Unlock AI capabilities across your entire product stack — from feedback, to marketing, to community, and support.
Reading, right now, continuously
Discord Reddit Steam YouTube Twitch Discourse
GitHub App Store Google Play Instagram TikTok
X Press Support Docs Reviews
Raw platform data burns tokens
Kinn transforms unstructured data to be AI-ready.
Community platforms store conversation, not structure. The same problem arrives a hundred times in a hundred phrasings, scattered across places that never talk to each other, with nothing marking which ones matter.
Volume
The problem
One active Discord makes more text in a week than any model can hold. Your AI reads a sliver and fills in the rest.
With Kinn
Ranked and deduplicated before the model sees it.
Context cost
The problem
Every question re-reads raw messages. You pay for the noise each time you ask.
With Kinn
95% less context per question.
Cross-platform
The problem
Each connection is its own silo. The same bug on Steam, Discord and Reddit stays three conversations.
With Kinn
One issue, three platforms attached as evidence.
Tracing
The problem
A pasted export has no source. You cannot check a claim, so you cannot act on it.
With Kinn
Every answer cited to the message it came from.
Constantly improving and maintenance-free.
Trust and efficiency are built-in. Take it further with skills, tools, and enhancements designed specifically for product and marketing teams.
MemoryYour features, versions and past patches persist between questions.
Your languageJargon, nicknames, build numbers and system names, learned over time.
ConfirmationLined up against your own error and performance data, so a complaint can be confirmed rather than counted.
UpkeepPlatform APIs change constantly. Breakage is our problem, not your Thursday.
Clustering actionable evidence from noise.
Dozens of people hit the same thing across three different platforms and describe it dozens of different ways. Here is the path from those messages to one answer you can act on.
Illustrative example, not customer data — the path is the same for a bug, a feature request, a refund or a review
From teams
already running it
Kinn unlocked a new level of player feedback. The research opportunities it enables are endless.
Sam CosterCEOButterscotch Shenanigans
Its sentiment scoring is top-notch, and it catches bug reports across all our platforms.
Jarvs TaskerHead of CommunicationsHappy Volcano
Kinn solves the reporting pain points we hit every week. A serious advantage for player retention.
Taylor RodriguezFounderEmber & Forge
Your AI already knows how to talk with us.
One MCP server, open to Claude, Claude Code, Cursor, ChatGPT and anything else that speaks the protocol. No SDK, no glue code, no export.
Speaks to
95% fewer tokens used
Answering the same question
Bars are proportional to tokens consumed on a like-for-like question. The saving comes from what the model never has to read — not from a smaller answer.
Context stays intact
Work a year of Discord in one session without the window filling up. The model holds the shape of the problem instead of the transcript.
A gateway, not a dead end
The same server your team queries by hand is the one your agents call. Anything you can describe, you can build against.
Your data, your model
Bring the AI you already pay for. Nothing you connect is used to train anyone’s model, ours included.
A crash report, a feature request, a returns spike. Different industries, different vocabulary, the same shape of problem underneath: too many people saying the same thing in too many places to count by hand.
Dev tools &
open source
Scattered issues, discussions and forum threads become one ranked view of what contributors actually need next.
For open source →
Game studios
& publishers
Catch the bug reports, balance complaints and sentiment shifts driving churn — before they reach your reviews.
For studios →
Brands
See what is driving refunds, bad reviews and support tickets — so product, support and marketing act on one signal.
For brands →Specialized processes, running all day and night.
Nothing waits for you to open a dashboard. By the time you ask, the work is done.
Capture
Every message from every source you approve. Nothing scraped behind your back; deleted content drops out of the index.
Cluster
The same complaint said a hundred ways becomes one issue, with its real weight attached and its sources kept.
Synthesize
Background automations surface what is rising, and anyone on the team can just ask — both grounded in the evidence.
Decide
The specific call to make, ranked by severity and impact, with the threads behind it one click away.
The answer lands where the work happens.
Kinn does not ask your team to move. Findings go out as tickets, alerts and backlog items in the tools they already have open.
Automations you set once
- When
- Then
A cluster crosses 50 mentions
Draft a Jira ticket with the repro, the severity and every thread attached as evidence.
- When
- Then
Sentiment drops after a release
Post the three driving issues to Slack, in the channel that owns the build.
- When
- Then
A request keeps coming back
Add it to the Linear backlog carrying its real weight, not one person’s anecdote.
Build your dream tool on our data structure
What would you build with perfect data?
Kinn is a pipeline, not a destination. Once every message is captured, deduplicated, weighted and cited, the tools your team keeps buying become things your team can simply build.
Build it yourself / 01
An automated QA system
Every incoming report deduplicated against the known issues, checked against your error traces, ranked by how many real players hit it, and filed with a repro before anyone on QA opens a tab. The triage happens whether or not someone is at a desk.
02
Social listening, built to your brief
Track the things you actually care about, in your own vocabulary, without paying for a dashboard of generic brand metrics.
03
A contributor leaderboard
Who is answering questions, filing good issues and holding the community up — across GitHub, Discourse and Discord at once.
04
A release-day war room
One live view of what broke in the first six hours, ranked by blast radius and confirmed against your own telemetry.
05
Sentiment by region, in every language
Incoming content translated on the way in, so a complaint in Portuguese counts the same as one in English.
06
Whatever you need next
The MCP server is the same one we use. If you can describe the tool, your team can build it on top of the pipeline instead of buying it.
One pipeline underneath, instead of five contracts on top.
Build the right things faster. Stop guessing the roadmap.
Your community data, handled with care.
Sources you approve
Every connection is explicit. Nothing is scraped behind your back, and deleted or moderated content drops out of the index.
Your data does not train AI models
Your community’s words stay your community’s. Nothing you connect is used to train anyone’s model.
Bring your own AI
A full MCP server means your team can query Kinn from Claude, Cursor or its own agent stack. Your data, your model.
Want to keep your data in-house? Talk to our team about local deployment!
Stop guessing what to build.
Connect one source and ask it something today. No dashboards to babysit, no reports to assemble.