two exports were pasted together with different date conventions, and that 312 rows are exact duplicates. Spreadsheet software can find these things, but only through manual filtering that leaves no repeatable record, and it famously mangles identifier-like values along the way.

At the other end of the spectrum, enterprise data-preparation platforms handle all of this and far more — at a price, a learning curve, and a deployment footprint that make no sense for a weekly CRM import or a one-time migration.

Reconova is aimed at the middle: serious enough to profile data properly and make changes auditable, light enough that the free tier runs entirely in a browser tab.

Who gets the most out of it

The design assumes a person who is responsible for data being correct, without necessarily being a data engineer.

• Operations teams cleaning customer or order exports before they enter another system.
• Finance staff normalizing vendor names, checking amount and date columns, and protecting invoice references during a close.
• Agencies and freelancers standardizing client-supplied spreadsheets into a consistent format.
• Developers and analysts validating an export before a migration, instead of discovering surprises after the import.
• Anyone who has been burned by a spreadsheet silently converting "001234" into "1234".

Core capabilities

Profiling and the explainable quality score

When a file loads, Reconova profiles every column: detected type, missing values, distinct values, common patterns, and outliers. The headline result is a data-quality score built from five separate factors — completeness, validity, consistency, uniqueness, and conformity.

The important design decision is that the score is explainable. Each factor is shown on its own, so a low score points at a cause: completeness drops when fields are empty, uniqueness drops when duplicate rows appear, conformity drops when values stray from the column's dominant pattern. You are never left with an unexplained "73" and no idea what to fix first.

A transformation pipeline you can preview

Cleaning happens through an ordered pipeline of steps rather than in-place edits. The current engine ships thirteen operations, including trimming whitespace, collapsing repeated spaces, Unicode normalization, case conversion (lower, upper, and title case), exact value replacement, filling missing values, data type conversion, renaming and removing columns, filtering rows, and removing exact duplicate rows.

Every step reports how many rows it affected and how many values it could not process, and the whole pipeline supports a non-destructive dry run: you see the outcome — including per-step counts — before any export is written. If a step turns out to be wrong, you adjust or remove it and run the preview again. The original file is never modified.

Identifier safety by default

Reconova treats values such as "001234", long account numbers, and reference codes as identifiers by default, which means they pass through untouched. Converting a column's type is a deliberate act: you add an explicit conversion step, preview it, and choose what should happen to values that fail to convert. That single default prevents the most common and least visible spreadsheet injury — numeric coercion destroying leading zeros and long references.

Exports you can defend

The end of a session is a clean CSV or JSON export. Because the work happened as a pipeline, you also have something a hand-edited spreadsheet can never give you: an exact, ordered list of what was changed, which makes the result explainable to a colleague — or repeatable next month when the same messy export arrives again.

How a session works

In the browser

1. 1Open Reconova — no sign-in is required for the local workspace.
2. 2Add a CSV, TSV, or JSON file up to 25 MB.
3. 3Review the profile: column types, missing data, duplicates, and the five quality factors.
4. 4Add transformation steps and adjust their options.
5. 5Run a dry run and check the per-step results.
6. 6Export the cleaned file as CSV or JSON.

Closing the tab ends the session; nothing was sent anywhere, so there is nothing to delete.

In the secure cloud workspace

1. 1Sign in to your workspace with an active Reconova subscription.
2. 2Create a project (up to 10 can be active at once).
3. 3Choose Secure upload. The browser sends the file straight to private object storage using a one-time permission that is valid for 15 minutes and bound to a single randomly named object.
4. 4Profiling runs as a tracked job with visible progress. Jobs can be cancelled while running, and a failed job can be retried.
5. 5Download results through a short-lived link (about 5 minutes), or delete assets yourself at any time.

If a job fails — a malformed file, an unsupported structure — the job record says so, and the retry action reprocesses the same input without a second upload.

Browser processing versus cloud processing

The two modes exist because the trade-off is real: local processing offers the strongest privacy, while server processing offers capacity and continuity. Reconova makes the choice explicit instead of making it for you.

How files and data are handled

The privacy model differs by mode, and the difference is the point.

In browser mode, the file is read by your own browser and never transmitted. Local processing is not a magic privacy shield — the data is still on your device and visible to your browser, extensions, and operating system — but no copy exists on any Motifuse server.

