KashifData

Intressting !

This solve a large pain point for Marketing agencies and i think its really unprecedented with its features : like the pptx and the shareable reports ... Awesome !

The "spreadsheet → shareable report" flow solves a real pain point, especially for agencies managing multiple clients with varying data formats.

One thing worth thinking about as you scale: the LLM KPI detection works well on clean, structured data — but production spreadsheets from clients are often messy. Inconsistent column names, merged cells, manually entered totals mixed with row-level data. A validation/pre-processing layer before the AI step could prevent confident-looking but wrong insights from reaching clients, which is where trust breaks down fast.

Also curious about the live report refresh story — when the underlying spreadsheet changes, does the report update automatically? That's usually the question clients ask second, right after "can you make this shareable."

Yes, making it look right is the hardest part. As a data scientist I found it more harder to present the findings well than actually finding it from massive amounts of data

The "spreadsheet to presentation" gap is real and massively underserved. Most analytics tools assume the hard part is the analysis — but for agencies the hard part is always the last mile: making it look right, fast, for a client who doesn't care how you got there. Curious how you're handling datasets where the KPI structure isn't obvious — does the smart detection work well on non-standard schemas?

"Founders couldn't read their own data fast enough" — that's the real insight buried in the About section. The bottleneck was never the analysis, it was the time between data and decision.

The shareable live report angle is smart for the agency use case specifically. A CMO doesn't want a dashboard login; they want a link they can forward in a Slack message before the Monday call.

Building RecebeZap — payment reminder automation for Brazilian freelancers. Different market, same root problem: most tools give you the raw data (who paid, who didn't) but stop there. The freelancer still has to figure out what to do next. The "finish the job" framing is going into how I describe what RecebeZap does.

Good luck with the launch, Sidimed 🚀

this is beyond what most tools offer today! Awesome

This hits a real pain point. In BI consulting I see it constantly — the analysis is solid but the last mile to a client-ready deck eats hours every week. Power BI and Tableau are great for internal dashboards but genuinely poor for "just send the client a polished report," which is exactly the gap you're addressing.

The spreadsheet → insight → report → presentation flow is the right abstraction. Agencies especially feel this because they're doing it across 10+ clients simultaneously, and the formatting/slide-building overhead compounds fast. CMOs want the story, not the pivot table.

If you're scaling the data processing side and hitting SQL performance questions as datasets grow, I put together a free query optimization guide that might be useful: https://growthwithshehroz.gumroad.com/l/psmqnx

The live report you mention is so powerful ! How it present the visuals, predective and prespective .. this sounds like a full cheap data scientist in your pocket

The one-line fix is the real lesson here. Most makers ship first and hope the math doesn't matter. You caught it before it hurt someone. That's the difference between a feature and a liability

Solid lesson here. And what's really dangerous with AI is how confidently they give their answers. If someone isn't careful, it's easy to believe the AI response without putting in a second thought.

I had run into a similar issue with LLMs early on -- I gave GPT the scenario of a some shares in a company and asked it how much dilution would be necessary if a new investor joined. It gave me a confident answer but it felt off to me. So I ran the calculations and saw it was off by about 1.2 million shares.

I know there are people to run these calculations but imagine if people were to rely on AI for these calculations. It could have devastating consequences and they wouldn't even know.

Same problem here. I'm building an AI coaching feature for an indoor rowing app and the early version would confidently tell users their pace was improving when it wasn't. Pre-computing the actual stats and only letting the LLM write the interpretation fixed it completely.

Being your own target user helps a lot too - when the AI tells you something wrong about your own training data, you notice immediately.

the pandas pre-compute fix is solid engineering, but honestly the bigger lesson in your post is the persona pivot at the end and you barely unpacked it. building for technical users who already have ChatGPT/Power BI is the most common mistake I see in AI tool startups right now. agency account managers who can't write SQL but own client retention, that's a real wedge. curious how you found that persona, did agencies actually tell you, or is it still a hypothesis ?

The "LLMs hallucinate plausible numbers with full confidence" line is exactly what we hit when we shipped AI claim review for collision shops. Early version was reading repair photos and "computing" supplement totals. The numbers looked right, the format looked right, they were completely made up.

Same fix philosophy as yours: stop asking the model to compute. We pre-extract every photo annotation, line item, and supplement history into structured JSON, then the LLM only writes narrative explanations and flags discrepancies. It's allowed to say "these two numbers don't match," it's not allowed to do the subtraction.

The persona pivot is the harder lesson though. Curious how you found the agency account manager persona?

