"too expensive for everyday" "love the taste but not worth it" "carbonation feels aggressive" "demasiado caro para mí" "wish it came in smaller cans" "hard to find at my local store" "artificial aftertaste, not for me" "trop sucré pour mon goût" "ran out last two times I looked" "not worth the premium" "would buy weekly if cheaper" "packaging is misleading" "pricing unclear on multi-pack" "too sweet / cloying" "prefers competitor flavor" "wants more SKU variety"

"nunca está disponible online" "needs a sugar-free option urgently" "ads don't match the taste" "es bueno pero demasiado dulce" "gave it as a gift, they loved it" "been buying this brand for years" "checkout flow is broken on mobile" "brand feels too corporate now" "love love love but price" "compared to store brand, not worth it" "4 dollars a can is wild" "I'd buy daily if 3 bucks" "stocked out every weekend" "flavor is great, size is wrong" "would share more if cheaper" "more zero-sugar options please"

"not as good as it used to be" "aftertaste lingers too long" "sabor artificial que no me gusta" "delivery was 4 days late" "price crept up quietly, noticed" "label looks cheap vs old design" "recommended by a friend" "been my go-to for road trips" "wish I could subscribe and save" "too fizzy, hurts my throat" "not at my grocery store anymore" "would like a lower-sugar version" "quality dropped this year" "cans dent too easily" "I want a glass bottle option" "el precio es el problema"

"worth every penny for special nights" "marketing is misleading on sugar" "prefer old flavor profile from 2019" "need a family-size pack" "mixing it with food works better" "ran out of stock pre-holiday" "customer service was helpful" "subscription option would win me" "no puedo encontrarlo en mi ciudad" "packaging is not recyclable" "fizz is too much for kids" "app ordering is broken" "brand lost its edge" "tastes better ice cold" "overpriced at this quality" "wish there was a mini version"

"會買如果便宜一點" "two stars for availability" "would buy for a gathering" "prefer my local brand" "taste is inconsistent batch to batch" "need clearer nutrition labels" "delivery driver left it in sun" "refrigerator space is a problem" "size options are limited" "flavor fades after opening" "good but not great" "brand too sugary for my kids" "honestly cheaper alternatives exist" "been loyal since college" "can't justify weekly cost" "subscription model would work"

01 · the problem

Turn this

12,104 open responses. Five languages. One deadline.

12,104open responses

5languages

~6 wksto read by hand

Scroll

02 · the output

Into this.

A defensible, quant-ready dataset. Every response tagged. Every theme traceable to the rows behind it. Back in hours, not weeks.

Request access Why it's different

beverage_q3_open_responses.coded.xlsx Live

#VerbatimCodesSent.

00012 not at my grocery store anymore not in my store -0.62

00347 artificial aftertaste, never going back tastes artificialaftertaste issues -0.78

01124 love love love but pricey tastes greatnot worth premium +0.04

04891 overpriced for what you get tbh not worth premium -0.51

07203 tastes great but cans dent way too easily tastes greatcans dent easily -0.05

09556 delivery was 4 days late, taste fine though delivery delaystastes great -0.24

Theme distribution · 12,104 responses · 14,238 codes

12,104 responses

Taste & flavor32.5%

Availability26.0%

Price / value19.6%

Packaging size11.8%

Sugar content7.9%

Delivery speed2.2%

Taste & flavor

32.5%

Availability

26.0%

Price / value

19.6%

Packaging size

11.8%

Sugar content

7.9%

Delivery speed

2.2%

out of stock

13.8%

tastes artificial

11.4%

aftertaste issues

9.7%

too expensive

9.5%

not in my store

8.2%

flavor declined

6.8%

not worth premium

6.4%

tastes great

4.6%

too sweet

4.6%

cans dent easily

4.6%

online unavailable

4.0%

size too big

3.9%

cheaper alternatives

3.7%

want smaller cans

3.3%

want sugar-free

3.3%

delivery delays

2.2%

Taste & flavor32.5%

Availability26.0%

Price / value19.6%

Packaging size11.8%

Sugar content7.9%

Delivery speed2.2%

Responses12,104

Codes applied14,238multi-coded

Themes6

Wall time9:14m:s

01Security

Your data goes to Spoiler. And stops there.

Most AI tools split your data across two companies — the app you signed up for, and the AI service it calls. Spoiler doesn't.

No one else gets your data. No OpenAI, no Anthropic, no Google, no third-party AI of any kind. Your data reaches Spoiler and stops there.
Built with Llama. Our AI is built on Meta's open Llama 3.3 model — customized by Spoiler for survey work, and run only on Spoiler's servers. Not Meta's. Not anyone else's.
Your data is never used to train AI. Not ours. Not anyone else's. Ever.
Stored safely. Yours to delete. Encrypted storage, daily backups, US data centers. Delete your account and your data goes with it.

Built into the system. Not just a promise. · Read the full security statement →

Your open-text dataExcel · CSV · API

SpoilerUS-based, fully owned

Owned perimeter

OpenAI / Anthropic APINever reached

Foundation cloudNever reached

Training poolsNever reached

Only Spoiler sees it. No outside AI. Never used to train.

03Precision

More than an LLM. A pipeline.

A single-model pass is fast but brittle. Spoiler runs every response through several layers — each a different lens.

Multiple layers, multiple lenses, one defensible answer.
Reproducible across runs. Reviewable at every step.
Built to show its work — not "trust me, the model said so."

"Codes you can defend."

12,104 open responses in

Discover

Spoiler analyzes your responses and surfaces the patterns it finds — themes that emerge from your data, not from a preset list.

Refine

Open any cluster to see the responses inside — the evidence is right there as you merge, split, or rename.

Sync

Every edit reassigns responses in real time, so the codeframe and the data stay consistent.

Deliver

Confirm the final taxonomy and Spoiler returns a fully coded dataset, ready to analyze.

Structured, analyzable data out

Common questions

The questions every data lead asks first.

Direct answers to the four objections we hear in every first call.

Q · Architecture

Isn't this just a wrapper around a general AI?

No. Most AI tools are wrappers — meaning your data passes through both the tool you signed up for and the AI vendor it calls. Spoiler runs its own AI on its own servers, so your data only goes to one place: us. Different architecture, different security, different speed.

Q · Model

Why purpose-built models over a general-purpose AI?

Because tagging open-text data isn't a general-intelligence problem — it's structured analytical work. Spoiler is a multi-layer pipeline built specifically for that job, not a general-purpose chatbot approximating it.

Q · Cost

Isn't running your own AI expensive?

Yes — it's cheaper to build an AI tool by calling OpenAI. We just didn't think that was the right tradeoff for the data you'd be handing over. So we built ours on Meta's open Llama foundation and run it on our own servers. You pay per job — for the work, not a seat or a subscription.

Q · Compliance

What if my data has PII?

Your data stays inside Spoiler — no outside AI service downstream, and we never use customer data to train any model. Full handling details (where it lives, how long, how to delete it) are in our security statement.

Turn this

Into this.

A qualitative-to-quantitative conversion platform.

01Security. 02Speed. 03Precision.

Your data goes to Spoiler. And stops there.

Minutes, not meetings.

More than an LLM. A pipeline.

The questions every data lead asks first.

Isn't this just a wrapper around a general AI?

Why purpose-built models over a general-purpose AI?

Isn't running your own AI expensive?

What if my data has PII?

Qualitative in. Quantitative out.