[ AI sentiment analysis ]

Hear What Thousands of Customers Are Saying, Every Day

We build feedback analytics that read reviews, tickets, surveys and call transcripts, tag the topics behind each opinion and warn product and support teams when something starts to go wrong.

Plan Your Feedback PilotHow It Works
Reviews, tickets, surveys, calls and social
Every channel
Reviews, tickets, surveys, calls and social
Sentiment per feature, not just per message
By topic
Sentiment per feature, not just per message
Rising issues flagged as they emerge
Alerts
Rising issues flagged as they emerge
Two women with coffee cups talking in a shopping center

  • “Checkout kept failing on my phone.”

    CheckoutNegative

  • “Support sorted it in ten minutes.”

    SupportPositive

  • “Price went up without warning.”

    PricingNegative

Sentiment by topic (example)

  • DeliveryNegative
  • CheckoutNegative
  • PricingNegative
  • ReportsPositive
  • SupportPositive

Alerts go to the owning team when a topic turns negative.

[ Inputs ]

Feedback Sources

We map each source in the first weeks and only use the ones that improve the result.

  • Support tickets and chats
  • App store and product reviews
  • NPS and survey comments
  • Call and meeting transcripts
  • Social media mentions
  • Community and forum posts
Your modelSentiment Analysis
[ The problem ]

Why Most Feedback Goes Unread

Teams sample a few comments and miss the patterns hiding in the rest.

  • 01

    Too much to read

    Thousands of comments a week across many tools and languages.

  • 02

    Scores without reasons

    NPS and star ratings say how people feel, not why.

  • 03

    Issues found late

    A broken feature trends for days before anyone notices.

  • 04

    Generic tools miss context

    Off-the-shelf sentiment misreads your product terms and sarcasm.

[ How we build it ]

How We Build Your Feedback Analytics

We learn your product language first, then measure accuracy on your own feedback.

  1. 1

    Channel mapping

    We connect review sites, help desk, surveys, calls and social sources.

    Unified feedback feed

  2. 2

    Topic taxonomy

    Product areas, features and issue types are defined with your teams.

    Topic taxonomy

  3. 3

    Model build

    Models tag topic and sentiment per sentence and are tested against human labels.

    Accuracy report

  4. 4

    Dashboards and alerts

    Trends, drill-downs and alerts go live for product, support and leadership.

    Live analytics

[ Features ]

What the System Does

From raw comments to clear priorities for each team.

01 / 06

Multi-channel intake

Zendesk, Intercom, app stores, Trustpilot, surveys and call transcripts.

[ What we measure ]

What We Measure With You

Targets are agreed on a labeled sample of your feedback.

01
Tagging accuracy
Agreement with human labels on topics and sentiment
02
Coverage
Share of feedback analyzed each week
03
Time to detect
Days from first complaints to an alert
04
Issue resolution
Time from alert to fix for top issues
[ Industries ]

Where It Fits

  • 01SaaS and tech productsFeature feedback from tickets, reviews and calls.
  • 02eCommerce and retailProduct, delivery and return reasons from reviews.
  • 03HospitalityGuest reviews by property, room and service area.
  • 04Financial servicesComplaints analysis for service and compliance teams.
  • 05HealthcarePatient feedback across clinics and services.
[ Your words, your topics ]

Tuned on Your Feedback, Not Generic Reviews

We label a sample of your own comments, build the topic list with your teams and report accuracy before launch.

Book a Feedback Call
Person reading on a laptop with a cup of coffee
[ Stack ]

Sentiment Analysis Stack

Fine-tuned language models with dashboards your teams will open.

Models

  • Hugging Face Transformers
  • RoBERTa
  • OpenAI GPT
  • spaCy

Pipeline

  • Apache Kafka
  • Python
  • PostgreSQL

Search

  • Elasticsearch
  • OpenSearch

Apps

  • React dashboard
  • Looker
  • Slack
[ Cost ]

Sentiment Analysis Cost

Indicative starting prices. Model usage is billed to your account.

Get a Fixed Quote

Feedback pilot

Two sources, a taxonomy and an accuracy test

3 to 4 weeks

from$3,500

Feedback analytics

All main channels, dashboards and alerts

6 to 10 weeks

from$12,000

Voice of customer platform

Multilingual, competitor benchmarking and integrations

3 to 5 months

from$30,000

[ Client voices ]

What Customer Teams Say

Rated 4.9/5 on average across the platforms clients review us on.

  • Clutch5.0
  • GoodFirms5.0
  • Google5.0
  • Upwork4.9
I had the pleasure of working with Sajal Tech on a travel website, and it was an incredible experience from start to finish. They demonstrated professionalism, clear understanding of requirements, and excellent communication throughout.
DKDaniel KwekaCEO, Darius Digital
[ FAQ ]

Sentiment Analysis Questions

Still deciding? A 30-minute call usually answers the rest.

Ask Us Directly

  • We measure it against your own labeled feedback before launch and report accuracy per topic, then retrain as your product changes.

  • Better than generic tools, because models are tuned on your feedback and your product terms.

  • Zendesk, Intercom, Freshdesk, Salesforce, app stores, Trustpilot, Google reviews, survey tools and call platforms.

  • Yes. Most major languages are supported in one dashboard.

  • Personal details can be masked before analysis, and data stays in your cloud account.

  • A pilot on two feedback sources usually shows first insights within three to four weeks.

[ Related solutions ]

[ Contact ]

Let's build your next product together

Book a free strategy call and leave with a clear plan and estimate. No commitment.

  1. 01Pick a time that suits you
  2. 0230 minutes on scope, stack, timeline and budget
  3. 03Fixed-price proposal, NDA on request
Prefer email? contact@sajaltech.com