Solution: Feedback analysis
You have reviews and feedback you never read
Every review, survey response and support ticket is read, tagged by theme and sentiment, and summarized, so you know what customers love, what annoys them and what to fix first.
Sounds familiar?
Signs this is costing you time
Customers tell you what they think all the time: in online reviews, survey comments, support tickets, app store ratings and sales calls. Most of it is never read systematically. Someone skims a few reviews, the loudest complaint gets attention, and the quiet patterns are missed.
Reading thousands of comments by hand is not realistic, and star ratings alone hide the reasons behind them.
AI can read everything consistently, group comments into themes and track them over time, so decisions are based on what most customers say, not what one customer said loudest.
The result is a short, regular report that product, operations and marketing all read: what is getting better, what is getting worse, and what to fix next, with real customer quotes behind every point, so nothing is taken on faith.
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Reviews pile up on several platforms and are rarely read.
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Survey comments sit in exports nobody analyzes.
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Decisions follow the loudest complaint, not the most common one.
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Emerging problems are noticed late.
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Product, operations and marketing each see a different slice of feedback.
Before and after
How it works today, and how it works after
How it works today
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Feedback lives in review sites, survey tools and the help desk.
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Someone reads a sample when there is time.
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Themes are guessed rather than counted.
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Nobody tracks whether issues improve after changes.
How it works after
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Feedback from every source is collected automatically.
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Each comment is tagged with themes and sentiment.
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A dashboard shows top themes, trends and examples.
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Alerts flag sudden spikes in a complaint.
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Monthly summaries go to the teams that can act on them.
What we build
What we build
Feedback collection
Theme tagging
Sentiment scoring
Insight dashboard
Spike alerts
Written summaries
In practice
What it looks like in practice
An illustrative walk-through, not a client story.
A hotel group collects reviews from booking sites and its own post-stay survey. Management reads a few reviews each week and knows guests generally like the breakfast.
After a month of automated analysis, the dashboard shows that "check-in wait" is the fastest-growing negative theme at one property, mostly on Friday evenings, and that "room noise" appears in a steady share of reviews at another. The general manager adds staff to Friday check-in, and the next month's report shows the theme falling. Each theme links to real quotes, so the team can read what guests actually said.
Models and tools
AI models and MCP servers that usually fit
We choose models by task, data sensitivity and cost, and test them on your real examples before we commit. These are common starting points, not a fixed recipe.
How we build it
The services behind this solution
Most solutions combine two or three of our services. These are the ones this one usually needs.
AI workflows
Step-by-step automations where AI handles the reading, sorting and drafting inside a process you control.
Dashboards and reporting
Dashboards that pull numbers from the systems you already use and show what matters without a spreadsheet.
LLM integration
Add a large language model to software you already have, with the guardrails, costs and logging handled.
Industries where it fits best
Honest limits
Limits and human checks
AI tagging is consistent but not perfect. Sarcasm, mixed opinions and very short comments can be misread. We test tagging accuracy on a sample your team labels, show example quotes behind every number and let people correct tags. For decisions, trends across many comments are reliable; individual tags are not.
Reviews also contain personal information. We collect only what is needed, respect each platform's terms and keep the data in your own systems, where your team controls who can see it.
Keep exploring
Services, industries and case studies
Related services
View all related services- AI workflows Step-by-step automations where AI handles the reading, sorting and drafting inside a process you control.
- Dashboards and reporting Dashboards that pull numbers from the systems you already use and show what matters without a spreadsheet.
- LLM integration Add a large language model to software you already have, with the guardrails, costs and logging handled.
- Product scaling Keep a growing product fast and stable as users, data and features increase.
- AI Product Development AI agents, knowledge assistants, copilots and document automation built into the way your team already works.
- AI chatbots Website and messaging chatbots that answer common questions well and hand everything else to a person.
Solutions
View all solutions- Price monitoring Collect competitor prices automatically, match them to your products and alert you when something changes.
- Scheduling and drafting automation Turn one piece of content into posts for each channel, schedule them and track results, with approval before anything goes out.
- AI automation and integrations Connect the tools you already use so data moves once, correctly, and AI reads the parts that arrive as text or documents.
- Automated reporting Reports that build themselves from your systems on schedule, with a plain-language summary of what changed.
- AI translation workflows Translate websites, products and support content quickly with AI, a shared glossary and native-speaker review where it matters.
