DeepCleanCSV
Automated CSV cleaning for analysts and data scientists.
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June 11, 2026
I built a CSV cleaning tool after wasting too much time on dirty data.
Working on data projects, I kept wasting hours cleaning CSVs before I could do anything useful — fixing nulls, duplicates, wrong types, messy text. So I built DeepCleanCSV to automate that.
You upload a CSV, configure which cleaning operations to apply (nulls, duplicates, text cleanup, type casting, numeric precision, empty columns), and download a clean file in seconds. No code required.
Happy to get feedback — especially from anyone who deals with messy data regularly.
60 Comments
A CSV tool represents a strong, what I call, a narrow-use-case tool. CSV cleanup is one of those problems that looks small until someone is doing it repeatedly across real projects. Curious if you had plans about commercializing this. A tool like this seems like it could work well as a lightweight paid utility, freemium or open source if the program's workflow/features stays simple. Keep coding.
Thanks! Yes, it's already live and monetized — freemium model with a free tier and paid plans. You can check the full pricing on the landing page: www.deepcleancsv.com. The "narrow use case" framing is exactly why I built it — repetitive CSV cleanup is genuinely painful and most tools either do too little or too much. Appreciate the encouragement!
perfect
That's actually helpful, a very real pain point!
website looks awesome!
that sounds cool
Congrats on the launch, Franco. Anyone who has ever built an import pipeline or trained a model understands how soul-crushing data preparation is. "Garbage in, garbage out" is the ultimate truth of software development.
Having built systems that ingest complex raw datasets myself, I have two pieces of feedback regarding your product positioning and future roadmap:
First, the data privacy concern. Security is the biggest friction point for B2B tools. If people are uploading customer lists or proprietary financial data, they will be terrified of their CSVs being stored on a third-party server. Does DeepCleanCSV process everything client-side (in-browser) or do you upload it to a server? If it is 100 percent in-browser, you should make this a major headline on your landing page. It is a massive selling point.
Second, the integration roadmap. Manual CSV uploads are great for one-off tasks, but the real commercial value is in automated workflows. Have you considered releasing a simple API or a Zapier integration? Imagine being able to hook this up so that every time a raw report is generated, it automatically runs through your cleaning engine before hitting the target database or CRM.
Your UI looks very clean and the pain point is incredibly sharp. Keep pushing, man!
Thanks for taking the time to write this — both points are spot on.
On privacy: it's server-side, not in-browser. Cleaning a 100 MB file with proper type inference needs more muscle than a browser tab can reliably give. But the data handling is deliberately strict: on the free plan, your file lives only in server memory and is discarded within minutes — it never touches disk or storage. On paid plans, files are stored only to power the history feature, with retention you control (7/30 days max, down to zero if you want nothing stored), one-click deletion, and full wipe on account deletion. You're right that none of this is on the landing page though, and it should be. Fixing that.
On the API: agreed that's where the real commercial value is. Right now I'm focused on nailing the manual flow first, but a cleaning API is on the radar — the engine is already separate from the UI, so it's a natural step.
Really appreciate the thoughtful feedback.
That makes total sense regarding the server-side choice. Trying to run heavy type inference on a 100MB file inside a browser tab is a recipe for memory leaks and crashed browsers. WASM is getting better, but it is not quite there yet for heavy data sets.
The fact that the free tier never touches the disk and paid tiers have customizable retention is actually a brilliant compromise. You should definitely put that exact explanation on your landing page. Having a dedicated Security FAQ or a simple "Zero-Disk Retention" trust badge will instantly clear the biggest friction point for your B2B buyers.
Focusing on nailing the manual UI before releasing the API is also very smart product management. Once the core UX is flawless and you have some early revenue, exposing that API is going to be a breeze since your engine is already decoupled.
Great execution, Franco. I will be keeping an eye on your progress!
"Zero-Disk Retention" is a great framing for it. Thanks for the thoughtful feedback and for following along!
