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Proprietary product · Open‑text analysis

Qualitative in. Quantitative out.

Spoiler-AI is Outsized Insights’ qualitative-to-quantitative conversion platform — purpose-built for any team with open-text data — survey verbatims, support tickets, interview transcripts, app reviews, employee feedback — that needs to become structured, analyzable data without handing it to a third-party AI service. Unlike generic AI tools that pipe your responses through OpenAI or Anthropic, Spoiler runs its own AI on its own servers.

Explore Spoiler-AI

Your data goes to Spoiler. And stops there.

01 · Security

Built on Meta’s open Llama 3.3 model — customized by Spoiler for survey work, and run only on Spoiler’s servers. Built into the system, not just a promise.

  • No OpenAI, no Anthropic, no Google — no third-party AI of any kind
  • Your data is never used to train AI — not ours, not anyone else’s
  • Encrypted storage, daily backups, US data centers — yours to delete

Minutes, not meetings.

02 · Speed

Reading and tagging by hand takes weeks. Spoiler gives every job its own GPU horsepower — hardware reserved for you, not a shared queue.

  • Insights in minutes; fully tagged datasets in hours
  • Dedicated GPU per job — no waiting, no rate limits
~9 min
Spoiler
~6 wks
Reading by hand

More than an LLM. A purpose built data quality pipeline.

03 · Precision

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

  • Reproducible across runs, reviewable at every step
  • Every theme traceable to the rows behind it
  • Built to show its work — not “trust me, the model said so”

From open text to structured data

  1. 01 Discover Spoiler analyzes your responses and surfaces the patterns it finds — themes that emerge from your data, not from a preset list.
  2. 02 Refine Open any cluster to see the responses inside — the evidence is right there as you merge, split, or rename.
  3. 03 Sync Every edit reassigns responses in real time, so the codeframe and the data stay consistent.
  4. 04 Deliver Confirm the final taxonomy and Spoiler returns a fully coded dataset, ready to analyze.

It’s cheaper to build an AI tool by calling OpenAI. We just didn’t think that was the right tradeoff.

Common questions

Isn’t this just a wrapper around a general AI?
No. Spoiler runs its own AI on its own servers, so your data only goes to one place: us. No OpenAI, no Anthropic, no Google — no third-party AI of any kind.
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.
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. You pay per job — for the work, not a seat or a subscription.
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.

Talk to us about a pilot on your data.

Custom-built infrastructure. Evidence-backed output. No foundation-model middlemen. You pay per job — for the work, not a seat or a subscription.

Explore Spoiler-AI