Your journey is a perfect case study of stochastic entropy manifesting as confident failure. Standard AI analysts focus on syntax, but the bottleneck is always the underlying schematic of the logic. Fixing the outputs is the easy part; re-aligning the geometric progression of the model's reasoning is the true challenge. I'd be curious to know if you neutralized the latent bias in the initial logistics map of the system, or if you just optimized for local minima. Decipher the logic, avoid the local minimum.

The core problem you hit is exactly why I'm taking a different approach with AI in my project. LLMs hallucinate with full confidence and there's no way to know from the output alone whether the answer is real or made up.

Your fix is right for analytics. Pre-compute the facts, let the AI write the narrative. Separate the math from the language.

I'm building a blockchain where AI agents operate as protocol primitives and we enforce the same principle at the chain level. The AI can publish signals and store observations but every operation has fixed semantics that the protocol understands. The chain doesn't ask the AI to compute state. The chain computes state and the AI reacts to it.

It's the same insight you landed on. Don't let the AI do the thing it's bad at. Give it pre-computed facts and let it do what it's actually good at.

The persona shift from analysts to account managers is a good catch too. The person who needs the output is rarely the person who can build the tool.

The persona pivot at the end is the part that actually matters and it's easy to miss.

Marketing analysts already have Power BI, Python, and ChatGPT. They're fine. The agency account manager juggling 8 clients who's still building reports manually at 11pm Sunday that person has nothing. That's where the real problem is.

The hallucination thing is also something most people just quietly ship around. Separating the computation from the language model is the right call and honestly not obvious when you're deep in it. The fact you caught it before someone tanked a client's budget with it is the bit that matters.

Quick question, how are you thinking about trust with the account manager persona? These people are presenting directly to clients. One wrong number and they never open it again. Are you showing any kind of data source or confidence indicator so they can sanity check before the Monday call?

Rooting for this. The problem is real.

That’s a frustrating but very real experience AI can sound confident even when it’s off. The key takeaway is building strong validation steps and not relying on a single output. Iterating and refining like you did is what actually makes these systems smartplayofficial reliable. Curious to hear what checks or methods worked best for you.

ran into exactly this - confidently wrong metrics for weeks on my sprint planner. same root: I trusted the model to compute what I should've pre-computed first. the confidence is the tell.

ran into exactly this - confidently wrong metrics for weeks on my sprint planner. same root: I trusted the model to compute what I should've pre-computed first. the confidence is the tell.

The "pre-compute everything, let the AI write narrative only" pattern saved
my product too.

I was building analysis reports where the AI generated citations with sample sizes, percentage breakdowns — all looked professional, all fabricated.

Same thing you described: confident, wrong, and dangerous.

The fix was identical to yours. Every number comes from a verified source or a real calculation. The AI's job is storytelling, not math.

The moment I stopped treating the LLM as a calculator, accuracy went from maybe
60% to essentially 100%.

The persona pivot is the other gem here. "The person who needs the output, not the tool"

-that reframe alone is worth more than any technical fix.

I went through the same shift: stopped targeting people who already have solutions
and started targeting people who don't even know the problem exists.

How are you handling cases where the pandas pre-computation itself has edge cases? Like division by zero on spend columns or channels

with single-day data skewing the aggregation?

This is the real issue. The danger isn’t just bad output, it’s output that sounds convincing enough to act on.

A lot of the opportunity in this space is not making AI louder, it’s making the analysis more grounded and more honest about what it knows, what it doesn’t, and what needs checking next.

This is painfully relatable. I've built TrendyRevenue (AI idea validation – live) and ran into the exact same hallucination problem. LLMs are confident liars when you ask them to do math on raw data.

Your fix – pre-compute in pandas, feed clean JSON – is exactly what I landed on too. But one thing I'm still struggling with: how do you handle edge cases where the data structure varies wildly (different column names, missing values, weird formats)? I'm doing some basic sanitization but it's brittle.

Also, the 'wrong persona vs right persona' point – I think I'm making the same mistake. I built for 'founders' but maybe I need to narrow to 'solo devs with a failed product'. How did you validate the persona shift before rebuilding messaging?
Live link without signup is bold. Respect.

ok

Hit the same wall in a different domain (city ranking grounded on Census/BLS/climate data). The pandas-as-truth, LLM-as-narrator split is the right end state. Two things I'd add from the layer past your fix:

1. The model will sometimes ignore your structured facts and re-derive them from training data, especially when your numbers are unusual. Fix: name the values explicitly in the prompt (cost_burden=39%) and tell the model to quote them in its narrative. If the quote drifts from input, a deterministic check flags it before it hits the user.