Hey Franco,
Nice web site. I ran one of our messy csv files through it to see how it works.
The biggest issue is the not really free service you offer. You're holding back the 2 pieces people need to be effective with it. So they go all the way through your funnel and don't get the dopamine hit of getting a job done that they need. And yet it's basically free for you to run this through your server. Let them have the full service for files under a certain size. You would actually be winning on that deal because more than anything you need word of mouth more than you need cash. And you need a lot of it in a massively competitive with very good free options.
Second, you are changing people's data. If you don't give them a easy to use history of what you've changed you've essentially damaged their data rather than helped them out. There's plenty of nice cleaning you'll do. But it's worthless if they can't check where it's happened on a spreadsheet larger than what you show or a larger sheet where you can't keep track of it.
And now where you sit in the market. These are technical users. They'll all move to MCPs over the next year and just fire this up through Claude. So you're building on the rusty edge. You can do great here but know that no one wants to write about it and it will take a long while.
On top of this I would think this user is working for a customer who cares a great deal about their data being put out into the cloud. If you are making your living do this you're running in Excel where they have a lot of tools to do this well already. And if you put client data out on the web you're out of a job. So you're fighting for an odd, smaller part of the market.
You know how to build a nice site and have a good layout. Think about if this is really the problem to solve.
Hey Bobsenoff, thanks for taking the time to run a real file through it and share detailed feedback — that's genuinely useful.
A quick clarification on the free tier: the clean CSV download is fully available on the free plan with no size restriction on the output — if you upload a 5 MB file, you get the full cleaned CSV back. What the free plan doesn't include is the PDF report, the reports tab, the history tab, full change history, saved cleaning profiles, and the complete row viewer (free users see the first 1,000 rows). That said, your point about the viewer limit is fair.
On the change log: you're right that this is a trust issue, not just a feature. Knowing exactly what was touched is part of the value. It's currently Business-only, and I'm rethinking that boundary.
On MCPs: I'd push back a bit here. The people I'm building for aren't developers who'll wire up Claude integrations — they're analysts, ops teams, and small business owners who work with CSVs and Excel files and don't want to touch code. That market isn't going anywhere near an MCP anytime soon.
On client data and compliance: fair concern for enterprise contexts. On the free plan, no file is stored — everything is processed in memory and discarded within minutes. On paid plans, users control their own retention policy: Pro can set between 0 and 7 days, Business between 0 and 30 days. Setting it to 0 means nothing is saved to Storage. On top of that, users can manually delete their file history at any time, and deleting the account permanently removes all data. It's not a complete answer to every compliance scenario, but it covers the majority of use cases I'm seeing.
this is something everyone doing data work has needed at some point, cleaning CSVs is always way more painful than it should be 😭
The tricky part here is trust: people may hesitate to download a “clean” file unless they can see exactly what changed. A before/after diff plus a reusable cleaning recipe could make this feel safer and also help distinguish it from a one-off CSV fixer.
Interesting product. One thing I've noticed with data-heavy tools is that users often spend more time cleaning data than extracting insights from it. Curious what kind of users are signing up first — analysts, marketers, or small business owners?
With the rise of AI coding tools, do you think data cleaning could be automated by a custom AI skill?
Definitely a direction I'm exploring. The core pipeline stays deterministic (same input, same output, full change log) — critical when you need to audit every change on production data. But AI on top to suggest what to clean? Natural evolution. The tool already auto-detects some issues (ambiguous nulls, type mismatches) without an LLM.
Solo dev running a sports data pipeline here — CSV pain eats a real % of my build time. Two edge cases that always burn me:
Mixed-encoding files where a CSV is mostly UTF-8 but has a few rogue Windows-1252 rows from someone's Excel export. Default utf-8 read raises; charset detection guesses wrong on small samples. Does DeepCleanCSV auto-detect per row or fall back to a single dominant encoding?
