Your community tells you everything. Now you can hear all of it.
Until now, feedback tools gave each response one sentiment score and called anything complicated mixed. Communiti now reads every submission the way your best analyst does: every issue, its own sentiment, and the resident's exact words - filed straight into your dashboard topics, in more than 50 languages.
One response, two real issues
"I really love the train service, but I hate how terrible the bus delays are getting!"
Every other tool we tested scores this whole response as mixed, and the trains and buses both disappear.
At a glance
Three ways to analyse the same response
Take that one sentence about trains and buses, multiply it by five thousand responses, and here is what each approach actually gives you.
| Capability | Reading it yourself Analyst + spreadsheet | Existing platforms Standard sentiment tooling | Communiti Intelligence New - issue-level analysis |
|---|---|---|---|
| What this response becomes | Complete Both points noted - when there's time to read every row closely | Collapsed One label for the whole response: "MIXED". Trains and buses both vanish | Complete Trains · positive and Bus delays · negative, with the resident's words highlighted |
| Issues residents imply but never name | Caught A good reader sees "twenty minutes on hold" for what it is | Missed Found about 1 in 100 in our testing - there's no named "thing" to tag | Caught Found 9 in 10, scored and filed like any other issue |
| Feedback in community languages | Sometimes Only the languages your team happens to read - unless you pay for a translation service | English only Issue-level analysis supports no other languages | 50+ languages 10 benchmarked natively, including mid-sentence switching - nothing skipped |
| Filing issues under your report topics | By hand Every issue copied and categorised manually - and each reader files differently | Not available No concept of your project's topics | Automatic Filed into your topics as it reads - rename or merge topics any time |
| Evidence behind every finding | By hand Quotes pasted into the report, hours of copy-and-paste | Not linked A score, and at best keyword tags - the words aren't tied to the sentiment or your topics | Built in Every issue links to the exact words in the original response |
| 5,000 responses | Weeks And consistency drifts as readers tire | Hours Fast - but you get one shallow score per response | Under 3 hours Your best analyst's read, at machine speed, the same rules on row 1 and row 5,000 |
What this looks like in action
Watch feedback become understanding
Below, responses from our published test set arrive exactly as residents wrote them - different languages, different styles, half-finished sentences. Watch each one get read, separated into issues, and filed onto the dashboard. Then see what opening one topic gives you.
Survey export · English
"Love the weekend markets, but the lack of toilets there is a disgrace."
Buses
0 issues
Swimming pools
0 issues
Car parking
0 issues
Parks & playgrounds
0 issues
Council services
0 issues
Community programs
0 issues
Issues file themselves under your project's topics as each response is read.
Buses
Topic opened - what your team sees
- 23
- issues raised
- 78% negative
- sentiment
- 4
- languages
- Delays & shelters
- most-raised themes
"I hate how terrible the bus delays are getting!"
"公交车总是晚点, but the new library hours are very helpful."
"the buss never come on time an wen it do its full"
"E lelei le polokalame mo tupulaga, but the bus shelter leaks when it rains."
Animated illustration using responses from our published benchmark corpus - every highlight, issue, and filing decision shown here is a real output from the evaluation runs.
Tested before we shipped it
Benchmarked on the messiest feedback we could write
We built a 217-response test set designed to break sentiment tools: typos, sarcasm, rambling voice transcripts, low-literacy writing, and ten languages. The full method and results are published alongside this page.
- 92%
- of issues found, even in deliberately messy feedback
- 99%
- sentiment accuracy on the issues it finds
- 100%
- of mixed responses split into their real issues
- 10
- community languages benchmarked natively, including Te Reo Māori and Samoan
Head to head № 1
The industry-standard tool misses what residents do not name
Most platforms and many in-house projects run on Amazon Comprehend. It tags things it can recognise. But residents rarely name things. They write, "Twenty minutes on hold just to ask why my rates notice changed." There is no customer service in that sentence, only the experience of it.
What Communiti reads
Issue-level analysisI want to start by saying the new playground at Riverside Park has been wonderful for our family - my kids ask to go every weekend. But getting there is another story. We waited forty minutes for a bus the timetable said ran every fifteen, and when one finally arrived it drove straight past because it was already full.
My mother is in her late seventies and can't manage the walk from the closest stop - the footpath on Hartley Street has been lifted by tree roots for over a year. She has stopped coming with us. And the water pools right across the path near the underpass every time it rains - you need gumboots just to get through.
On the plan itself, I support the new cycleway and would use it to ride to work, but nothing in the document mentions lighting - walking back from the 6pm session it was pitch black by the carpark. Twenty minutes on hold when I rang to ask about session times didn't help either.