- AI lead qualification Score, research and route every new inquiry so your team talks to the best leads first.
Industries
View all industries- Hospitality and travel Booking sites, guest messaging, review analysis and multilingual content for hotels, tour operators and travel businesses.
- Retail Stock forecasting, sales dashboards, feedback analysis and store tools for retailers with shops and online sales.
- SaaS and startups MVPs, subscription billing, AI features, multi-tenant platforms and scaling support for founders and product teams.
- Healthcare Patient booking, intake forms, internal knowledge assistants and admin automation for clinics and care providers.
- Home services Booking, dispatch, quotes, invoicing and reviews for plumbers, electricians, cleaners and other home service businesses.
- Construction and facilities Job tracking, maintenance requests, inspections, quotes and proof-of-work photos for builders and facility teams.
Case studies
View all case studies- Goal and effort tracking app A product design built around handwritten daily proof, locked plans and honest self-scoring. The build is in progress.
- Fashion deal aggregator Sale items from more than thirty brands are collected every night and classified consistently by a three-stage pipeline.
- Vacation rental operations system One calendar per property, cleaning tasks created from bookings, and owner statements built from the same records.
Guides and articles
View all guides and articles- How to plan a SaaS MVP Decide what goes into the first version of a SaaS product, what waits, and which foundations to build properly.
- How to validate a product idea before building Cheap, fast ways to test whether customers want your product before you spend on development.
- MVP vs full product Whether to launch small and learn, or build the complete product first.
- How to automate reporting with AI and MCP Build reports that assemble themselves from your systems through MCP, with a written summary people can trust.
AI models
View all ai models- General purpose All-round language models for writing, summarizing, support, extraction and most everyday business tasks.
- Claude Haiku 4.5 Anthropic's fastest and lowest-cost Claude model, with near-frontier intelligence for high-volume and real-time work.
- Gemini 3.8 Flash Google's most capable Flash model, stable since September 2026, for agents, software engineering and enterprise workflows with full multimodal input.
- GPT-6 Luna OpenAI's most efficient model for focused, high-volume tasks, with vision, tool calling and structured outputs at the lowest price level in the family.
- GPT-4o mini A compact, low-cost older OpenAI model for focused tasks, with vision, tool calling and structured outputs and a 128K token window.
- text-embedding-3-small OpenAI's efficient, lowest-cost embedding model, with 1,536-dimension vectors for search, retrieval and similarity at scale.
MCP servers
View all mcp servers- CRM and sales Servers that connect AI to CRM and support platforms, to look up customers, update records and prepare follow-ups.
- Google Sheets Google's own Sheets MCP server lets AI read and update cell values and formulas, change spreadsheet structure and insert rows or columns.
- Zendesk A community MCP server that lets AI read Zendesk tickets, comments and attachments, create tickets and add comments.
- PostgreSQL Lets AI assistants explore a PostgreSQL database, run SQL in a restricted read-only mode and check query performance and database health.
- Google Analytics Google Analytics' official MCP server lets AI run GA4 reports, funnels and realtime reports and read property settings. Read-only.
- Airtable Airtable's official hosted MCP server lets AI list workspaces, create bases and read records, with wider read and write access to tables and automations.
Glossary terms
View all glossary terms- Zero-shot classification Zero-shot classification is sorting text or images into categories an AI model was not specifically trained on, using only the category names or descriptions.
- Natural language processing Natural language processing is the field of AI that deals with understanding and generating human language, in text or speech.
- Structured output Structured output is when an AI model returns its answer in a fixed format, such as JSON matching a schema, so software can use it reliably.
- Machine learning Machine learning is a way of building software that learns patterns from data to make predictions or decisions, instead of following only hand-written rules.
- MVP An MVP, or minimum viable product, is the smallest version of a product that real users can use and pay for, built to learn quickly what works.
- SaaS SaaS, or software as a service, is software delivered over the internet on a subscription, rather than installed and owned by each customer.
FAQ
Questions about feedback analysis
Have a question that is not here? Ask us directly.
It reads each comment, assigns themes from a list you define, scores sentiment per theme and summarizes the results. People review samples to check accuracy. See AI workflows.
Review platforms with APIs or exports, survey tools, help desk tickets, app store reviews and call notes. We confirm access for each source.
No. Even a few hundred comments benefit from consistent tagging. Trends become more reliable as volume grows.
Yes. Results can feed your BI tool or a custom dashboard. See dashboards and reporting.