"Looks like a number but isn't" columns — phone numbers, ZIP codes, player IDs — that pandas/Polars eagerly cast to int64 and drop the leading zero or scientific-notation a 16-digit ID. Can you pin column types before the cleanup pass, or is the type inference fixed?
These two alone eat 2-3 hours per dataset for me. Real pain point you're solving.
Both are real and both are on my list.
On encoding: most files parse fine, but the genuinely mixed case — UTF-8 body with a few Windows-1252 rows from someone's Excel — currently fails silently. The file loads, the cleaning runs, but the affected rows come back with corrupt characters and no warning. Fixing that is my next priority.
On leading zeros: the upload is already safe — nothing gets silently cast during read. The risk is in the "Fix types" pass, which right now is all-or-nothing. Per-column type overrides ("keep this as string no matter what") are exactly what's missing and what I'm building next.
Curious about your pipeline — are the ID columns consistent enough to auto-detect (all-digit, fixed length), or do you need manual column pinning?
I like that you're keeping the cleaning steps explicit. Automatic cleanup can be risky when it silently changes data, so having users choose exactly what's applied feels safer than a fully automated approach.
Thanks — that's been deliberate from day one. If a tool can't explain what it changed, you can't trust the output. Everything is opt-in and the cleaning report shows exactly what was touched during the cleaning pass. Even for ambiguous values like literal "none" or "unknown" strings: the app detects them and asks whether to treat them as nulls, instead of deciding for you.
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Congrats Franco! this is a very real pain point. One thing I’m curious about, especially after the comments about recurring workflows and platform-specific CSV quirks: are you thinking about validation against the destination system too?
For example, not just cleaning nulls, duplicates, or types, but confirming that the output still matches the schema/import rules for a CRM, CDP, Shopify-style catalog import, or client reporting pipeline
Thanks! And great question — you're describing the natural next step of something already on the roadmap. From this thread came the idea of platform presets (Shopify, WooCommerce, Amazon): pre-configured cleaning profiles for each format. Your point takes it further, and I think you're right: a preset shouldn't just clean, it should validate the output against the destination's import rules — required columns, formats, value constraints — and tell you "this file will pass Shopify's importer" before you waste an upload attempt. That's more valuable than the cleaning itself, because the failure happens at import time, not cleaning time. Honest answer: not built yet, but cleaning profiles already exist as a feature, so adding validation rules per destination is an extension, not a rewrite. Which destination systems do you deal with most? Knowing where imports actually fail for people would help me prioritize which schemas to support first.
Yeah, exactly. I don’t have one specific destination system to push for, I was thinking more about the import preflight layer itself: required fields, accepted values, IDs/foreign keys, and date/number formats. I think the useful output would be a short blocker report before the user ever uploads the file
There's a pricing insight buried in your own replies. The users who convert are analysts receiving messy CSVs from clients on a schedule. That's a recurring problem, which is what makes a subscription make sense. I'd anchor your plans to frequency of use, not file size caps. Size limits feel like a tax. Monthly workflows feel like a subscription. I spent 20 years selling services into enterprises and the lesson that stuck: customers pay for cadence, not capacity. Find the five users who upload weekly and build the roadmap around them.
This is a really useful reframing, thank you. It matches what I'm seeing: the users who stick around aren't the ones with huge files, they're the ones who get messy CSVs from clients every week. Right now the tiers mix both axes (the free plan caps files per month, paid plans mostly differ on size), and I see your point about size caps feeling like a tax. The features that actually map to cadence — saved cleaning profiles, file history, and eventually an API for automated workflows — are probably the better foundation for the paid tiers. "Customers pay for cadence, not capacity" is going on a sticky note above my desk. One question, since you've sold into enterprises: did frequency anchoring work better as explicit limits (X files per week) or as workflow features that only pay off with regular use?