0 of 8 issues found
What Amazon Comprehend sees
Targeted sentiment, best settingI want to start by saying the new playground at Riverside Park has been wonderful for our family - my kids ask to go every weekend. But getting there is another story. We waited forty minutes for a bus the timetable said ran every fifteen, and when one finally arrived it drove straight past because it was already full.
My mother is in her late seventies and can't manage the walk from the closest stop - the footpath on Hartley Street has been lifted by tree roots for over a year. She has stopped coming with us. And the water pools right across the path near the underpass every time it rains - you need gumboots just to get through.
On the plan itself, I support the new cycleway and would use it to ride to work, but nothing in the document mentions lighting - walking back from the 6pm session it was pitch black by the carpark. Twenty minutes on hold when I rang to ask about session times didn't help either.
0 of 8 found 0 missed
Comprehend's export for this submission contains 2 issues. The other 6 - the ones the resident implied but never named - never arrive in any report. The charts below score the same gap across all 60 submissions.
Issue detection score - long written submissions
60 multi-issue submissions, 700 issues to find, same scoring for both
Comprehend left 233 significant issues unreported across those 60 submissions. Communiti left none.
Issue detection score - issues implied, never named
96 responses like, water comes over the kerb every time it rains
This is most of what consultation feedback looks like, and it is structurally invisible to entity-tagging tools.
Head to head № 2
Feedback in 50+ languages - read, not skipped
Residents write the way they speak - sometimes switching language mid-sentence: "公交车总是晚点, but the new library hours are very helpful." Communiti reads the complaint about buses and the praise for the library. It supports more than 50 languages, and the ten below are the ones we benchmarked natively, end to end. Comprehend's issue-level analysis supports English only.
- Mandarin 中文
- Arabic العربية
- Vietnamese Tiếng Việt
- Cantonese 廣東話
- Punjabi ਪੰਜਾਬੀ
- Greek Ελληνικά
- Italian Italiano
- Hindi हिन्दी
- Te Reo Māori
- Samoan Gagana Sāmoa
These ten are the natively benchmarked set - 40+ more are supported via automatic translation. Industry-standard issue-level analysis: 0 of 10 supported.
Head to head № 3
Could we just paste it into an AI assistant?
Fair question. We tested it properly, including with the same AI model we use ourselves. Today's AI assistants read well. The difference is what arrives at the other end: a consultation dashboard needs every issue filed under your topics, with the resident's words attached, for every row in the spreadsheet, every time.
Share of issues that arrive in your dashboard, correctly filed
60 long submissions, found and filed under the right reporting topic
The assistant finds issues, then names its themes differently on every row - so almost nothing lands in your reporting structure without someone re-filing it by hand. That re-filing is the job Communiti does.
How it works
Nothing new to learn
Upload as usual
Spreadsheets, survey exports, documents, audio transcripts, photos of workshop sticky notes - exactly as you do today.
Every issue surfaces
Each response is read for every issue it raises, with its own sentiment and the resident's exact words highlighted.
Your dashboard fills itself
Issues are filed under your project's topics automatically, and you can rename or merge topics at any time.
For your IT and governance teams
Built for Australian data requirements
Processed in Australia
Analysis runs on AWS in Sydney and Melbourne using Australia-geographic AI infrastructure. Feedback is not processed offshore.
Never used to train AI
Your community's feedback is not used to train any AI model, and the model provider has no access to it - contractually guaranteed by AWS.
Evidence on request
The complete benchmark pack - test data, scoring code, raw outputs, and methodology notes - is available for technical review.
Every claim traceable
Each issue links to the exact words in the original response. Nothing is summarised beyond what a reviewer can verify in one click.
How we measured - the fine print we think you should read
- Test data. All benchmarks use synthetic feedback written for testing, with no resident data, spanning 473 responses across two test sets: a 217-response stress set and a 256-response set of long submissions, implied issues, and mixed-language responses. Headline accuracy figures of 92% and 99% come from the stress set.
- Comprehend comparison. Amazon Comprehend Targeted Sentiment was scored on its best-performing configuration, only on English content, with identical matching rules to ours. The comparison is about what each tool can see.
- AI-assistant comparison. The per-response test used a well-crafted prompt on the same AI model Communiti uses, plus a stronger model, so the gap shown is workflow and product, not model quality. Correctly filed means the issue was found and assigned to the project's reporting topic.
- Reproducibility. Every number on this page traces to a published results file, and the full evaluation suite re-runs end to end. Ask us for the benchmark pack.
Available now in Communiti
See your own consultation analysed this way
Bring one real, de-identified feedback export to a 30-minute walkthrough and watch every issue, in every language, land on your dashboard.
Want the numbers behind this release? Explore the full published benchmark - precision, recall, difficulty tiers, and methodology.
