Is Affogata only for gaming? Real insights from noisy data across 7 industries

Published October 12, 2026

We hear this a lot. Just last week a prospect at a big consumer platform replied: “seems like you focus on gaming”. Fair enough. Gaming is where we started, and some of the biggest studios in the world run on Affogata. But the tech was never about games. Affogata is the Unstructured Data Enablement Layer: real insights from noisy data. It plugs into any unstructured data, cuts through the noise, finds the real signal, and ties it to the numbers your business already tracks.

Here is what that looks like across industries, with real numbers from one week of our public benchmark.

Is Affogata only for gaming?

No. Affogata works on any unstructured customer data: reviews, social posts, comments, community chats, support tickets and surveys, in many languages. Today that covers consumer apps and platforms, fintech and trading, commerce and delivery, travel and mobility, media and streaming, dating, education and wellness, gaming, public affairs and alternative data.

The industry changes. The job does not: find what people really think, filter out what is noise, and connect it to what the business cares about.

How do I add public social and community data to Claude, ChatGPT or my AI agent?

Add Affogata’s MCP (Model Context Protocol) connector, https://app.affogata.com/mcp, to your AI assistant or agent. It gives Claude, ChatGPT, Gemini Enterprise, GitHub Copilot and your own AI agents public social and community data with spam, bots and promo noise filtered out, and every answer comes with the numbers and the posts behind it.

On a paid account the same connector answers from your own data, with the permissions you already have.

Why did Affogata start in gaming?

Players are the toughest crowd on the internet. Slang changes every week, sarcasm is the default tone, memes carry half the meaning, bots and promo spam flood every launch, and one patch can move a huge wave of posts in a single day.

Reading that correctly forced us to build the hard parts first:

• Noise filtering that holds up during spikes, when spam and bots are at their loudest.
• Sentiment that gets sarcasm and slang, across languages.
• Clustering that finds the real topic, not the loudest keyword.
• Version-level scoring: scores tied to releases, so you see what a version changed, not just how a week felt.

Gaming pushed us to build capabilities for slang, sarcasm, noisy communities and sudden conversation spikes. We apply those same capabilities to customer feedback in every other industry.

What does “insights, not data” mean?

The real value isn’t the data, it’s the insights. Raw social data is not a clean signal of anything on its own. A big part of it is noise: spam, bots, giveaways, promo chatter and reposts. Count it raw and you mostly measure noise.

So the first job is to strip that out and keep the real opinion. The second job is to make it mean something for the business: we correlate the real signal with raw business metrics like product usage, churn, revenue and even stock movement. That is when “people are angry about the new UI” turns into something your product, CX and finance teams can act on together.

The public benchmark below shows the signals we extract from customer conversation. To see how they relate to business performance, teams connect those signals with their own usage, churn or revenue metrics.

What does one week look like across seven industries?

From our public benchmark for week 40 (Sep 28 to Oct 4, 2026): 38 apps outside gaming, more than 79,000 customer posts and reviews, each app scored 0 to 10 on its current version. Same model we use for games.

*Small sample that week, so read the wellness range with care. Scores come from public data. Brands in our benchmark are public examples and are not necessarily Affogata customers. Each industry links to its live category page.

Loud is not the same as happy. The most talked-about music app that week had 17,450 posts and scored 6.2. The top-scoring neobank had about half that volume and scored 9.5. Volume and sentiment are two different signals, and you need both.

Two more things stand out:

• The same pain shows up in different industries. Subscriptions and paywalls are the shared complaint across dating, education and wellness. In fintech it is money you can’t reach, and in shopping, travel and streaming it is fees and support.
• The spread inside one category is wide. Dating ran from 2.0 to 8.0 in the same week. Benchmark against your own category, not against the internet.

Public affairs and alternative data are not part of the public benchmark, but they run on the same layer. Research teams use it to read public opinion on policy, institutions and brands across languages, and investors use the cleaned signal next to company KPIs.

Want the “why” behind these moves? The Insights Model maps the events behind each spike, with the posts that prove them. Open it by industry: payments, trading, dating, education, wellness or gaming.

What do teams do with it?

• Product: what changed after a release, which feature drives the drop, this version against the last one.
• CX and support: support tickets next to public chatter, so a broken flow or an outage shows up before the queue fills.
• Research and insights: one clean read across markets and languages, without building a pipeline first.
• Data and AI teams: structured, deduplicated insight data in the warehouse, ready for models and dashboards.
• Leadership and finance: the signal next to churn, revenue and the share price, so the story behind a number is clear.

How can teams use it: UI, DIL, MCP or API?

Same data, same definitions and the same governance, whichever way you come in:

• UI, for every team, no setup: dashboards, conversation clusters and Helper, Affogata’s built-in AI assistant.
• DIL (Data Integration Layer), for data teams: live, structured tables in your data warehouse, ready for BI and models.
• MCP, for anyone with an AI assistant and for AI agents: ask Claude, ChatGPT, Gemini Enterprise, GitHub Copilot and your own AI agents, and get answers with the posts behind them.
• API, for developers: pull it into your own tools and workflows.

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