Workflow features, almost every time. Explicit limits make the customer count, and the moment they are counting they are either looking for the exit or gaming the cap. A weekly file limit trains people to batch their work and quietly resent you for it. Features that only pay off with repeat use do the opposite: saved profiles, history, and an API make the habit itself the reason to stay, so the value compounds the more they show up. Price the workflow, not the meter. Keep a generous floor so your weekly user never thinks about a limit, then charge for the things that get better with frequency: automation, integrations, audit trails. The cap should be the thing they never hit, not the thing they plan around.
As someone who recently built a tool to clean and normalize messy ingredient data, I feel your pain on a spiritual level. Dirty data is the ultimate time-killer for any project. Automating things like type casting and text cleanup without writing code is a massive time-saver. Definitely bookmarking DeepCleanCSV. Congrats on shipping!
Ingredient data sounds like its own special circle of hell — I can only imagine the unit inconsistencies. Glad the pain resonates (unfortunately). Thanks for the bookmark, and congrats on shipping yours too!
The "upload dirty, download clean" flow is exactly right for this audience. One thing worth testing: show a before/after row comparison on the landing page. The transformation is the product make it visible before anyone clicks.
That's a really good point — the transformation is the product. I have a landing update coming up (privacy section plus some polish), and a before/after comparison fits right in. Probably better than the demo video at communicating it in two seconds. Thanks for the nudge.
Like the project, really useful, I created a tool myself to clean up CSV files for real estate brokers.
Thanks! And that's a great niche — vertical-specific cleaning is something I keep hearing about. Curious what the messiest part of broker CSVs turned out to be; in my experience every industry has its own flavor of chaos.
For broker CSVs, the messiest part is usually not simple cleanup like nulls or duplicate rows.
The real pain is that the same property can appear in slightly different ways across sources: small address variations, different sqft formats, sale vs lease fields mixed together, cap rates missing or estimated, broker names written inconsistently, and sometimes no clear source/provenance.
So the hard part becomes less “clean this CSV” and more “can I trust this row enough to use it in a market file?”
That’s what pushed me toward more domain-specific fields like source links, duplicate signals, enrichment status, and notes explaining when broker/contact/cap-rate data is unavailable or estimated.
Generic CSV cleaning helps, but in real estate the context around the row matters a lot.
That "can I trust this row" framing is spot on — and honestly a much harder problem than what most cleaning tools tackle. DeepCleanCSV handles the generic layer (nulls, duplicates, types, encoding) but what you're describing goes deeper: provenance, cross-source dedup signals, estimated vs. confirmed fields. That's closer to a data reliability tool than a cleaning tool, and it makes total sense for real estate where a bad cap rate can cost real money. Would love to see what you built — do you have it online anywhere?
Really appreciate that framing — “data reliability tool” is much closer to what it became.
I initially thought I was building a simple way to pull CRE listings, but the more real broker-style data I tested, the more the problem shifted toward trust: source provenance, duplicate signals, estimated vs. confirmed fields, and making missing data explicit instead of hiding it.
The first public version is here:
https://apify.com/kazkn/commercial-real-estate-brokerage-intel?fpr=8fp2od
Right now it turns LoopNet + Crexi searches into a cleaner first-pass market file with source URLs, duplicate signals, cap-rate context, days-on-market when available, broker fields when public, and enrichment status so the user knows what was actually enriched vs. search-only.
Still early, but I’m trying to make the output honest rather than “perfect-looking.” Would genuinely love your take from the CSV-cleaning side: what would you expect to see in the output to make a row feel trustworthy?
I second the data privacy concern. I would love for it to reassure me the data stays in my browser but it reads as being uploaded, data science applied, then downloaded, hence leaving my local environment. That's a real trust issue.
Second thing, the speed. A lot of these tools get this part wrong, and you didn't. Lighthouse gave it a 96. The page shows up in about 1 second, nothing jumps around while it loads, and there's no lag when you start clicking. For a tool where people upload a file and expect it to just work, that fast and steady feel is quietly doing a lot for you. So this part is basically done.
Since the speed is sorted, here are a few small things I'd still tidy up. None are urgent. But for a data tool they all feed the same thing: trust.