In cloud mode, uploads go directly to private object storage encrypted at rest with AES-256, under a randomly generated object key. Access is controlled per request, download links expire in about 5 minutes, and stored files carry fixed retention windows enforced by a daily cleanup job:

You can delete any asset yourself before its window ends, and deletions are recorded. Expired access grants are revoked automatically. The Privacy Policy describes the platform-wide data practices, and the security page covers reporting and infrastructure posture.

Subscriptions and the workspace

Cloud capability is sold as a Reconova subscription with monthly and annual plans, billed through Razorpay and shown on the pricing page before checkout. Subscriptions renew automatically until cancelled; cancellation takes effect at the end of the paid period, and access continues until then. Customers who already hold a Motifuse client workspace from a custom-tool project keep their Reconova access without a separate self-service subscription.

There is currently no free trial of the cloud tier — the browser workspace itself is the honest trial, since it runs the same profiling and pipeline engine on your own machine.

Three practical scenarios

Cleaning a customer list before a CRM import

An operations coordinator receives a 30,000-row customer export assembled from two regional systems. Profiling shows the problem immediately: 7 percent of emails missing, a status column with "Active", "active", and "ACTIVE" as separate values, and 214 exact duplicate rows. The pipeline is four steps — trim whitespace, title-case names, replace the status variants with one canonical value, remove exact duplicates — previewed once, exported once. The CRM import goes through without creating duplicate accounts, and the same pipeline handles next quarter's export.

Normalizing vendor data for a finance close

A finance analyst preparing accounts-payable data needs vendor names consistent and invoice references intact. Case conversion and whitespace cleanup standardize the vendor column, while invoice references — many with leading zeros — pass through untouched because Reconova treats them as identifiers unless told otherwise. A type conversion on the amount column is added explicitly, with rows that fail conversion surfaced for review rather than silently zeroed. The exported file loads into the reporting tool with references intact.

Validating a migration export

A developer migrating a legacy system profiles the export before writing a single import script. The profile reveals a date column with two formats and a supposedly unique key column with 41 duplicate values — both cheaper to discover now than after the import. With the cloud tier, the same profile runs on the full 80 MB export rather than a sample, and the profiling report stays available for 7 days while the fixes land.

Where quick free tools are still the right choice

Reconova does not replace single-purpose utilities, including Motifuse's own free tools. If you need to convert one small file, reformat a JSON snippet, or check a CSV's structure once, a focused tool with zero setup is faster — that is what pages like the CSV to JSON converter are for.

Reconova earns its place when the questions get bigger than the format: how healthy is this data, what exactly is wrong with it, can I fix it without breaking identifiers, and can I do the same thing again next month. Those questions need profiling, previews, and a repeatable pipeline — not another converter.

Limitations to know before you rely on it

• The browser workspace tops out at 25 MB and roughly 100,000 rows, and wide files can hit browser memory before those limits.
• Cloud uploads accept CSV, TSV, JSON, NDJSON, XLSX, and Parquet up to 100 MB per file. Other formats, including password-protected or corrupted files, are rejected.
• Two-source reconciliation — matching rows across two files, exactly or fuzzily — is on the roadmap (planned as Phase 6) but is not available today. What exists now is single-file profiling, cleaning, and exact duplicate removal.
• Cloud outputs expire on the schedule above; Reconova is a processing workspace, not a storage product.
• Download links are short-lived by design; a link that expires is refreshed from the project page, not a data loss.
• An internet connection is required for the cloud tier; browser mode works offline once the page is loaded.
• Like any automated tool, it applies the rules you configure. Review outputs before important use — a pipeline is only as correct as its steps.

Getting started

The sensible path is to start free. Open Reconova, drop in a file you know is imperfect, and read the profile — the quality factors usually surface something within seconds. Build a small pipeline, dry-run it, and export.

If your files are larger than the browser handles, arrive as XLSX or Parquet, or belong to recurring work you want to keep in projects, compare the plans on the pricing page and sign in to the customer workspace. And if your team's need is a workflow specific to your business rules rather than a general tool, Motifuse also builds private custom tools delivered in the same workspace.

Frequently asked questions

The bottom line

Reconova occupies a deliberately practical middle ground: more rigorous than hand-editing a spreadsheet, more transparent than a black-box cleanup script, and far lighter than enterprise data-preparation platforms. Its free tier is genuinely useful on its own, its privacy model is concrete rather than promotional — local by default, explicit and time-limited when the cloud is involved — and its current limits are stated instead of hidden.

If messy exports are a recurring part of your work, the fastest way to evaluate it is the product itself: open the browser workspace with a real file and see what the profile finds.