2. Pin a small golden set on the narrative side too. We saw verdicts drift across model versions even with locked numbers. 8 datasets, 8 known-correct narratives, cheap LLM-as-judge on every prompt change. Catches the "numbers right, conclusion reversed" failure mode.

The persona shift is the bigger insight here. Account managers wanting confidence over flexibility maps to half the AI categories I can think of right now.

This hits hard — I made the same mistake early on, trusting LLM math and got “perfect-looking” wrong reports 😅
Pre-computing in code and letting AI just explain it is the only thing that actually made my outputs reliable too.

This is the part most “AI analyst” products get wrong: the dangerous failure mode isn’t an obviously bad answer, it’s a polished answer with the wrong numbers.

The pandas → clean facts → LLM narrative pattern makes a lot of sense. Let deterministic code do the accounting, let the model do the explanation. That separation is probably the difference between a cool demo and something an agency can actually trust with client decisions.

The persona shift is also underrated. Analysts may want flexibility, but account managers want confidence, speed, and a report they can send without embarrassment.

The next moat might not be “better AI,” but better trust UX: showing the source calculation, highlighting assumptions, and making it easy to verify why a recommendation was made.

This is a solid lesson, but the real issue wasn’t just “LLMs are bad at math”—it was a separation of responsibilities problem; you were asking a probabilistic system to do deterministic work, and it gave you something that looked right instead of something that was right. The fix you implemented (pandas for computation, LLM for narrative) is the correct architecture, and honestly should be the default pattern for anyone building “AI analysts,” especially in high-stakes domains like marketing spend. The more interesting part is your persona shift—that’s where the product actually becomes viable, because non-technical account managers don’t care how it’s computed, they care that it’s fast, clear, and trustworthy. The next challenge isn’t generating reports, it’s proving accuracy and building trust at a glance (e.g., showing how numbers are derived, confidence levels, or quick validation hooks), otherwise you risk the same “looks right but…” skepticism coming back. How are you planning to make users trust the output without having to double-check it themselves?

Sounds like the updates changed everything and i really liked the live shareable report and the pdf free download

The biggest advantage in your kashifdata is that it supports Arabic easly which make it stand in the race alone and far away from those complex BI tools like power bi, looker and tableau .. We the arabic world are very thinkful for this product

I subscribed in Kashifdata starter and honestly i can say its really valuable and worth its price. The speed and the accuracy of the report results, the powerpoint file , and the most important feature .. the sheareable link that i can share it publicly or privaltly with a password with my stakeholders .. those features are very impressive and unprecedented honestly ... keep improving

The live report is so powerful and very insightful .. This is genuily helpful . Thanks !

I think this solve a lot of reports generating headache .. Its really great

board-ready is a strong claim. generating charts is the easy part - boards go wrong not from missing visuals but from asking the wrong questions. what helps the user know what question to ask the CSV?

Amazing

wonderful updates

the use cases you added are great and add trust

great question

Looks interesting — I like the “$9 vs $100/hr” positioning, it’s very clear 👍

The 5-phase concept sounds strong, but the real question is: how actionable is the final output? If the “Phase 5 Verdict” gives clear next steps (not just insights), that’s where this could really stand out.

$9 feels almost too cheap — great for traction, but you might undervalue it if the results are actually solid.

I’d suggest: focus on showcasing real before/after use cases. That will sell it more than anything else.

This is a really interesting angle, especially the “impulse buy” pricing — you don’t see many tools deliberately aiming that low from day one.

One thing I’m curious about: who’s the ideal user you’ve seen so far actually get value from it? Because in my experience, a lot of founders say they want “insights”, but what they actually act on tends to be very specific (like pricing, funnel drop-offs, etc.).

Also agree with your point about people “flying blind” — I’ve been building something in the clinic comparison space and ran into a similar issue, just in a different form. Lots of data out there, but very little that’s actually structured in a way people can decide with.

Feels like the real challenge isn’t analysis — it’s making the output actionable.

Would be interesting to see an example report (even anonymised) — that would probably convert a lot better than explaining the phases.

Thanks a lot Kashifdata

Thanks, KashifData for this service .. wonderful

The speed and the quality of the report are insane !!!
Very good .. keep it up

This is unprecedented !!
The report is very hepful

I think RIP for data Analysts

This helped me a lot

What an idea !!
That 9$ it's not a thing .. i think

This comment was deleted 5 months ago