There's a JavaScript error on the homepage. The console throws "Cannot read properties of null (reading 'children')" as the page loads. It may not break anything you can see, but it's the kind of thing a careful user spots in dev tools. For a tool about clean data, that sends the wrong signal. Worth a look.
The demo video is heavy. demo .mp4 is about 3.2MB, which is almost the whole weight of the page. It's fine today because everything else is light, but you could compress it, add a poster image, or only load it when it scrolls into view. Small change, smaller footprint.
Some text is hard to read. A few spots fail the basic contrast check. The white text on the blue buttons is a bit low, and the muted gray text on the dark background is harder to read. Nudging the blue a little darker and the gray a little lighter would help, especially for people skimming fast.
Tiny one: the page has no 'main' landmark in the HTML. It's a one line fix, and it helps screen readers and SEO.
So the hard part, making it fast and smooth, is already done well. The rest is just polish that makes it feel as solid as it actually is. As someone who does almost daily work with CSV based imports into CDPs, I will soon check out the actual tooling, but wouldn't yet trust it with real client data containing PII.
This is the kind of detailed feedback that's genuinely hard to get — thank you for taking the time.
On privacy: you read it right, and I won't pretend otherwise — processing happens server-side, not in your browser. Cleaning a 100 MB file with full type inference isn't something a browser tab can do reliably. What I can offer is strict handling: on the free plan your file lives only in server memory and is discarded within minutes — it never touches disk or storage. On paid plans, files are stored only for the history feature, with retention you control — and if you set it to zero days, nothing is ever written to storage at all, same as the free plan. None of this is visible on the landing page today, which as you say is the actual trust problem. A privacy section is going up soon.
On the four issues — console error, video weight, contrast, missing main landmark — all noted and going into the next landing update. You're right that a console error on the homepage of a data-cleaning tool sends exactly the wrong signal.
And fair call on the PII caution. If you do test it against your CDP import workflows, I'd genuinely value hearing where it falls short — that's precisely the use case I want it to be solid for.
That's exactly the reassurance I was missing, thanks for spelling it out. The zero-retention option being identical to the free plan is the detail that would put me at ease, so getting that onto the page should close the trust gap. I do daily CSV work into CDPs, so I'll give it a run on a sample file at some point. Nice job turning the feedback around this fast.
The CSV pain is real. I built BulkSheet partly because Shopify merchants kept hitting this from the other direction. Their workflow was: export product catalog as CSV, edit in Excel, reimport. Even when the data going in was clean, the round-trip introduced problems: encoding issues, Shopify's strict column format, Excel silently reformatting variant prices.
Curious whether you've looked at domain-specific dirty data patterns. Platform CSV exports from Shopify, WooCommerce, Amazon each have their own quirks that generic cleaning rules don't catch. That could be a strong differentiator if you're targeting ecommerce merchants specifically.
The Excel round-trip problem you describe is painfully familiar — silent reformatting is one of the worst offenders because nothing looks broken until the reimport fails.
Not yet — right now the cleaning rules are generic (nulls, dupes, encoding, type coercion, numeric precision) because I started from the ML/analytics use case. But there's a feature that points in the direction you describe: reusable cleaning profiles. Today users build their own; platform-specific presets ("Shopify catalog export", "WooCommerce products") would be a natural next step on top of that, and probably a better acquisition channel than generic cleaning.
Did you end up encoding Shopify's quirks as explicit rules in BulkSheet, or handling them case by case? Curious how deep that rabbit hole goes.
That is so simple yet so effective..I know many people who work with large amounts of data regularly and this tool would be so helpful to them
Thanks! Large files were actually a big focus — a 100 MB CSV goes from upload to cleaned in about 20 seconds. If you know people who'd find it useful, I'd love for them to try it and tell me what's missing.
Wow ,the UI seems really great and feels like engaging.
Thanks! That was a deliberate goal — data cleaning tools tend to feel like a black box, so I tried to make every step visible. Glad it comes through.
