Support and ticket analysis
Classifying what people contact you about and how that mix is shifting, from the text rather than the category field.
AI Capabilities & Services
Sentiment Analysis
Understand customer voice across channels in Arabic and beyond.
The problem
Feedback arrives faster than anyone can read it — reviews, tickets, survey comments, social posts, call notes — so it gets sampled, and the sample gets read by whoever has time. A satisfaction score tells you that something changed without telling you what, and by the time the theme is obvious it has been running for a quarter. In Arabic-speaking markets the problem is worse, because dialect and mixed-script text defeat most off-the-shelf tooling.
Who this is for
- Customer experience and service leadsThe theme behind the score, early enough to act on
- Product and marketing teamsWhat people say unprompted, which differs from what surveys ask
- OperationsWhich branch, product or process the complaints actually concentrate on
What people use it for
Review and social monitoring
Themes and sentiment across public channels, with the spikes traced to what caused them.
Survey free-text at scale
The open comment field that everybody collects and nobody reads, turned into ranked themes.
Employee feedback
Aggregate themes with individual anonymity preserved, which is a design requirement rather than an option.
What it needs to work
- Your text sources — tickets, reviews, survey comments, call transcripts, social
- A taxonomy of what matters to you, or time from the people who know to build one
- A labelled sample — a few hundred examples read by someone who knows the domain
- Clarity on personal data: what may be processed, retained and for how long
How it works
-
Collect and normalise
Text from every channel into one place, with dialect, mixed Arabic–English and transliteration handled rather than discarded.
-
Classify to your taxonomy
Topic and sentiment against categories that mean something to your business, not generic positive/negative.
-
Aggregate into themes
Volume and direction by theme, segment, product and location — one angry review is noise, a trend is information.
-
Alert on movement
A theme rising unusually fast reaches the owning team while it is still a small problem.
-
Sample and correct
Humans review a sample continuously. Language changes, and a classifier trained once quietly stops being right.
How we deliver it
-
Source and taxonomy
Which channels, and what categories would actually change a decision. A taxonomy nobody acts on produces reports nobody uses.
-
Label and build
A labelled set built with your team, then models trained and measured per category — including the ones they get wrong.
-
Dashboard and alerting
Themes, trends and drill-down to the underlying text, because a theme nobody can read the evidence for is not actionable.
-
Review loop
A standing sample review, and retraining when accuracy on a category slips.
Where it runs
- On-premises or private cloud where customer text cannot leave
- Batch for periodic reporting, streaming where alerting matters
- Integrated into your service desk or CX platform
Security and governance
- Personal data minimised and, for employee feedback, aggregated so individuals are not identifiable
- Accuracy reported per category, because an average hides the category that fails
- A human sample review on any theme that triggers action
- Retention set by you and applied to source text as well as to results
Timeline
A first working classifier over one channel is quick once a labelled sample exists. Producing that sample is the part that needs your people, because only somebody who knows the domain can say whether a comment is a complaint about delivery or about the product. We would rather spend a week on labels than ship a classifier that is confidently wrong about your most important category.
What you receive
- Classifiers measured per category on your own labelled data
- A themes dashboard with drill-down to the source text
- Alerting on unusual movement, routed to the owning team
- The taxonomy, documented, and the labelling guide behind it
- A review and retraining procedure your team can run
Related work
Published projects where we did this.
What this does not do
Sarcasm, heavy dialect and very short text remain genuinely hard, and any supplier claiming otherwise has not tested on real Arabic social data. Accuracy varies by category and by channel, so we report it that way rather than as a single headline number. And sentiment tells you the direction, never the cause — the cause is in the text, which is why drill-down matters more than the score.
Questions we are asked
Does it handle Egyptian and Gulf dialects?
It is built to, and that is tested explicitly on your own data rather than assumed. Dialect coverage is a measurement we report per category, because a model that handles Modern Standard Arabic and fails on how people actually write is not useful.
Can it replace our survey programme?
No, and it should not. Surveys ask a defined question of a defined sample; this reads what people volunteer. They answer different questions and are most useful together.
About the figures on this page
This page describes capability and method. It does not publish accuracy figures, throughput numbers or delivery dates, because those depend on your data, your systems and your scope — and a number published here would be wrong for most readers. You get them, in writing and against your own data, at scoping.
A first call is a technical conversation, not a pitch: what you have, what you need, and whether this is the right approach at all.
Built on the Unified Intelligence Layer
Every InsAI product runs on the same four-stage backbone.
- 1
Data Integration
ERP · IoT · BIM · CRM
- 2
AI Models & Predictive Engines
Forecasting, detection, optimization
- 3
Automation & AI Agents
Acting on predictions, end to end
- 4
Real-time Dashboards & Decision Systems
From the floor to the boardroom
Sentiment Analysis
Understand customer voice across channels in Arabic and beyond.