Hi, great tool i have just tested it and i love it, it can save a lot of time for a lot of people out there. You should also try to support excel files instead of csv file only, your tool has a lot of potential.
Thanks for testing it out! Excel support is actually next on my roadmap — .xlsx upload with automatic conversion. It's one of the most requested things so far, so it's coming soon.
Glad to hear it gonna be supported. Good luck with the project and i will surely recommend your tool to people who need it.
Cleaning data always seems to take longer than the actual analysis. I like that you're focusing on the repetitive cleanup steps that most people end up doing over and over again.
Thanks! That was the starting point for me too — I kept writing the same cleanup scripts for every new dataset. At some point it made more sense to build the tool once than to rewrite the script every time.
Great, a tool that, at first glance, has everything to be very useful, the problem is real and a headache for many. AI makes our work much easier, but we always have to do something that's still missing... CONGRATULATIONS and good luck on the journey that now begins!
Thank you so much! You nailed it — AI has made analysis itself so much faster, but it still chokes on messy input. "Garbage in, garbage out" didn't go away, it just moved upstream. That's exactly the gap I'm trying to close: making the boring prep step take seconds instead of an hour of pandas one-liners.
Really appreciate the kind words — the journey is just starting and feedback like this keeps the momentum going!
This is a very relatable problem. I’ve lost way too many hours cleaning messy CSVs before I could even start analyzing anything — dealing with inconsistent formatting, weird null values, and duplicate rows that somehow always sneak in.
I like that you focused on a no-code approach with configurable cleaning steps. That lowers the barrier a lot compared to writing custom scripts every time.
One thing I’m curious about: what are the most common or painful data issues you’ve seen users run into that were surprisingly hard to handle automatically? Also, how does the tool perform with very large files (tens or hundreds of thousands of rows)? Does it stay fast and reliable, or do you recommend splitting the file first?
Would love to hear more about the edge cases you’ve encountered so far.
Thanks! Those are exactly the issues that motivated this.
The edge case that surprised me most was ambiguous null-like strings — values like "none", "nil", "unknown", or "-999" that look like nulls but aren't. The tool detects which of those actually appear in the file and surfaces them in the cleaning config so the user can decide whether to convert them or not, rather than making that call automatically.
Mixed date formats in the same column are also painful — especially when Excel silently reformats some rows and not others.
On performance: the tool runs on Polars, which handles large files well. In tests with files around 100 MB — compression took 3-4 seconds client-side, upload and analysis 11 seconds, and cleaning with multiple operations configured under 5 seconds. Current file limit is 100 MB on the top plan, so hundreds of MBs aren't supported yet — but hundreds of thousands of rows within that range run fast and reliably without splitting.
Interesting build.
The thing I'd be careful with is that some problems consume a lot of time without necessarily creating a strong reason to buy.
That sounds obvious, but the distinction tends to matter more than most founders expect.
I wouldn't make that call casually in a thread.
Fair point - it's something I think about too.
The use case I've seen convert best is non-technical analysts who receive CSVs from external sources — clients, legacy systems, tool exports — and need them clean before a report or handoff. They hit the problem frequently but don't have a programmatic fix ready, so the deadline pressure actually lands.
Curious what you're picking up on though — happy to hear it if you see a gap in that logic.
Possibly.
The reason I stopped short earlier is that I don't think the interesting part is the explanation itself.
I think there's a more important decision underneath it.
I wouldn't try to unpack that properly in a thread.
If you'd like the tighter version, drop your email and I'll put it together properly.
Appreciate it, but I'd rather keep the conversation here — if there's a decision I'm missing, other founders in the thread would probably benefit from it too. Happy to read whatever version fits in a comment.
That's fair.
The reason I'm hesitant isn't that I think the observation is private.
It's that I think the decision that follows from it is easy to get wrong.
I'd rather stop at identifying the risk than casually tell someone what conclusion to draw from it.